case study of any contemporary dbms

Modern Database Management Explained

How You Can Transform Raw Data in Actionable Insights With Modern Database Management

Databases play a pivotal role for businesses, serving as the backbone of countless applications, websites, and internal processes. As the world continues to generate vast amounts of data, the efficient organization, storage, and retrieval of this data becomes crucial. This is where modern database management comes into play, offering a structured way to store, manage, and access vast reservoirs of information.

Table of Contents

The evolution of database management systems (dbms).

Database Management Systems (DBMS) have undergone significant transformations in recent times. What began as rudimentary systems to catalog and retrieve data have now evolved into sophisticated platforms that underpin a vast array of business operations.

In the early days, data was often stored in flat files or hierarchical databases, which had a set structure and required significant manual effort for data retrieval. But as organizations grew and data volumes surged, there emerged a need for more efficient ways to store and access data.

This led to the development of the relational model in the 1970s, a watershed moment in the history of databases. Systems based on this model, known as Relational Database Management Systems (RDBMS), revolutionized data storage and retrieval by representing data in tables, allowing for more versatile and efficient querying.

The turn of the century brought with it the challenges of handling the Internet’s explosion and the vast amounts of unstructured data it generated. Modern database systems began to diversify, incorporating NoSQL databases to cater to needs that traditional RDBMS couldn’t address. Today, we have a plethora of database systems, each tailored for specific use cases – from object-oriented databases, graph databases, to distributed systems, and more.

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Modern Database Management Simplified

Modern dbms features.

Think of these databases as toolkits. They’ve got a bunch of features built to handle today’s data challenges, making sure everything runs smoothly, safely, and is user-friendly.

Data Operations: DBMS tools let you play with data. You can create, read, update, or delete data i.e. perform CRUD operations . They provide enhanced querying capabilities, meaning you can dig deep and mold your data just the way you want it. Five elevates this functionality by offering a user-friendly visual MySQL database builder. It supports complex SQL operations and integrates various data sources, thus simplifying data operations even for those with limited coding experience.

Integrity: It’s like a promise that your data will stay correct and trustworthy. Modern systems have rules in place (like making sure no two items have the same ID) to stop mistakes or mix-ups.

Concurrency: Imagine many people trying to edit a document at once. Without control, it’s chaos. Modern DBMS ensures everyone can work at the same time, without stepping on each other’s toes.

Backups: With more data, there’s more to lose. Modern systems have your back, making sure there are copies of your data. If something goes wrong, you can bring back your lost data. Plus, a lot of this can be set up to run on its own.

Smart Organization (Normalization): Instead of lumping all data together, modern systems sort data into tables, reducing repeated info. It’s like having a well-organized office space – everything has its place, making it efficient and tidy.

In short, modern DBMS tools, are designed to make handling large amounts of data a breeze, ensuring everything is organized, safe, and easily accessible.

Different Kinds of Database Systems

Different applications have unique needs. That’s why there’s a variety of Database Management Systems (DBMS) to choose from. Here’s a quick look at some of the main types:

1. Distributed DBMS:

• What it is: Manages databases across multiple locations. All these databases work together, appearing as one to the user.
• Why it’s great: It’s scalable. As companies grow, they can add more databases easily.

2. Hierarchical & Network DBMS:

• Hierarchical: Think of it like a company org chart, with a tree structure. Each data point has one “parent” but can have many “children.”
• Network: It’s a bit more complex. Each child data point can have several parents. It’s flexible and mirrors real-life data relationships.

3. Relational DBMS (RDBMS):

• What it is: This popular system organizes data into tables. Each table has rows (records) and columns (attributes).
• Why it’s great: It’s user-friendly, versatile, and scales well. It’s the go-to for many projects, big or small. This is what our low-code IDE Five uses.

4. Object-oriented DBMS:

• What it is: Designed for object-oriented programming, it keeps data as “objects,” combining data and related methods.
• Why it’s great: Represents real-world entities efficiently. Great for modern, dynamic apps.

To wrap up this section, the variety of DBMS types shows how data management keeps evolving to meet our needs. Whatever your project demands, there’s a DBMS out there for you.

Distributed DBMS	Manages databases across multiple locations, appearing unified to users.	Highly scalable; can easily expand as companies grow.	E-commerce platforms operating in multiple countries.
Hierarchical DBMS	Data structured like an org chart; tree structure with parent-child relationships.	Organized; allows for clear hierarchical data representation.	Organizational directories; file management systems.
Network DBMS	Each child data point can have multiple parents; mirrors complex data relationships.	Highly flexible; represents multifaceted data relationships.	Telecommunications networks; airline reservation systems.
Relational DBMS (RDBMS)	Organizes data into tables with rows and columns. Used by “Five”.	User-friendly, versatile, and scales well.	Customer relationship management (CRM) systems; inventory databases.
Object-oriented DBMS	Combines data and related methods as “objects”.	Efficient representation of real-world entities; good for dynamic apps.	Graphic design software; video game settings and characters.

Modern Database Management with Five Pt.1

1. rapid development and deployment.

In modern database management, speed and flexibility are vital. Five empowers developers to rapidly build custom business applications with the speed of low-code and the depth of full code. This means you can prototype, test, and deploy quicker than ever.

2. Device-Independent Database Applications

The future is mobile. Applications created with Five are web-based and responsive, ensuring accessibility across all devices. No need for multiple codebases; one application fits all.

3. Transform Legacy Systems

Modernizing outdated applications is a significant aspect of contemporary database management. With Five, developers can transition from older systems like Microsoft Access or Excel-based processes into robust, scalable cloud-native web applications.

4. Seamless Integration of Business Logic

Businesses have intricate processes. While many platforms offer out-of-the-box solutions, Five understands that customization is key. With event-driven programming, SQL, and JavaScript, developers can readily tailor solutions to fit specific business logic.

5. Advanced Database Features

As part of modern database management, Five offers features like a GUI for MySQL, visual query building, and easy data importing from CSVs. This ensures developers can manage data efficiently and effectively, all within a unified environment.

IDEs like Five are setting the benchmark by offering a suite of tools that cater to both the developers’ needs and the end-users’ expectations, all while prioritizing speed, flexibility, security, and user experience. As businesses continue to grow and adapt, the value of such tools in modern database management cannot be understated.

“Five lets you store your database plus gives you the tools to understand it better.”

Modern Database Management with Five Pt.2

Data is often touted as the new oil – a vast reservoir of potential waiting to be tapped. Yet, unlike crude oil, raw data isn’t inherently valuable. Its true value emerges when it’s refined, analyzed, and translated into actionable insights.

Modern Database Management is no longer just about storing and retrieving data but also about making sense of it. To this end, most modern DBMS are equipped with robust tools or integrations tailored for data analytics and reporting.

Rapidly Develop Your Data Analysis Tool with Five

Empower Decision-Making With Five: At the core of Five is the understanding that businesses need more than just data – they need insights. Five allows you to build a custom-built Data Analysis Tool that simplifies the management of your data.

• Intuitive Data Visualization Dashboards: Dive deep into your data with interactive dashboards that are as informative as they are visually compelling. Whether you’re looking to track sales metrics, monitor user engagement, or identify emerging market trends, Five has got you covered. These dashboards are not just static displays but dynamic tools that allow for real-time data interaction.
• Customizable Reporting: Every business is unique, and so are its data needs. Five offers customizable reporting tools, enabling businesses to focus on metrics that matter most to them. From pie charts to bar graphs, choose the best way to represent and interpret your data.
• Seamless Integration: Data often resides in different places. Whether it’s sales data from an eCommerce platform or user metrics from a mobile app, Five ensures that all your data sources speak the same language. This seamless integration ensures that your analytics and reports are comprehensive.

The digital revolution has ushered in a new age for database management. As we trace back the lineage of Database Management Systems (DBMS) , we see a transition from simplistic systems to today’s sophisticated platforms. Early methods of storing data, although foundational, were limited in scope. The advent of the Relational Database Management Systems (RDBMS) in the 1970s was a pivotal moment, introducing a structured approach using tables.

Today’s landscape showcases a rich variety of DBMS tailored for distinct needs, whether it’s the globally-connected Distributed DBMS, the structured Relational DBMS, or the modern Object-oriented DBMS. Among these modern solutions, Five stands out with a suite of advanced features for detailed data management.

In summary, the importance of efficient and effective database management platforms cannot be understated. Tools like “Five” are at the forefront, ensuring that businesses are equipped to handle, interpret, and leverage their data in the most optimal way.

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Case Studies Examples Scenarios Database System DBMS

Most of the time you see the case studies and scenario-based questions in the Database System (DBMS) paper. Keeping in view, I am sharing with you some of the case study base questions of the database course.

Examples of Case Studies and Scenarios questions from DBMS

• Examples of Case Studies and scenarios from the Database System.
• How you can make a database from the scenario mentioned below.
• How you can normalize the database tables from the case studies mentioned below.
• How to draw the Entity-relationship diagram from the given case study.
• How to draw the Data flow diagram from the case studies mentioned below.
• What database model is suitable for the case studies mentioned below.
• What kind of database users are suitable for the given case study.
• What kind of database redundancies and inconsistencies are possible in the given scenario.
• How You can write SQL Queries on the tables of the mentioned case study.
• Find the possible database keys from the tables of these case studies.
• Suggest the relationships among the tables of the given scenarios.

Vehicle information dissemination system for Cloud  Android Project for BCS BSIT MCS BSSE

Gym and Fitness Management System Project IN C# for BCS BSIT MCS BSSE

HR Management System Project in C# and VB.NET for BCS BSIT MCS BSSE

Employees Attendance System via Fingerprint  in C# and VB.NET for BCS BSIT MCS BSSE

Pharmacy Record Management System Project in PHP, ASP or C#.NET

Car information System using Android and Arduino final year Project for BSCS BSIT MCS BSSE

Agile File Master App final year project for BSCS BSIT MCS BSSE

Android Messenger App final year project for BSCS BSIT MCS BSSE

Android Call Recorder App final year project for BSCS BSIT MCS BSSE

Music Listening App final year project for BSCS BSIT MCS BSSE

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Financial Helper Using QR/Barcode Scanner Android Final year project for MCS BSCS BSSE

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If you are still in reading the more case studies, then you can read 100+ case studies .

Related Posts:

• Case Studies Examples Scenarios OOP
• History of Database System (DBMS)
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• 3 Tier Database Architecture in DBMS

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A comprehensive collection of SQL case studies, queries, and solutions for real-world scenarios. This repository provides a hands-on approach to mastering SQL skills through a series of case studies, including table structures, sample data, and SQL queries.

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by Susan Berry | January 29, 2024

Database Management Systems (DBMS): Definition, Uses, and Examples

Efficient data management is critical in today's business landscape because of the impact that data has on informed decision making. But managing and organizing data can pose significant challenges with storing large amounts of data, ensuring data quality, extracting meaningful insights from complex data, and more. Database management systems (DBMS), which are designed to create and oversee databases, offer a foundation for addressing these challenges, enabling organizations to manage, organize, and use their data seamlessly.

A DBMS enables you to perform tasks such as creating, securing, retrieving, updating, and deleting data within a database. The system acts as an intermediary by connecting databases with users or with application programs, and it guarantees consistent organization, accessibility, and usability of your data. A DBMS also oversees the control of data, the database engine, and the database schema to ensure data security, integrity, concurrency, and consistent data-administration procedures.

Database management systems: Use cases and examples

The choice of a specific DBMS depends on the requirements of the application and the nature of the data that needs to be managed. These systems are quite versatile in that they can be used across a wide range of industries, including those listed here:

• Economics and finance: Economic and financial firms store and manage information about customer transactions, stock market trades, bonds, mortgages, and other financial products.
• Healthcare: Healthcare organizations (hospitals, physician practices, etc.) store patient records and medical history that are used for health-data management.
• Government: Government agencies store and manage public records, regulatory data, and administrative information.
• Manufacturing: Manufacturing companies manage production schedules, inventory, and quality control data.
• Research and academia: Groups such as universities and research institutions store and manage student information, research data, publications, and more.
• Retail: Retail businesses manage inventory, customer transactions, and supply-chain data.
• Software: Software companies store, manage, and migrate large volumes of data that is generated by the applications that they develop.

This list illustrates just a portion of the many industries where a DBMS can be used for data management, retrieval, and analysis that is tailored to an organization's specific needs and requirements.

Six benefits of database management systems

Using a DBMS for data storage and management offers several benefits:   

• Data consistency: Data-consistency rules in the DBMS ensure accuracy and uniformity of your data across applications.
• Data availability: Your data is available continuously, and authorized users can access it even during system failures or disruptions.
• Data-process automation: A DBMS enables you to automate various processes, including query execution, transaction management, task scheduling, data archiving, and data backup and recovery.
• Data security: Data integrity and security are ensured by using constraints on values and by providing access controls (user permissions and access levels) to regulate data access.
• Data sharing: Multiple users and applications can use the same data simultaneously.
• Data organization and management: DBMS tools for organization, indexing, and management enhance overall system performance and reduce storage costs.

As you can see, implementation of a DBMS can increase your data quality, automate processes, cut costs, and help you to make better business decisions by providing a secure, dependable, and efficient platform for storing and managing your data.

Components of database management systems

Database management systems contain various integrated components that store, manage, and facilitate access to data.

Here are some of the common core components that you will find in DBMSs:

• Backup and recovery manager: This component creates backup copies of the database and facilitates recovery in the event of data loss or system failures.
• Data Definition Language (DDL) compiler: The compiler manages the definition of the database structure, including creating, altering, and deleting tables and other schema objects.
• Database utilities: Various utilities perform maintenance tasks (for example, data loading, data extraction , and data transformation ).
• DBMS Engine: The DBMS engine is the core component that manages and controls the interaction between the database and the users or application programs.
• Query languages (for example, SQL): A DBMS provides a standardized language for users and applications to interact with the database. Structured Query Language (SQL) is the most common.
• Query processor: The query processor translates SQL queries into a series of operations that the DBMS engine can execute.
• Metadata catalog: A metadata catalog is a central repository for information about the structure, organization, and characteristics of the data managed by the DBMS.
• Storage manager: This component manages how data is stored on physical storage media (for example, on hard drives).
• Transaction manager: A transaction manager ensures the reliability and consistency of transactions, and it manages the execution and recovery of transactions.
• Security and authorization module: This module enforces access controls and authentication mechanisms to ensure that authorized users only can access and modify specific data in your database.

These components are just some of the many that work together in a DBMS to ensure the effective and secure management of data within a database system. The specific features and functionalities can vary depending on the type of DBMS and the vendor's implementation.

Types of Database Management Systems

Database management systems come in many forms that cater to specific use cases and data models. So, the choice of a particular type depends on many factors—for example, the nature of your data, scalability requirements, and the specific needs of your application. However, four common types are relational (RDBMS), NoSQL, object-oriented, and hierarchical systems.

Relational database management system

Relational database management systems (RDBMS) are one of the most popular DBMS types. The popularity of the RDBMS type results from user-friendly interfaces and great flexibility. This type of system stores data in interconnected tables, and it relies on SQL for data manipulation and access.

Many organizations use an RDBMS to store large amounts of structured data, including vital information like customer details and product inventory. Keys are used to manage the relationships between the tables.

Examples of common RDBMSs include MySQL, Microsoft SQL Server, and Oracle.

• Advantages RDBMSs are favored for their simplicity, as they allow for task management through simple SQL queries. They ensure precision and data integrity by employing constraints and normalization techniques to eliminate redundancy and inconsistencies, and support secure collaboration through user permissioning.
• Limitations The high costs associated with RDBMS setup, development, and maintenance make them an expensive choice. They also require substantial physical storage due to their row and column structure, with storage needs escalating as data grows. And scaling an RDBMS to handle large data volumes or high transaction loads often presents challenges, such as hardware constraints and the necessity for complex, costly solutions. Furthermore, performance can degrade with increasing data volumes, especially in the case of complex queries and joins, which can be resource-intensive and impact response times.

NoSQL database management system

NoSQL is a type of DBMS made specifically to manage expansive amounts of unstructured and semi-structured data. This approach allows for a flexible schema and support for diverse data models, and it enables you to manage large-scale, high-performance scenarios.

Examples of common NoSQL DBMSs include Apache Cassandra, Couchbase, DynamoDB, and MongoDB.

• Advantages NoSQL databases offer several advantages, particularly in managing unstructured and semi-structured data. They are cost-effective, as they can run on commodity hardware and are often open source, reducing licensing costs. These systems are known for their flexibility with schemas, supporting dynamic and schema-less data models, allowing developers to easily adapt to changing data requirements. NoSQL databases are designed for horizontal scalability to handle large data volumes and high traffic by distributing data across multiple nodes. They also prioritize performance, especially for intensive reading and writing tasks, and are optimized for specific workloads like real-time analytics and high-speed content processing.
• Limitations NoSQL databases lack a standardized query language, unlike SQL, making it difficult to query and manage data uniformly across different NoSQL systems. Many NoSQL DBMSs prioritize scalability at the expense of transactional guarantees. While some offer atomicity, consistency, isolation, and durability (ACID) properties, others might only provide eventual consistency, which might not be adequate for all applications. Data integrity and consistency pose additional challenges in NoSQL DBMSs, especially where data is distributed across multiple nodes. Although NoSQL databases are built for horizontal scalability, achieving this often requires compromises in consistency and increases in complexity.

Object-oriented database management system

An object-oriented DBMS (OODBMS) organizes data in objects. They combine the principles of object-oriented methodologies with database capabilities. These databases can store intricate data structures, which enables developers to focus on objects rather than the intricacies of the database structure.

Note: Some OODBMSs might now be classified as NoSQL databases, especially those that support document-oriented or graph database models.

Examples of common OODBMSs include GemStone/S, Verdant, and Objectivity/DB.

• Advantages OODBMSs are particularly advantageous in scenarios requiring an object-oriented approach to data management. They excel at handling complex data structures, such as nested objects and relationships. This feature is especially useful for applications with intricate, interconnected data. OODBMSs also enable the native representation of objects, allowing for direct storage and retrieval without the need to map objects to relational tables, which leads to a more natural interaction with object-oriented data models. Developers familiar with object-oriented programming find it easier and quicker to work with OODBMSs because the mapping between application objects and database objects is more straightforward. Such mapping results in faster development cycles. In addition, OODBMSs are adept at supporting complex relationships between objects, maintaining these relationships more naturally and without extensive normalization.
• Limitations OODBMSs lack a standardized query language like SQL, which can lead to challenges in interoperability and portability among different OODBMSs. The learning curve can be steep for developers who are unfamiliar with object-oriented concepts such as encapsulation, inheritance, and polymorphism. Integrating an OODBMS with existing systems can present data migration and compatibility challenges, particularly if you are transitioning from a traditional RDBMS. In addition, performance can be a concern in OODBMSs, depending on their implementation and the specific use case. Optimizing performance in these systems might require careful consideration of the data model and indexing strategies.

Hierarchical database management system

A hierarchical database management system (HDBMS) organizes data in a hierarchical, tree-like structure. In this model, data is represented as a collection of records, where each record contains fields or attributes. The records are organized in a hierarchy, with parent-child relationships that define the structure.

Examples of some hierarchical databases include IBM's Information Management System (IMS), Microsoft Windows Registry, and the Lightweight Directory Access Protocol (LDAP).

• Advantages HDBMSs are particularly effective in scenarios where inherent hierarchical relationships exist. They provide a natural representation of data structures, meaning that the organization of data in the database mirrors its conceptual structure or real-world relationships. Using indexing tools, HDBMSs facilitate quicker access to records, and retrieving data is straightforward with relatively simple queries along predefined paths in the hierarchy. HDBMSs also offer granular security controls, managing access permissions at different levels of the hierarchy for fine-grained control over data access and modification.
• Limitations The rigid structure of HDBMSs poses challenges in adapting to changes in data requirements. Alterations like adding new elements or modifying existing structures can be effort-intensive and potentially disrupt existing applications. The system offers limited query flexibility for non-standard paths or relationships, and the hierarchical model can lead to data redundancy, particularly in cases of multiple parent-child relationships. Such redundancy can result in increased storage needs and potential data inconsistencies. In addition, the hierarchical model might not align well with modern application development, which often requires more dynamic and diverse data structures.

Four most popular database management systems

As mentioned previously, the choice of a DBMS depends on many factors, including the nature of your data, scalability requirements, performance needs, and so on. Given that, there are many popular DBMSs, but the following list presents some of the most well-known and popular ones in use across industries today:

• Microsoft SQL Server (RDBMS): SQL Server 's widespread adoption can be attributed to a robust set of features. as well as its integration with other Microsoft products. However, SQL Server can be expensive and resource intensive, and it is platform dependent (Microsoft Windows) for the most part. (There is a version for Linux operating environments, but the features and tools are not as mature as those in Windows environments.)
• Oracle MySQL (RDBMS) : MySQL is an open-source RDBMS that is known for its performance, reliability, ease of use, and strong community support. MySQL is used in various applications and industries, especially in web applications.
• Oracle (RDBMS): Oracle Database (commonly referred to as Oracle DBMS) is a comprehensive and widely used RDBMS developed by Oracle Corporation. Known for its robustness, scalability, and extensive feature set, Oracle DBMS is widely used in enterprise environments, ranging from small businesses to large corporations. Its feature-rich nature makes it suitable for mission-critical applications, data warehousing, and complex business processes. While Oracle DBMS is robust and feature rich, it does have certain limitations that users should be aware of, including high licensing costs, complex setup and management, and compatibility with some third-party applications and platforms.
• PostgreSQL (RDBMS): PostgreSQL is an open-source DBMS that stands out for several reasons. Its open-source nature enables a highly collaborative and supportive community. PostgreSQL also offers a robust feature set, supporting complex queries, transactions, and indexing mechanisms. In addition, PostgreSQL prioritizes data integrity and reliability, implementing ACID compliance to ensure that transactions are processed reliably. The system's scalability is also noteworthy, making it suitable for a broad range of applications, from small projects to large enterprise solutions.

The CData difference

Once you choose a DBMS to manage your enterprise data, you can use CData Sync to build automated pipelines that replicate your data to that system. Sync's compatibility with a wide range of DBMSs ensures seamless integration and adaptability to different data environments.

Comparing Database Management Systems: MySQL, PostgreSQL, MSSQL Server, MongoDB, Elasticsearch, and others

• 25 min read
• Business ,   Engineering
• Last updated: 16 May, 2023
• 3 Comments Share

In the world of software development, choosing the right database is a crucial decision that can significantly impact your application's performance, scalability, and ease of use. With many options available, it can be challenging to determine the best database management system (DBMS) that will perfectly suit your needs.

In this article, we’ll compare the 12 most commonly used DBMSs: MySQL, MariaDB, Oracle, PostgreSQL, MSSQL, SQLite, MongoDB, Redis, Cassandra, Elasticsearch, Firebase, and DynamoDB. We will focus on their business-related benefits and challenges while highlighting the ideal use cases for each. With this database comparison at hand, you will be able to make an informed decision for your project.

What is a database management system?

A Database Management System (DBMS) is a specialized software designed to store, retrieve, and manipulate data. It acts as a mediator between the database, applications, and user interfaces to manage and organize data effectively. The system provides a comprehensive suite of tools to govern databases, ensuring data security, consistency, and integrity.

A DBMS supports various applications, from simple storage and retrieval tasks to complex data-driven systems, by implementing efficient data access and management practices. Additionally, the system can handle concurrent users, maintain transactional consistency, and provide robust backup and recovery options, making it an essential component in any data-centric environment.

Since databases are just a part of the whole data management strategy , learn about this comprehensive approach in our dedicated article.

Types of databases: Relational vs non-relational

There are two types of DBMSs: relational and non-relational, also referred to as SQL and NoSQL respectively. Before discussing the most popular database options, let’s take a closer look at how relational and non-relational database systems differ, considering commonly used data structures, performance, scalability, and security.

Relational vs non-relational databases in a nutshell.

Relational or SQL databases

A relational database management system (RDBMS) is an information repository that organizes data into tables consisting of rows (records) and columns (attributes that contain the properties of these records). Each table represents a relation, and the rows (also called tuples ) hold individual records within that relation. RDBMSs have a predefined schema with a strict structure and clear dependencies between different data points.

So tables in relational databases are connected to other tables through primary key or foreign key relationships . A primary key is a unique identifier for each record in a table, ensuring that no two records have the same value for that specific column or set of columns. On the other hand, a foreign key is a column or a set of columns in one table that refers to the primary key in another table, establishing a link between them.

Despite these connections between tables, the term relational in relational database systems comes from the mathematical concept of relations. Dr. Edgar F. Codd proposed this idea as a new way to organize and manage data using principles from mathematics in his seminal paper "A Relational Model of Data for Large Shared Data Banks," published in 1970.

The second name of such systems is SQL databases . This is because Structured Query Language (SQL) is used to communicate with and manage these databases.

Scalability. Relational databases usually scale vertically, meaning data lives on a single server, and scaling is done by adding more computer (CPU, GPU, and RAM) power to that one server. However, switching from smaller to bigger machines often involves downtime. Scaling an SQL database between multiple servers (horizontal scaling) can be challenging as it requires data structure changes and additional engineering efforts.

Performance. Relational databases perform well with intensive read/write operations on small to medium datasets. They also offer improved speed of data retrieval by adding indexes to data fields to query and join tables. However, the performance may suffer when the amount of data and user requests grows.

Security. Due to the integrated structure and data storage system, SQL databases don’t require much engineering effort to render them well-protected. They are a good choice for building and supporting complex software solutions, where any interaction has a range of consequences. One of the SQL fundamentals is ACID compliance (Atomicity, Consistency, Isolation, Durability). ACID-compliance is a preferred option if you build, for instance, eCommerce or financial applications, where database integrity is critical.

Non-relational or NoSQL databases

A non-relational or non-tabular database uses different data models for storing, managing, and accessing data. The most common data models are

• document-oriented — to store, retrieve, and manage data such as JSON documents;
• key-value — to represent data as a collection of key-value pairs, where keys are unique strings having corresponding data values;
• graph — to store data in the node-edge-node structure where nodes are data points and edges are their relationships; and
• wide-column — to store data in the tabular format with flexible columns, meaning they can vary from row to row in the same table.

As these databases aren’t limited to a table structure, they are called NoSQL . They allow for storing unstructured data such as texts, photos, videos, PDF files, and a bunch of other formats. Data is simple to query but isn’t always classified into rows and columns as in a relational database.

Scalability. When the number of data and requests increases, non-relational or NoSQL databases are usually scaled horizontally by adding more servers to the pool. They share data between various servers where each contains only a part of the data, decreasing the request-per-second rate in each server.

Performance. Non-relational databases are known for their high performance: They have a distributed design, which lowers the performance load on the system and provides a large number of users with simultaneous access. Such databases can store unlimited data sets that come in all types and shapes. They are also quite flexible when it comes to changing data types.

Security. Unlike relational systems, NoSQL databases have weak security, making them a major concern for many infrastructures. While they may provide ACID guarantees, they are typically available within the scope of one database partition. However, some DBMSs offer advanced security features that meet strict security and compliance standards.

Since NoSQL databases allow for reserving various data types together and scaling across multiple servers, their never-decreasing popularity is understandable Also, NoSQL databases can be highly advantageous when it comes to building an MVP . They don’t require pre-deployment preparations, making quick, time-lag-free updates to the data structure easier.

So what are the most commonly used database systems in SQL and NoSQL? What are their main advantages and disadvantages, and how should businesses use them? Let’s take a deeper look.

Below, we’ll discuss the following list of SQL databases:

and will complement it with such NoSQL databases as:

Elasticsearch

Amazon dynamodb.

The screenshot below reflects the popularity of these and a few other databases.

Most popular database systems. Source: 2022 Developer Survey by StackOverflow

While more detailed descriptions of the aforementioned databases await you further in the post, the table here provides a quick-look comparison against key criteria.

Database management systems comparison.

Now that you have a general understanding of the differences between relational and non-relational databases, we’re moving on to describe the main modern database management systems along with the pros, cons, and use cases of each.

MySQL is one of the most popular relational database systems. Originally an open-source solution, MySQL is now owned by Oracle Corporation. Today, MySQL is a pillar of LAMP application software. That means it’s a part of Linux, Apache, MySQL, and Perl/PHP/ Python stack. Having C and C++ under the hood, MySQL works well with such system platforms as Windows, Linux, MacOS, IRIX, and others.

Pros of MySQL

Free installation. The community edition of MySQL is free to download. With a basic set of tools for individual use, MySQL community edition is a good option, to begin with. Of course, there are other, prepaid versions for Enterprise or Cluster purposes with richer functionality. Nevertheless, if your company is too small to pay for one of them, the free-to-download model is the most suitable for a fresh start.

Simple syntax and mild complexity. MySQL’s structure and style are very plain. Developers even consider MySQL a database with a human-like language. MySQL is often used in tandem with the PHP programming language. Because they share a gentle learning curve, it’s much easier to form a team to manage your database. Also, MySQL is easy to use. For instance, most of the tasks can be executed right in the command line, reducing development steps.

Cloud compatibility. Business-oriented by nature and originally developed for the web, MySQL is supported by the most popular cloud providers. It’s available on leading platforms like Amazon, Microsoft, and others. This makes MySQL even more attractive and gives businesses room for growth.

Cons of MySQL

Scalability challenges. MySQL was not built with scalability in mind, which is inherent in its code. Theoretically, you can scale MySQL, but it will need more engineering effort than any of the NoSQL databases. So, if you expect one day your database will increase substantially, keep this limitation in mind or choose another DBMS option.

Partial open-source support. Although MySQL has an open-source part, it’s mostly under Oracle’s license. This limits the MySQL community in terms of improving the DBMS. Why do you care? Because when you have completely open-source support, you expect many problem-specific implementations and community assistance. This is not the case when the software belongs to corporate owners, and you have to pay for support.

Limited compliance with SQL standards. Structured Query Language has specific standards. MySQL doesn’t completely follow them, i.e., MySQL provides no support for some standard SQL features. On the other hand, MySQL has some extensions and distinct features that don’t match the Structured Query Language standards. It’s not a big deal for small web applications. The issues may appear when you have to shift to other databases, which will likely happen when your business starts growing.

Small web-based solutions. MySQL database system is the best option when you’re designing a small, web-based solution with a small volume of data. For example, when building a local eCommerce store, MySQL may come in handy.

OLAP/OLTP systems. This is one of the best use cases for a MySQL database, as OLAP/OLTP don’t require complex queries and large volumes of data. Also, consider applying MySQL for the same reason if you’re building a business intelligence tool .

IoT applications. MySQL can be used for small to medium-sized Internet of Things (IoT) applications to store and manage sensor data, device information, and user interactions.

Keep in mind that while MySQL supports these use cases, its performance and suitability might vary depending on the specific requirements and size of the project.

MariaDB , an open-source fork from MySQL, is a great SQL database example with commercial support. It works under a GNU General Public License and has similar commands, APIs , and libraries to MySQL.

Pros of MariaDB

Encryption. For MariaDB, open source doesn’t mean insecure. In addition to internal security and password check, MariaDB provides such features as PAM and LDAP authentication, Kerberos, and user roles. Combined with encrypted tablespaces, tables, and logs, it creates a robust protective layer for data. Beyond that, MariaDB publishes related releases on each security update, keeping the security patches totally transparent.

Broad functionality. MariaDB has introduced a lot of new features in the last few years. For instance, GIS support suggests smooth coordinate storage and location data queries. Dynamic columns allow a single DBMS to provide both SQL and NoSQL data handling for different needs. You can also extend its functionality with plugins that are available at MySQL via 3rd parties only. MariaDB is shipped with storage engines for NoSQL backend, legacy database migration tools, sharding options, and much more.

High performance. Although MariaDB originates from the MySQL engine, it's gotten very far in terms of performance. Extensive optimization features improve thread pool management and data processing. Thus, when rows from the table are deleted, the operating system immediately accesses the free space, eliminating gaps in the tablespace. On top of that, the database management system suggests engine-independent table statistics. This feature enhances the optimizer’s performance, accelerates query processing, and helps customize data analysis.

Cons of MariaDB

Still a growing community. Although MariaDB has substantial open-source contribution, its community has yet to grow much. Since this database management system was established not so long ago, the number of professionals involved is relatively small.

Gaps between MySQL and MariaDB update versions. Though the MariaDB team is constantly merging its code with MySQL's, it’s already not that simple to keep them in line. Given the currently existing differences between MariaDB 10.6 and MySQL 8.0.32, further deviations are yet to come. Additionally, MySQL engineers introduced some native features to the code that are only available to commercial MySQL users. This can create compatibility issues or data migration problems from MariaDB back to MySQL.

Since MariaDB is close to MySQL, it can be used to work with the same types of web-based applications. Additionally, you get extended location data storage, higher performance, and improved scalability.

Oracle is a relational database management system created and run by the Oracle Corporation. Among all the types of SQL databases, Oracle stands out. Currently, it supports multiple data models like document, graph, relational, and key-value within a single database. In its latest releases, it refocused on cloud computing. Oracle database engine licensing is fully proprietary, with both free and paid options available.

Pros of Oracle

Innovations for daily workflow. Starting with the Oracle 12c release, when the software entered the hybrid cloud era, new cloud computing technologies appeared regularly. With every new release, Oracle tries to keep up with the innovation pace while focusing on information security, including active data guard, partitioning, improved backup, and recovery.

Strong tech support and documentation. Oracle ensures decent customer support and provides comprehensive tech documentation across multiple resources. So, you’ll likely find solutions to any issues that appear. You may also expect some community support.

Large capacity. Oracle’s multi-model solution allows for accommodating and processing a vast amount of data. Thanks to the recently released multi-tenancy feature, the database architecture now simplifies packing many databases and manages them smoothly. In combination with in-memory data processing capabilities, it creates a strong engine for synchronous data processing.

Cons of Oracle

High cost. Though the Oracle database has free editions, they are very limited in terms of functionality. Standard Edition, which doesn’t include all available features, costs $17,500 per unit. The Enterprise Edition is over $47,000 per unit.

Resource-consuming technology. The Oracle database needs powerful infrastructure. Not only does installation require a lot of disk space, but you’ll also have to consider constant hardware updates if you deploy it on-premises.

Hard learning curve. Oracle database is not a system to start using right away. It’s better to have certified Oracle DB engineers to run it. Oracle’s documentation, while covering many issues, can sometimes be overwhelming and even confusing. So, to install and run an Oracle database, you’ll have to consider hiring dedicated experts.

Large-scale enterprise applications. Given all those perks and pitfalls, you can consider Oracle RDBMS as a reasonable solution for online OLTP, data warehousing, and even mixed (OLTP and DW) database applications. If you have a billion records to hold and manage – and a sufficient budget to support it – Oracle hybrid cloud software is a good option.

Financial institutions. Oracle is widely used in the financial sector, where data integrity and security are paramount. Banks, insurance companies, and investment firms often rely on Oracle to manage sensitive financial data and transactions.

Government and public sector. Oracle is often chosen for its robust features and security in government and public sector applications, including national security, healthcare , and transportation systems.

The PostgreSQL database management system shares its popularity with MySQL. This is an object-relational DBMS where user-defined objects and table approaches are combined to build more complex data structures. Besides that, PostgreSQL has a lot of similarities with MySQL. It’s aimed at strengthening the standards of compliance and extensibility. Consequently, it can process any workload, for both single-machine products and complex applications. Owned and developed by PostgreSQL Global Development Group, it still remains completely open-source. This DBMS is available for use with platforms like Microsoft, iOS, Android, and many more.

Pros of Postgre

Great scalability. Vertical scalability is a hallmark of PostgreSQL. Considering that almost any custom software solution tends to grow, resulting in database extension, this particular option certainly supports business growth and development.

Support for custom data types. PostgreSQL natively supports many data types by default, such as JSON, XML, H-Store, and others. PostgreSQL takes advantage of it, being one of the few relational databases with strong support for NoSQL features. Additionally, it allows users to define their own data types. As your software business model may need different types of databases throughout its existence for better performance or application comprehensiveness, this option brings improved flexibility to the table.

Easily-integrated third-party tools. The PostgreSQL database management system has the strong support of additional tools , both free and commercial. The scope of these includes extensions to improve many aspects. For example, ClusterControl provides impressive assistance in managing, monitoring, and scaling SQL and NoSQL open-source databases. To make data comparison and synchronization more effective, consider using DB Data Directive. In case you’re going to scale up your data to heavy workloads, the pgBackRest backup and restore system will be a nice option to choose from.

Open-source and community-driven support. Postgres is completely open-source and supported by its community, strengthening it as a complete ecosystem. Additionally, developers can always expect free and prompt community assistance.

Cons of Postgre

Inconsistent documentation. While PostgreSQL has a large community and strongly supports its participants, the documentation still lacks consistency and completeness. As the PostgreSQL community is rather distributed, the documentation doesn’t follow uniform standards for all Postgre features.

Lack of reporting and auditing instruments. A significant shortcoming of PostgreSQL is the absence of revising tools that would show the current condition of a database. You have to continuously check if something goes wrong. There’s always a risk that DB engineers will notice a failure too late.

Due to complicated queries and a wide choice of custom interfaces accomplished with predefined functions, PostgreSQL is a perfect match for data analysis and warehousing. If you are building a database automation tool, PostgreSQL is the best fit for it due to its strong analytical capabilities, ACID compliance, and powerful SQL engine. All in all, it significantly accelerates the processing of vast amounts of data. This DBMS is popular with financial institutions and telecommunication systems.

As a completely commercial tool, Microsoft SQL Server is one of the most popular relational DBMSs, in addition to MySQL, PostgreSQL, and Oracle. It copes well with effective storing, changing, and managing relational data. To interact with SQL Server databases, DB engineers usually utilize the Transact-SQL (T-SQL) language, which is an extension of the SQL standard.

Pros of MSSQL

Variety of versions. Microsoft SQL Server provides a wide choice of different options with diverse functionalities. For instance, the Express edition with a free database offers entry-level tooling, the perfect match for learning and building desktop or small server data-driven applications. The Developers option allows for building and testing applications, including some enterprise functionalities, but without a production server license. For bigger projects, there are also Web, Standard, and Enterprise editions, with a varying extent of administrative capabilities and service levels.

End-to-end business data solution. With a focus on mostly commercial solutions, MSSQL provides a lot of business value-added features. The optional selection of components allows building ETL solutions, forming a knowledge base , and implementing data clearance. Also, it provides tools for overall data administration, online analytical processing, and data mining, additionally offering solutions for report and visualization generation.

Rich documentation and community assistance. With Microsoft SQL Server aimed at comprehensive database maintenance, the full online documentation also reflects this concept. The correspondingly structured guidelines, numerous whitepapers, and demos give a full picture of the MSSQL data system. Also, Microsoft Premier provides access to dedicated Microsoft community support, which is an advantage when a DB engineer needs assistance.

Cloud database support. A part of the consistent Microsoft ecosystem, MSSQL can be integrated with Microsoft Cloud, Azure SQL Database, or SQL Server on Azure Virtual Machines. The solutions allow shifting database administration to the cloud if your business software database becomes really overwhelming and hard to administer.

Cons of MSSQL

High cost . Being mostly used at the enterprise scale, MSSQL Server remains one of the most expensive solutions. Speaking of numbers, the Enterprise edition currently costs over $15, 123 per core, sold as 2 core packs.

Unclear and floating license conditions. Another issue is the ever-changing licensing process. The pricing strategy itself is hard to understand, and the elements included in a particular edition are floating, tending to shift from one to another.

Complicated tuning process. For those beginners who have to operate heavy data sets, working with query optimization and performance tuning may be problematic. As the process is not so obvious, it can create substantial bottlenecks early on.

MSSQL Server is a reasonable option for companies with other Microsoft product subscriptions. As Microsoft builds a robust ecosystem with seamless integration of services, MSSQL emerges as a powerful database solution. With its cloud accessibility and advanced data retrieval tools, MSSQL proves to be a valuable asset for businesses, ensuring a sustainable and efficient system that aligns with evolving needs.

SQLite is a self-sufficient, serverless, and no-configuration-required database management system. Frequently utilized as an embedded database, it is popular for small-scale mobile and desktop applications.

Pros of SQLite

Small in size and easily portable. SQLite is a streamlined database engine that operates without a separate server process. The entire database is contained within a single cross-platform disk file, enhancing its portability and simplifying its integration into applications.

Minimal resource consumption. SQLite is engineered for optimal memory and disk space efficiency, making it an ideal choice for applications with constrained resources, such as those found in mobile and IoT devices.

Reliable and user-friendly. SQLite is an ACID-compliant database, ensuring the integrity and consistency of data. Additionally, it is simple to set up and demands minimal configuration.

Cons of SQLite

Restricted concurrency. SQLite employs file-based locking, limiting its capacity to manage multiple concurrent write operations. This makes it less appropriate for applications with high write concurrency or multiple users accessing the database simultaneously.

Absence of advanced features. SQLite lacks some of the sophisticated features found in other database management systems, such as stored procedures, triggers, or user-defined functions.

Restricted scalability. Owing to its serverless structure, SQLite is not tailored for extensive applications or distributed settings. Its performance may diminish when handling substantial datasets or elevated levels of concurrent access.

SQLite is well-suited for modest-sized applications and mobile and desktop applications that demand a lightweight, easily portable, and user-friendly database. It is also fitting for embedded systems and IoT devices with limited resources where implementing a server-based DBMS would be unfeasible.

A free, open-source, non-relational DBMS, MongoDB also includes a commercial version. Although MongoDB wasn’t initially intended for structured data processing, it can be employed for applications that use both structured and unstructured data . In MongoDB, databases are connected to applications via database drivers. They are widely available within the database management system. Multiple data types are processed simultaneously and use the internal cache for this purpose.

Pros of MongoDB

Simple data access, storage, input, and retrieval. One of the benefits of MongoDB derived from its NoSQL nature is the fast and easy data operation. That is to say, data can be entered, stored, and withdrawn from the database quickly and without any additional confirmation. As with any other non-relational database, it places emphasis on RAM usage, so the records can be manipulated really fast and without any consequences to data integrity.

Easy compatibility with other data models. MongoDB is easily combined with different database management systems, both SQL and NoSQL types. Besides that, it has pluggable storage engine APIs. To make a long story short, this option allows third parties to build their own data storage engines for MongoDB. From a commercial point of view, it creates extra value for business software.

Horizontally scalable solution. Scalability – where data is spread out across a distributed network of manageable servers – is a facet of MongoDB’s fundamental nature. It becomes even more important for enterprises operating big data applications. Additionally, the database can allocate data across a cluster of machines. How can that help you? The data is distributed faster and equally, free of bulkiness. As it leads to faster data processing, the application performance is accelerated too.

Cons of MongoDB

Extensive memory consumption. The denormalization process, when previously normalized data in a database is grouped to increase performance, usually results in high memory consumption. Also, this DBMS keeps in memory all key names for each value pair. Beyond that, because there is no support for joins, Mongo databases have data oversupply, resulting in big memory waste and lower application performance.

Data insecurity. With a focus on fast data operation, MongoDB, like any other NoSQL DBMS, lacks data security. As user authentication isn’t a default Mongo option, and higher protection is available with a commercial edition only, you can’t consider it totally secure. Additionally, there are constant MongoDB update releases, with no guarantee that all amendments or data changes will work as they did before. Keep in mind that all manipulations should be formed around these updates, being covered with additional tests.

Complicated process to interpret into other query languages. As MongoDB wasn’t initially developed to deal with relational data models, the performance may slow down in these cases. Besides, the translation of SQL to MongoDB queries takes additional action to use the engine, which may delay the development and deployment.

MongoDB works best in real-time data integration and database scalability. For instance, it’s the right option for product catalogs due to its capacity to stock a multiplicity of objects with various attribute collections. Also, consider here analytic platforms, as MongoDB’s speed provides dynamic performance that can help track the user’s behavior in real time.

An open-source, NoSQL, in-memory data structure store, Redis can also be used as a cache. Instead of documents, it uses key-value pairs. Its distinct feature is that there are several options for data structuring, such as lists, sets, and hashes.

Allowing for data replication and supporting transactions, Redis executes commands in a queue instead of setting it one at a time.

Pros of Redis

Rapid solution. Due to its replication and transaction features, Redis processes the data really fast. The absence of dependencies and in-memory data store type makes Redis a worthy competitor even among simple SQL alternatives.

Massive data processing. From the data perception and refining perspective, Redis can be considered a colossus. It can easily upload up to 1GB of data for one entry. Add built-in data caching and you get a powerhouse data machine.

Cons of Redis

Dependency on the application memory. Total reliance and dependency on the application memory is a real drawback. That is to say, your database will crash if its size exceeds the size of available memory.

No support for query language or joins. Regarding compatibility with other dataset types, Redis lags behind. Given that at some time your business may need scaling and using other data formats, having rapid entries as a single option leaves this issue open.

Redis basically has a few different directions to work with. And the first of them is IoT applications. Here, heavy data from IoT devices can be transferred to Redis to process these records before keeping them in any steady data storage. Also, Redis is a perfect option for microservice architectures with scalable cloud hosting. As data here doesn’t have to be long-term persistent, Redis seems a reasonable decision.

Cassandra is a decentralized system developed by Apache. It’s a free Java -based DBMS with multi-replication and multi-deployment features as its strengths. These peculiarities allow for numerous query copying and deploying all of them at the same time. Being rapidly scalable, Cassandra allows for managing large data volumes by replicating them into multiple nodes. It eliminates the problem of database crash – if some of the nodes fail at any time, it’s replaced immediately, and the system keeps working as long as at least one single node is safe.

Cassandra uses its own query language, CQL. In its syntax, it’s very similar to SQL but doesn’t apply joins, replacing them with so-called column families . And the second difference is that not all columns in a table are stored for subqueries. Some of them are used as clustering columns, where adjacent data is put next to each other for fast retrieval. Why does that matter? It provides faster querying from massive datasets, accelerating data processing.

Pros of Cassandra

Data security. Due to its master node replication feature, Cassandra stays failure tolerant. It means that DB engineers can feel confident about data safety unless master nodes fail all at the same time. As long as it’s extremely unlikely, the database and the application built on it will stay sound and secure.

Flexibility and on-hand amendments . Cassandra’s simple syntax has the best of SQL and NoSQL. In addition to scalability, it largely contributes to dataset flexibility. Cassandra collects data on the go, and data retrieval shares the same simplicity, despite dataset size. This allows for enlarging the database to the fullest extent.

Cons of Cassandra

Slow reading. As Cassandra was initially designed for fast writing, its weakness lies in its incapacity for fast reading. One of the reasons for it is that the system doesn't have bottlenecks for incoming information. So while data can be written to the database quickly, the system may take longer to process and retrieve that data. This can be further explained by the fact that Cassandra spreads the data across multiple nodes in a cluster. When you query the data, it may have to read from various nodes, which can slow down the read performance.

Need for additional resources. As Cassandra processes multiple layers of data simultaneously, it demands enough power to do it. This means additional investment in both software and hardware. If this is the first time a company faces such a necessity and is not sure about the resources, then maybe it should consider other database systems.

Thanks to even data distribution, Cassandra is relevant in applications where large volumes of information are processed. For instance, it’s a great choice for data centers. Also, Cassandra fits well with real-time analytics, as it allows linear scaling and data increase in real time. You may also consider it for applications with constant data streaming , like weather apps. Another option is using it as a DBMS for an eCommerce store, as it allows for storing purchase history and other transactions. Add here the feasibility of tracking such data types as order status and packages, and you’ll get the full solution with eCommerce delivery integration.

Elasticsearch is a NoSQL, document-oriented database management system having a full-text search engine at its heart. Built on the Apache Lucene library, it stores data as a JSON file, supports RESTful APIs , and uses a powerful analytical engine for faster data retrieval. Being open-source software, it includes both free and paid editions.

Pros of Elasticsearch

Scalable architecture. One of Elasticsearch’s peculiarities is its robust distributed architecture. Its key structure options, such as clustering, indexing, sharding, and many more, provide extensive horizontal scaling, which allows for accommodating terabytes of records with further automation. The architecture’s abstraction levels streamline system management on both individual and aggregate levels.

Fast data processing. Due to the distributed data structure and built-in parallelization, the Elasticsearch DB shows excellent performance results. Even when executing a complex data query, it generates lightning search result responses. This is partly available due to documents being maintained close to relevant metadata in the index, which makes them fast to find.

Cons of Elasticsearch

Lack of multi-language support. When handling request or response data, Elasticsearch DBMS lags behind. Though it’s perfectly combined with Cassandra DB to complement database performance, other languages and formats are not available for it. In these terms, it only supports JSON document format.

Limited consistent health check tools. When something goes wrong, as it may at any stage, Elasticsearch can only show the status as “yellow” or “red.” Simply put, it has no reporting tools. Though issues are usually like memory threshold or disk capacity, DBA engineers complain about the situation.

Due to its NoSQL distributed nature and flexible data models, Elasticsearch is a great tool for eCommerce products with huge databases that tend to use search engines. It’s very helpful when creating or updating a customer’s profile regarding the workload that real-time engagement usually demands.

Firebase databases

Owned by Google, Firebase is a real-time Backend-as-a-Service used to develop web and mobile software. As far as NoSQL databases, there are two options: Firebase Realtime Database (providing real-time access to data residing in different platforms) and Cloud Firestore (offering more scalability and more complex data models). As such, both solutions fit nicely into the scenario when you need to deal with lots of data in real time: Changes for the databases are fetched as they happen. Google’s child stores data in JSON format and provides various data management offerings, including a convenient data browsing tool.

Pros of Firebase

Beginner friendliness. Firebase can be a great option when there’s little software development expertise available, as it presents an easy-to-use environment to kick off the project.

Convenient data access. Both Realtime and Firestore are great options for storing and managing different types of data. There is a Firebase console for easy data access. Being cloud-based and NoSQL, they offer decent flexibility and scalability when the amount of data grows. Moreover, Firebase tools allow for working with responsive applications and keeping data updated even when there’s no Internet connection.

Top-notch documentation. The solution comes with well-written documentation that facilitates the work with provided services for all users. It includes guidelines, technical documentation , SDK references, information about integration, and much more. If we get back to the StackOverflow survey, Firebase is the 12th most popular database choice of developers. The size of the product community is significant, which makes it easy to find answers to problems that pop up.

Cons of Firebase

Limited querying capabilities. While this is valid only for Realtime Database, it’s still an issue if you are mainly planning to use this storage. The problem here is that you are restricted to making simple queries as there are no filter capabilities for more complex ones. This is because the entire database is a big JSON file with no options for data modeling.

Limited data migration. If you use Firebase to host all your data, migrating it to another platform can become an issue. The service lacks migration tools to transfer data or set the default database of a project.

Firebase databases can be a good option to consider when your software deals with real-time data that needs to be synchronized between different browsers and devices. They are often chosen for such projects as messaging apps, social media apps, and gaming apps.

Amazon DynamoDB is a NoSQL database service managed by Amazon Web Services (AWS), designed for applications necessitating high scalability, low latency, and consistent performance.

Pros of Amazon DynamoDB

Exceptional scalability. DynamoDB can effortlessly scale up or down to accommodate any level of traffic and data, making it ideal for applications that experience rapid growth or fluctuating demand.

Low latency. DynamoDB delivers single-digit millisecond latency for read and write operations, ensuring quick and consistent data access, vital for real-time applications.

Fully managed service. DynamoDB handles operational tasks such as hardware provisioning, patching, and backups as a managed service, allowing you to concentrate on your application's development and features.

Adaptable data model. DynamoDB supports various data models, including key-value and document-oriented models, offering flexibility in structuring and querying your data.

Cons of Amazon DynamoDB

High cost. DynamoDB's pricing structure can be intricate and may result in higher costs compared to self-managed NoSQL databases, particularly for applications with variable or unpredictable workloads.

Restricted querying capabilities. Although DynamoDB supports basic queries and filters, it does not provide support for complex querying and aggregation operations needed in some use cases.

Vendor lock-in. As a proprietary AWS service, transitioning from DynamoDB to another database system might necessitate significant effort and planning.

Amazon DynamoDB is well-suited for applications requiring high scalability, low latency, and consistent performance, such as serverless apps, eCommerce platforms, gaming platforms, and IoT solutions. It is also ideal for serverless architectures and applications that utilize other AWS services, as it integrates seamlessly with the AWS ecosystem.

How to choose a database management system

Apart from the options described in the post, there are a lot of other database management systems out there. Each of them is good in its own way, having some drawbacks as well. Though we haven’t covered even a third of all databases, we tried to compare those commonly used for both small web applications and big data warehousing systems.

So, how do you choose the right one for your own software application?

If you are just starting a local eCommerce business , databases like MySQL can be a sensible jumping-off point that will also work well for web-based BI tools and OLTP systems.

In case you are striving to build an eCommerce giant with a complete buyer journey for your customer, you may go with Cassandra. To complement it with a powerful search engine, you may also attach the Elasticsearch database solution.

Speaking of Cassandra, it’s also a pretty respectable option for data centers and real-time analytics with oceanic volumes of data.

When speaking of analytic tools without multiple data layers, it may be reasonable to opt for NoSQL databases like MongoDB. It also performs well for product catalogs.

Following up the scope of data warehousing applications , MSSQL is also worth a mention, especially for companies with a number of other Microsoft subscriptions.

In terms of building an OLTP solution and data warehousing applications, Oracle is a good choice as well.

IoT applications and microservice architecture that tend to scale its data hosting will summarize our list of best use cases with Redis.

Sure, there are more database systems to consider. It all depends on your business model and your business needs.

Which one do you use? Please share your ideas with us.

CS403: Introduction to Modern Database Systems

Course introduction.

• Time: 42 hours
• Free Certificate

Course Syllabus

First, read the course syllabus. Then, enroll in the course by clicking "Enroll me". Click Unit 1 to read its introduction and learning outcomes. You will then see the learning materials and instructions on how to use them.

Unit 1: Introduction to Modern Database Systems

Different databases serve different purposes; each one is dependent upon both deployment environment and different types of user interactions. In this unit, we will ask a number of questions pertaining to databases: What are some database environments and user types? How can the database management system ensure control over data integrity, avoid data redundancy, and secure data, while at the same allowing interactions with different user types? In answering these questions, we will identify and determine the characteristics of databases, their many deployment environments, and the different categories of users that interact with them.

Completing this unit should take you approximately 5 hours.

Unit 2: Database Architecture and Date Languages

In order to properly create and then manage a database, we need to have a thorough understanding of the data it holds. Because data can be seen from different levels, we will introduce different data models and learn how to apply them in order to describe the structure of the database, thereby providing a "view" of the database for the different types of users introduced in the previous section. This unit explains database architecture and design using the ANSI/SPARC three-schema architecture.

Completing this unit should take you approximately 4 hours.

Unit 3: The History of Databases

Databases have existed for centuries: the maintenance of records and data has evolved from engravings to cards to digital storage. The history of databases gives us a view of the evolution of database models and the problems of each model. Each subsequent model was motivated by the limitations of previous models, the availability of new technology, the need to store and retrieve new types of data, or by the need to handle new volumes of data that exceeded the capabilities of current models. In this unit, we will present the four different models of representing data, discussing the different limits of each.

Completing this unit should take you approximately 3 hours.

Unit 4: The Entity-Relationship Model

Databases often hold a great amount of data. In order to build a database, we need to understand which entities should hold data and identify the connections that may exist between entities. In this unit, we will learn about the Entity-Relationship model, which will allow us to create a graphical view of the different elements of a database as well as the relationships between them. We will also learn the drawing conventions of the E-R model using a part-to-whole approach, beginning with those conventions used to represent a single entity, and concluding with conventions used to represent all relations in a database. An E-R model is a model of a database's requirements. If we view database development from the perspective of a software life-cycle model, E-R modeling corresponds to requirements analysis. In database terms, this is called conceptual modeling.

Unit 5: The Relational Database Model

The relational database model provides us with a way to understand how data can be perceived. While the E-R model represents the relations between elements of a database, it does not provide a logical view of its data. We will use the relational model to solve that problem. The relational model looks at entities as tables and allows operations to be performed on them. In this unit, we will learn how to map ER models into relations.

From a life-cycle perspective, the relational model corresponds to high-level design, and adds detail to the conceptual design. The database development evolves from requirements (specified in a conceptual model), to high-level database design (specified in a logical model), to an implementation model (specified in a detailed design and physical model). An E-R model is a particular modeling method for requirements, while a relational model is a method for database design.

Unit 6: Relational Algebra

We have seen that database entities can be viewed as logical tables. While this is useful in its own way, we can learn more from the data if we can perform operations on the tables within a database, as data from one table may not be meaningful without the data from another table. In this unit, we will introduce relational algebra, the mathematical notation used to represent how data retrievals and updates are performed on tables in a database. Understanding relational algebra will serve as a prelude to using the Structure Query Language (SQL).

One of the overall themes of computer science is commonality: common components are useful for building many kinds of applications. A database is one of these components, and its usefulness is due to its effectiveness and efficiency in creating, storing, and operating on all types of data. Relational algebra covers basic operations and composing them to form complex queries. Relational algebra is a mathematical system, or model, that formally specifies queries of a relational database, and is implemented as a formal language, SQL. A query against a database can be expressed as a SQL statement in more than one way, each having the same semantics. Relational algebra enables optimization of SQL queries, and allows you to structure queries in such a way that they execute more efficiently.

Unit 7: Introduction to Data Normalization

In this course, we have learned that entities in a database can be thought of as logical tables. Data in a table must be stored in a normalized way. First, we will identify the properties of a normalized table, learning about the process of normalization and its importance to the structure of a database. We will then study the four major steps of normalization and discuss the database anomalies that can result in the absence of normalization. Data normalization is the process of writing the data so that data redundancy is reduced and data integrity is increased.

Unit 8: Introduction to SQL

Structured Query Language (SQL) is the main data definition language used for the creation and maintenance of databases. In this unit, we will look at basic SQL syntax, including some data definition and data manipulation language commands. When developing or using a database, we have to consider how the data should be organized so that it is stored and accessed efficiently, and how needed information can be found. If the data is for a specific application, the solutions would be based on knowledge of the application domain. However, a database is a common system used for many applications, and thus many solutions need to be generic. Relational algebra, relational calculus, and normalization help address these problems.

The evolution from a specific solution to a general solution is similar to the evolution from a special-purpose to a general-purpose computer, which was accomplished by the development of programming languages. In a similar manner, database languages implement the creation of databases and access of information for many applications, which allows us to use databases as a common component for many applications and types of systems.

Unit 9: Basic Select Statements

The Select statement or command is used to find and extract data from specified tables and which satisfy specified conditions.

Unit 10: The Join Statement

Programmers frequently join data from a number of different tables in order to obtain more information. They also – perhaps even more frequently – build queries to obtain information from more than one table in order to generate better information. In this section, we will learn about SQL Joins, which allow us to create complex queries, combine data from different tables, and obtain a new result set that can provide us with a better understanding of the data and maximize database flexibility.

Study Guide

This study guide will help you get ready for the final exam. It discusses the key topics in each unit, walks through the learning outcomes, and lists important vocabulary. It is not meant to replace the course materials!

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If you come across any urgent problems, email [email protected].

Certificate Final Exam

Take this exam if you want to earn a free Course Completion Certificate.

To receive a free Course Completion Certificate, you will need to earn a grade of 70% or higher on this final exam. Your grade for the exam will be calculated as soon as you complete it. If you do not pass the exam on your first try, you can take it again as many times as you want, with a 7-day waiting period between each attempt.

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Data Topics

• Data Architecture
• Data Literacy
• Data Science
• Data Strategy
• Data Modeling
• Governance & Quality
• Data Education
• Enterprise Information Management
• Information Management Articles

Database Management Trends in 2022

Historically, Database Management systems (DBMS) were simple software programs and associated hardware that allowed users to access data from different geographical locations. The system offers its users the ability to store data without concerns about structural changes, or the data’s physical location. Additionally, a Database Management system (DBMS) can set restrictions on the data being […]

Historically, Database Management systems (DBMS) were simple software programs and associated hardware that allowed users to access data from different geographical locations. The system offers its users the ability to store data without concerns about structural changes, or the data’s physical location. Additionally, a Database Management system (DBMS) can set restrictions on the data being used, and the services available to each user.

The coronavirus pandemic, with its emphasis on isolation, has accelerated the acceptance of online shopping and working remotely. Many small businesses have made the decision to digitize and are shifting to the cloud at an accelerated rate.

The market for Database Management systems is growing fast and, according to Research and Markets , the global DBMS market was estimated to have reached $63.9 trillion in 2020, and is projected to reach $142.7 trillion by 2027.

Increasingly, organizations are merging their data warehouses and data lakes into cloud storage systems. Shifting to the cloud requires a Database Management system (DBMS) for working with a broad range of new data formats.

Database Management trends in 2022 include:

Cloud-based DBMS

• Automation and DBMS
• Augmented DBMS
• Increased security
• In-memory databases
• Graph databases
• Open source DBMSs
• Databases-as-a-service

These trends are based, to a large extent, on businesses wanting to provide access to their products and services over the internet, with the goal of maintaining (or increasing) profits during the pandemic.

The Gartner report The Future of the DBMS Market Is Cloud predicts the use of cloud-based DBMSs will increase. The market for Database Management systems is being driven increasingly by cloud services, and no longer by on-premise systems. Certainly, there are large organizations still using on-premises DBMS solutions, however, they are combining it with a cloud-based DBMS and using a “hybrid” approach .

The choice of using a cloud-based DBMS service is being supported, in part, by a shift toward using software-as-a-service applications. This is a very reasonable alternative to the upfront expenses required for deploying an on-premise Data Management system. Improved data sharing, improved data integration, and data security are also reasons for using a cloud-based Database Management system.

Database Management Trends & Automated Services

Automated services can help streamline the process of Database Management. An automated DBMS can help significantly in sifting through the massive amounts of data generated by eCommerce, mobile applications, customer relationship management, and social media. As a consequence, organizations are experiencing enormous surges in the amounts of data being stored. These massive amounts of data can be used to the business’ advantage, providing useful insights about their customers and products.

Data automation supports the uploading, handling, and processing of data by automated tools, rather than performing the tasks manually. Automating data processing improves efficiency by working much faster than could be done manually, and by eliminating human error.

Having automation as part of the data analytics process allows researchers to focus on analyzing the data instead preparing it. Automation also helps improve the integration of data from multiple data sources to a single one. Examples of DBMS automation that is used on a daily basis include:

• Customer support
• Employee analytics
• Purchase order automation
• Desk support
• Scheduling meetings

DBMS automation is also being used to provide security, data integration, and Data Governance. Most organizations must meet several compliance requirements, and DBMS automation helps to meet them. The GDPR, for instance, requires user data be anonymous and used for statistical purposes before it is shared with external partners, and this can be done with automated services.

Augmented Data Management (ADM)

Augmented Data Management uses machine learning and artificial intelligence to automate Data Management tasks, such as spotting anomalies within large amounts of data and resolving Data Quality issues.

The AI models are specifically designed to perform Data Management tasks, taking less time and making fewer errors. Todd Ramlin, a manager of Cable Compare, in describing the benefits of augmented Data Management, said,

“Historically, data scientists and engineers have spent the majority of their time manually accessing, preparing, and managing data, but Augmented Data Management is changing that. ADM uses artificial intelligence and machine learning to automate manual tasks in Data Management. It simplifies, optimizes, and automates operations in Data Quality, Metadata Management, Master Data Management, and Database Management systems. AI/ML can offer smart recommendations based on pre-learned models of solutions to specific data tasks. The automation of manual tasks will lead to increased productivity and better data outcomes.”

Data Security (and Avoiding Data Breaches)

There have been several high-profile data breaches in the last year. For example, LinkedIn was breached in June 2021, resulting in 700 million users having their information sold online. In September, the retailer Neiman Marcus was breached, with 4.8 million customers being affected. In October of 2021, it was announced the information of 1.5 billion Facebook users was put up for sale in a hacker’s forum. And those are just a few of the hundreds of data breaches taking place in 2021. In the state of Washington, the number of known breaches went up from 220 last year to 280 in 2021.

Security has always been a consideration for database administrators , but the recent breaches have made it a primary concern. As a result, increased database security has become a trending issue.

In-Memory Databases

In-memory databases are gaining popularity because they respond faster than traditional systems. An in-memory database (IMDB) eliminates the disk drive, and instead stores data in the computer’s main memory – its random access memory or RAM. This tactic reduces response times.

The lowered response times is made possible because there is no need for translation and caching. The data being used remains in the same form as when it arrived, and in the same form as the application working with it. These databases are commonly used by applications that depend on rapid response times and offer real-time Data Management. The industries operating and benefitting from in-memory databases include banking, travel, gaming, and telecommunications.

The Graph Database

Graph databases provide an excellent way to establish and research relationships in a quick and easy way. They use nodes and edges to form data relationships (nodes represent entities, and edges represent their relationships). Graph databases are designed to assign the relationship between data entities with the same importance the data receives. The design results in only the data which is needed being accessed, while unnecessary data remains untouched, making data retrieval more efficient.

Currently, graph databases are being used with network and IT management. They have been used for accessing social media and providing business intelligence, and for finding anomalies and enhancing security. More recently, graph databases have started being used successfully with:

• Network management
• Telecommunications
• Impact analysis
• Data center and IT asset management
• Cloud platform management

Open Source Databases

Ten years ago, “open source” Database Management systems were not as commonly used as they are now. They are now used by 7% of the market. Open source technologies generally evolve and develop quickly, and this includes databases. Open source technologies are typically designed to minimize the barriers of adoption, and are extremely attractive to apps developers working with cloud-native platforms.

Gartner has predicted that by 2022, over 70% of the new in-house applications created will be developed using an open source DBMS (OSDBMS), or a cloud-based OSDBMS platform-as-a-service. Open source has shown itself to be a successful method for tapping into creativity and problem-solving skills. It has been used to develop and distribute useful business-critical software, and its use will continue to grow.

The Database-as-a-Service

Generally speaking, in the past databases were not designed to work with microservices. Databases were normally monolithic. Monolithic architecture is the traditional way of developing applications. Monolithic software is developed as a single, indivisible unit. Monolithic applications typically lack modularity and use one large code base.

The Database Management trend of using databases-as-a-service is based on the behavior of development teams designing and building applications, while using a microservice. When an application “interacts with a database,” the data is shared by all the application’s components.

With a microservices app, however, the data is not shared, but decentralized. Each microservice is autonomous and comes with its own private data storage, relevant to its functionality. One service cannot modify the data stored inside another service’s database. This creates a conflict for integrating microservices with a DBMS.   

Fortunately, many new database offerings (primarily NoSQL vendors like AWS DynamoDB and MongoDB) support the flexibility, redundancy, and scalability requirements, and the serverless architecture pattern needed for microservices .

Database Management Trends and Evolution

Until recently, DBMSs have been considered consistent, trustworthy structures that offered reliability without drama. However, with the pandemic acting as an accelerant, databases are evolving to process data more efficiently, while simultaneously becoming more intelligent. To access this evolution, and embrace the economic benefits offered by the cloud, businesses are increasingly shifting to cloud databases.

Currently, a large part of the DBMS market’s growth is being driven by organizations moving their Database Management systems to the cloud, which provides faster integration and configuration. Additionally, improved security protocols and superior tools have made remote work a more reasonable option, and has had significant impact on the market’s current growth. The increasing number of demands being made on DBMSs – and the increasing number of solutions – makes research a key step in selecting a new Database Management system.

Image used under license from Shutterstock.com

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Advances in database systems education: Methods, tools, curricula, and way forward

• Published: 31 August 2022
• Volume 28 , pages 2681–2725, ( 2023 )

Cite this article

• Muhammad Ishaq 1 ,
• Adnan Abid 2 , 3 ,
• Muhammad Shoaib Farooq 3 ,
• Muhammad Faraz Manzoor 3 , 4 ,
• Uzma Farooq 3 ,
• Kamran Abid 5 &
• Mamoun Abu Helou 6  

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Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students’ interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.

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1 Introduction

Database systems play a pivotal role in the successful implementation of the information systems to ensure the smooth running of many different organizations and companies (Etemad & Küpçü, 2018 ; Morien, 2006 ). Therefore, at least one course about the fundamentals of database systems is taught in every computing and information systems degree (Nagataki et al., 2013 ). Database System Education (DSE) is concerned with different aspects of data management while developing software (Park et al., 2017 ). The IEEE/ACM computing curricula guidelines endorse 30–50 dedicated hours for teaching fundamentals of design and implementation of database systems so as to build a very strong theoretical and practical understanding of the DSE topics (Cvetanovic et al., 2010 ).

Practically, most of the universities offer one user-oriented course at undergraduate level that covers topics related to the data modeling and design, querying, and a limited number of hours on theory (Conklin & Heinrichs, 2005 ; Robbert & Ricardo, 2003 ), where it is often debatable whether to utilize a design-first or query-first approach. Furthermore, in order to update the course contents, some recent trends, including big data and the notion of NoSQL should also be introduced in this basic course (Dietrich et al., 2008 ; Garcia-Molina, 2008 ). Whereas, the graduate course is more theoretical and includes topics related to DB architecture, transactions, concurrency, reliability, distribution, parallelism, replication, query optimization, along with some specialized classes.

Researchers have designed a variety of tools for making different concepts of introductory database course more interesting and easier to teach and learn interactively (Brusilovsky et al., 2010 ) either using visual support (Nagataki et al., 2013 ), or with the help of gamification (Fisher & Khine, 2006 ). Similarly, the instructors have been improvising different methods to teach (Abid et al., 2015 ; Domínguez & Jaime, 2010 ) and evaluate (Kawash et al., 2020 ) this theoretical and practical course. Also, the emerging and hot topics such as cloud computing and big data has also created the need to revise the curriculum and methods to teach DSE (Manzoor et al., 2020 ).

The research in database systems education has evolved over the years with respect to modern contents influenced by technological advancements, supportive tools to engage the learners for better learning, and improvisations in teaching and assessment methods. Particularly, in recent years there is a shift from self-describing data-driven systems to a problem-driven paradigm that is the bottom-up approach where data exists before being designed. This mainly relies on scientific, quantitative, and empirical methods for building models, while pushing the boundaries of typical data management by involving mathematics, statistics, data mining, and machine learning, thus opening a multidisciplinary perspective. Hence, it is important to devote a few lectures to introducing the relevance of such advance topics.

Researchers have provided useful review articles on other areas including Introductory Programming Language (Mehmood et al., 2020 ), use of gamification (Obaid et al., 2020 ), research trends in the use of enterprise service bus (Aziz et al., 2020 ), and the role of IoT in agriculture (Farooq et al., 2019 , 2020 ) However, to the best of our knowledge, no such study was found in the area of database systems education. Therefore, this study discusses research work published in different areas of database systems education involving curricula, tools, and approaches that have been proposed to teach an introductory course on database systems in an effective manner. The rest of the article has been structured in the following manner: Sect.  2 presents related work and provides a comparison of the related surveys with this study. Section  3 presents the research methodology for this study. Section  4 analyses the major findings of the literature reviewed in this research and categorizes it into different important aspects. Section  5 represents advices for the instructors and future directions. Lastly, Sect.  6 concludes the article.

2 Related work

Systematic Literature Reviews have been found to be a very useful artifact for covering and understanding a domain. A number of interesting review studies have been found in different fields (Farooq et al., 2021 ; Ishaq et al., 2021 ). Review articles are generally categorized into narrative or traditional reviews (Abid et al., 2016 ; Ramzan et al., 2019 ), systematic literature review (Naeem et al., 2020 ) and meta reviews or mapping study (Aria & Cuccurullo, 2017 ; Cobo et al., 2012 ; Tehseen et al., 2020 ). This study presents a systematic literature review on database system education.

The database systems education has been discussed from many different perspectives which include teaching and learning methods, curriculum development, and the facilitation of instructors and students by developing different tools. For instance, a number of research articles have been published focusing on developing tools for teaching database systems course (Abut & Ozturk, 1997 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Furthermore, few authors have evaluated the DSE tools by conducting surveys and performing empirical experiments so as to gauge the effectiveness of these tools and their degree of acceptance among important stakeholders, teachers and students (Brusilovsky et al., 2010 ; Nelson & Fatimazahra, 2010 ). On the other hand, some case studies have also been discussed to evaluate the effectiveness of the improvised approaches and developed tools. For example, Regueras et al. ( 2007 ) presented a case study using the QUEST system, in which e-learning strategies are used to teach the database course at undergraduate level, while, Myers and Skinner ( 1997 ) identified the conflicts that arise when theories in text books regarding the development of databases do not work on specific applications.

Another important facet of DSE research focuses on the curriculum design and evolution for database systems, whereby (Alrumaih, 2016 ; Bhogal et al., 2012 ; Cvetanovic et al., 2010 ; Sahami et al., 2011 ) have proposed solutions for improvements in database curriculum for the better understanding of DSE among the students, while also keeping the evolving technology into the perspective. Similarly, Mingyu et al. ( 2017 ) have shared their experience in reforming the DSE curriculum by adding topics related to Big Data. A few authors have also developed and evaluated different tools to help the instructors teaching DSE.

There are further studies which focus on different aspects including specialized tools for specific topics in DSE (Mcintyre et al, 1995 ; Nelson & Fatimazahra, 2010 ). For instance, Mcintyre et al. ( 1995 ) conducted a survey about using state of the art software tools to teach advanced relational database design courses at Cleveland State University. However, the authors did not discuss the DSE curricula and pedagogy in their study. Similarly, a review has been conducted by Nelson and Fatimazahra ( 2010 ) to highlight the fact that the understanding of basic knowledge of database is important for students of the computer science domain as well as those belonging to other domains. They highlighted the issues encountered while teaching the database course in universities and suggested the instructors investigate these difficulties so as to make this course more effective for the students. Although authors have discussed and analyzed the tools to teach database, the tools are yet to be categorized according to different methods and research types within DSE. There also exists an interesting systematic mapping study by Taipalus and Seppänen ( 2020 ) that focuses on teaching SQL which is a specific topic of DSE. Whereby, they categorized the selected primary studies into six categories based on their research types. They utilized directed content analysis, such as, student errors in query formulation, characteristics and presentation of the exercise database, specific or non-specific teaching approach suggestions, patterns and visualization, and easing teacher workload.

Another relevant study that focuses on collaborative learning techniques to teach the database course has been conducted by Martin et al. ( 2013 ) This research discusses collaborative learning techniques and adapted it for the introductory database course at the Barcelona School of Informatics. The motive of the authors was to introduce active learning methods to improve learning and encourage the acquisition of competence. However, the focus of the study was only on a few methods for teaching the course of database systems, while other important perspectives, including database curricula, and tools for teaching DSE were not discussed in this study.

The above discussion shows that a considerable amount of research work has been conducted in the field of DSE to propose various teaching methods; develop and test different supportive tools, techniques, and strategies; and to improve the curricula for DSE. However, to the best of our knowledge, there is no study that puts all these relevant and pertinent aspects together while also classifying and discussing the supporting methods, and techniques. This review is considerably different from previous studies. Table 1 highlights the differences between this study and other relevant studies in the field of DSE using ✓ and – symbol reflecting "included" and "not included" respectively. Therefore, this study aims to conduct a systematic mapping study on DSE that focuses on compiling, classifying, and discussing the existing work related to pedagogy, supporting tools, and curricula.

3 Research methodology

In order to preserve the principal aim of this study, which is to review the research conducted in the area of database systems education, a piece of advice has been collected from existing methods described in various studies (Elberzhager et al., 2012 ; Keele et al., 2007 ; Mushtaq et al., 2017 ) to search for the relevant papers. Thus, proper research objectives were formulated, and based on them appropriate research questions and search strategy were formulated as shown in Fig.  1 .

Research methodology

4 Research objectives

The Following are the research objectives of this study:

To find high quality research work in DSE.

To categorize different aspects of DSE covered by other researchers in the field.

To provide a thorough discussion of the existing work in this study to provide useful information in the form of evolution, teaching guidelines, and future research directions of the instructors.

5 Research questions

In order to fulfill the research objectives, some relevant research questions have been formulated. These questions along with their motivations have been presented in Table 2 .

5.1 Search strategy

The Following search string used to find relevant articles to conduct this study. “Database” AND (“System” OR “Management”) AND (“Education*” OR “Train*” OR “Tech*” OR “Learn*” OR “Guide*” OR “Curricul*”).

Articles have been taken from different sources i.e. IEEE, Springer, ACM, Science Direct and other well-known journals and conferences such as Wiley Online Library, PLOS and ArXiv. The planning for search to find the primary study in the field of DSE is a vital task.

5.2 Study selection

A total of 29,370 initial studies were found. These articles went through a selection process, and two authors were designated to shortlist the articles based on the defined inclusion criteria as shown in Fig.  2 . Their conflicts were resolved by involving a third author; while the inclusion/exclusion criteria were also refined after resolving the conflicts as shown in Table 3 . Cohen’s Kappa coefficient 0.89 was observed between the two authors who selected the articles, which reflects almost perfect agreement between them (Landis & Koch, 1977 ). While, the number of papers in different stages of the selection process for all involved portals has been presented in Table 4 .

Study selection

Title based search: Papers that are irrelevant based on their title are manually excluded in the first stage. At this stage, there was a large portion of irrelevant papers. Only 609 papers remained after this stage.

Abstract based search: At this stage, abstracts of the selected papers in the previous stage are studied and the papers are categorized for the analysis along with research approach. After this stage only 152 papers were left.

Full text based analysis: Empirical quality of the selected articles in the previous stage is evaluated at this stage. The analysis of full text of the article has been conducted. The total of 70 papers were extracted from 152 papers for primary study. Following questions are defined for the conduction of final data extraction.

5.2.1 Quality assessment criteria

Following are the criteria used to assess the quality of the selected primary studies. This quality assessment was conducted by two authors as explained above.

The study focuses on curricula, tools, approach, or assessments in DSE, the possible answers were Yes (1), No (0)

The study presents a solution to the problem in DSE, the possible answers to this question were Yes (1), Partially (0.5), No (0)

The study focuses on empirical results, Yes (1), No (0)

The study is published in a well reputed venue that is adjudged through the CORE ranking of conferences, and Scientific Journal Ranking (SJR). The possible answers to this question are given in Table 5 .

Almost 50.00% of papers had scored more than average and 33.33% of papers had scored between the average range i.e., 2.50–3.50. Some articles with the score below 2.50 have also been included in this study as they present some useful information and were published in education-based journals. Also, these studies discuss important demography and technology based aspects that are directly related to DSE.

5.3 Threats to validity

The validity of this study could be influenced by the following factors during the literature of this publication.

Construct validity

In this study this validity identifies the primary study for research (Elberzhager et al., 2012 ). To ensure that many primary studies have been included in this literature two authors have proposed possible search keywords in multiple repetitions. Search string is comprised of different terms related to DS and education. Though, list might be incomplete, count of final papers found can be changed by the alternative terms (Ampatzoglou et al., 2013 ). IEEE digital library, Science direct, ACM digital library, Wiley Online Library, PLOS, ArXiv and Google scholar are the main libraries where search is done. We believe according to the statistics of search engines of literature the most research can be found on these digital libraries (Garousi et al., 2013 ). Researchers also searched related papers in main DS research sites (VLDB, ICDM, EDBT) in order to minimize the risk of missing important publication.

Including the papers that does not belong to top journals or conferences may reduce the quality of primary studies in this research but it indicates that the representativeness of the primary studies is improved. However, certain papers which were not from the top publication sources are included because of their relativeness wisth the literature, even though they reduce the average score for primary studies. It also reduces the possibility of alteration of results which might have caused by the improper handling of duplicate papers. Some cases of duplications were found which were inspected later whether they were the same study or not. The two authors who have conducted the search has taken the final decision to the select the papers. If there is no agreement between then there must be discussion until an agreement is reached.

Internal validity

This validity deals with extraction and data analysis (Elberzhager et al., 2012 ). Two authors carried out the data extraction and primary studies classification. While the conflicts between them were resolved by involving a third author. The Kappa coefficient was 0.89, according to Landis and Koch ( 1977 ), this value indicates almost perfect level of agreement between the authors that reduces this threat significantly.

Conclusion validity

This threat deals with the identification of improper results which may cause the improper conclusions. In this case this threat deals with the factors like missing studies and wrong data extraction (Ampatzoglou et al., 2013 ). The objective of this is to limit these factors so that other authors can perform study and produce the proper conclusions (Elberzhager et al., 2012 ).

Interpretation of results might be affected by the selection and classification of primary studies and analyzing the selected study. Previous section has clearly described each step performed in primary study selection and data extraction activity to minimize this threat. The traceability between the result and data extracted was supported through the different charts. In our point of view, slight difference based on the publication selection and misclassification would not alter the main results.

External validity

This threat deals with the simplification of this research (Mateo et al., 2012 ). The results of this study were only considered that related to the DSE filed and validation of the conclusions extracted from this study only concerns the DSE context. The selected study representativeness was not affected because there was no restriction on time to find the published research. Therefore, this external validity threat is not valid in the context of this research. DS researchers can take search string and the paper classification scheme represented in this study as an initial point and more papers can be searched and categorized according to this scheme.

6 Analysis of compiled research articles

This section presents the analysis of the compiled research articles carefully selected for this study. It presents the findings with respect to the research questions described in Table 2 .

6.1 Selection results

A total of 70 papers were identified and analyzed for the answers of RQs described above. Table 6 represents a list of the nominated papers with detail of the classification results and their quality assessment scores.

6.1.1 RQ1.Categorization of research work in DSE field

The analysis in this study reveals that the literature can be categorized as: Tools: any additional application that helps instructors in teaching and students in learning. Methods: any improvisation aimed at improving pedagogy or cognition. Curriculum: refers to the course content domains and their relative importance in a degree program, as shown in Fig.  3 .

Taxonomy of DSE study types

Most of the articles provide a solution by gathering the data and also prove the novelty of their research through results. These papers are categorized as experiments w.r.t. their research types. Whereas, some of them case study papers which are used to generate an in depth, multifaceted understanding of a complex issue in its real-life context, while few others are review studies analyzing the previously used approaches. On the other hand, a majority of included articles have evaluated their results with the help of experiments, while others conducted reviews to establish an opinion as shown in Fig.  4 .

Cross Mapping of DSE study type and research Types

Educational tools, especially those related to technology, are making their place in market faster than ever before (Calderon et al., 2011 ). The transition to active learning approaches, with the learner more engaged in the process rather than passively taking in information, necessitates a variety of tools to help ensure success. As with most educational initiatives, time should be taken to consider the goals of the activity, the type of learners, and the tools needed to meet the goals. Constant reassessment of tools is important to discover innovation and reforms that improve teaching and learning (Irby & Wilkerson, 2003 ). For this purpose, various type of educational tools such as, interactive, web-based and game based have been introduced to aid the instructors in order to explain the topic in more effective way.

The inclusion of technology into the classroom may help learners to compete in the competitive market when approaching the start of their career. It is important for the instructors to acknowledge that the students are more interested in using technology to learn database course instead of merely being taught traditional theory, project, and practice-based methods of teaching (Adams et al., 2004 ). Keeping these aspects in view many authors have done significant research which includes web-based and interactive tools to help the learners gain better understanding of basic database concepts.

Great research has been conducted with the focus of students learning. In this study we have discussed the students learning supportive with two major finding’s objectives i.e., tools which prove to be more helpful than other tools. Whereas, proposed tools with same outcome as traditional classroom environment. Such as, Abut and Ozturk ( 1997 ) proposed an interactive classroom environment to conduct database classes. The online tools such as electronic “Whiteboard”, electronic textbooks, advance telecommunication networks and few other resources such as Matlab and World Wide Web were the main highlights of their proposed smart classroom. Also, Pahl et al. ( 2004 ) presented an interactive multimedia-based system for the knowledge and skill oriented Web-based education of database course students. The authors had differentiated their proposed classroom environment from traditional classroom-based approach by using tool mediated independent learning and training in an authentic setting. On the other hand, some authors have also evaluated the educational tools based on their usage and impact on students’ learning. For example, Brusilovsky et al. ( 2010 )s evaluated the technical and conceptual difficulties of using several interactive educational tools in the context of a single course. A combined Exploratorium has been presented for database courses and an experimental platform, which delivers modified access to numerous types of interactive learning activities.

Also, Taipalus and Perälä ( 2019 ) investigated the types of errors that are persistent in writing SQL by the students. The authors also contemplated the errors while mapping them onto different query concepts. Moreover, Abelló Gamazo et al. ( 2016 ) presented a software tool for the e-assessment of relational database skills named LearnSQL. The proposed software allows the automatic and efficient e-learning and e-assessment of relational database skills. Apart from these, Yue ( 2013 ) proposed the database tool named Sakila as a unified platform to support instructions and multiple assignments of a graduate database course for five semesters. According to this study, students find this tool more useful and interesting than the highly simplified databases developed by the instructor, or obtained from textbook. On the other hand, authors have proposed tools with the main objective to help the student’s grip on the topic by addressing the pedagogical problems in using the educational tools. Connolly et al. ( 2005 ) discussed some of the pedagogical problems sustaining the development of a constructive learning environment using problem-based learning, a simulation game and interactive visualizations to help teach database analysis and design. Also, Yau and Karim ( 2003 ) proposed smart classroom with prevalent computing technology which will facilitate collaborative learning among the learners. The major aim of this smart classroom is to improve the quality of interaction between the instructors and students during lecture.

Student satisfaction is also an important factor for the educational tools to more effective. While it supports in students learning process it should also be flexible to achieve the student’s confidence by making it as per student’s needs (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Also, Cvetanovic et al. ( 2010 ) has proposed a web-based educational system named ADVICE. The proposed solution helps the students to reduce the gap between DBMS, theory and its practice. On the other hand, authors have enhanced the already existing educational tools in the traditional classroom environment to addressed the student’s concerns (Nelson & Fatimazahra, 2010 ; Regueras et al., 2007 ) Table 7 .

Hands on database development is the main concern in most of the institute as well as in industry. However, tools assisting the students in database development and query writing is still major concern especially in SQL (Brusilovsky et al., 2010 ; Nagataki et al., 2013 ).

Student’s grades reflect their conceptual clarity and database development skills. They are also important to secure jobs and scholarships after passing out, which is why it is important to have the educational learning tools to help the students to perform well in the exams (Cvetanovic et al., 2010 ; Taipalus et al., 2018 ). While, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Subsequently, existing educational tools needs to be upgraded or replaced by the more suitable assessment oriented interactive tools to attend challenging students needs (Pahl et al., 2004 ; Yuelan et al., 2011 ).

One other objective of developing the educational tools is to increase the interaction between the students and the instructors. In the modern era, almost every institute follows the student centered learning(SCL). In SCL the interaction between students and instructor increases with most of the interaction involves from the students. In order to support SCL the educational based interactive and web-based tools need to assign more roles to students than the instructors (Abbasi et al., 2016 ; Taipalus & Perälä, 2019 ; Yau & Karim, 2003 ).

Theory versus practice is still one of the main issues in DSE teaching methods. The traditional teaching method supports theory first and then the concepts learned in the theoretical lectures implemented in the lab. Whereas, others think that it is better to start by teaching how to write query, which should be followed by teaching the design principles for database, while a limited amount of credit hours are also allocated for the general database theory topics. This part of the article discusses different trends of teaching and learning style along with curriculum and assessments methods discussed in DSE literature.

A variety of teaching methods have been designed, experimented, and evaluated by different researchers (Yuelan et al., 2011 ; Chen et al., 2012 ; Connolly & Begg, 2006 ). Some authors have reformed teaching methods based on the requirements of modern way of delivering lectures such as Yuelan et al. ( 2011 ) reform teaching method by using various approaches e.g. a) Modern ways of education: includes multimedia sound, animation, and simulating the process and working of database systems to motivate and inspire the students. b) Project driven approach: aims to make the students familiar with system operations by implementing a project. c) Strengthening the experimental aspects: to help the students get a strong grip on the basic knowledge of database and also enable them to adopt a self-learning ability. d) Improving the traditional assessment method: the students should turn in their research and development work as the content of the exam, so that they can solve their problem on their own.

The main aim of any teaching method is to make student learn the subject effectively. Student must show interest in order to gain something from the lectures delivered by the instructors. For this, teaching methods should be interactive and interesting enough to develop the interest of the students in the subject. Students can show interest in the subject by asking more relative questions or completing the home task and assignments on time. Authors have proposed few teaching methods to make topic more interesting such as, Chen et al. ( 2012 ) proposed a scaffold concept mapping strategy, which considers a student’s prior knowledge, and provides flexible learning aids (scaffolding and fading) for reading and drawing concept maps. Also, Connolly & Begg (200s6) examined different problems in database analysis and design teaching, and proposed a teaching approach driven by principles found in the constructivist epistemology to overcome these problems. This constructivist approach is based on the cognitive apprenticeship model and project-based learning. Similarly, Domínguez & Jaime ( 2010 ) proposed an active method for database design through practical tasks development in a face-to-face course. They analyzed results of five academic years using quasi experimental. The first three years a traditional strategy was followed and a course management system was used as material repository. On the other hand, Dietrich and Urban ( 1996 ) have described the use of cooperative group learning concepts in support of an undergraduate database management course. They have designed the project deliverables in such a way that students develop skills for database implementation. Similarly, Zhang et al. ( 2018 ) have discussed several effective classroom teaching measures from the aspects of the innovation of teaching content, teaching methods, teaching evaluation and assessment methods. They have practiced the various teaching measures by implementing the database technologies and applications in Qinghai University. Moreover, Hou and Chen ( 2010 ) proposed a new teaching method based on blending learning theory, which merges traditional and constructivist methods. They adopted the method by applying the blending learning theory on Access Database programming course teaching.

Problem solving skills is a key aspect to any type of learning at any age. Student must possess this skill to tackle the hurdles in institute and also in industry. Create mind and innovative students find various and unique ways to solve the daily task which is why they are more likeable to secure good grades and jobs. Authors have been working to introduce teaching methods to develop problem solving skills in the students(Al-Shuaily, 2012 ; Cai & Gao, 2019 ; Martinez-González & Duffing, 2007 ; Gudivada et al., 2007 ). For instance, Al-Shuaily ( 2012 ) has explored four cognitive factors such as i) Novices’ ability in understanding, ii) Novices’ ability to translate, iii) Novice’s ability to write, iv) Novices’ skills that might influence SQL teaching, and learning methods and approaches. Also, Cai and Gao ( 2019 ) have reformed the teaching method in the database course of two higher education institutes in China. Skills and knowledge, innovation ability, and data abstraction were the main objective of their study. Similarly, Martinez-González and Duffing ( 2007 ) analyzed the impact of convergence of European Union (EU) in different universities across Europe. According to their study, these institutes need to restructure their degree program and teaching methodologies. Moreover, Gudivada et al. ( 2007 ) proposed a student’s learning method to work with the large datasets. they have used the Amazon Web Services API and.NET/C# application to extract a subset of the product database to enhance student learning in a relational database course.

On the other hand, authors have also evaluated the traditional teaching methods to enhance the problem-solving skills among the students(Eaglestone & Nunes, 2004 ; Wang & Chen, 2014 ; Efendiouglu & Yelken, 2010 ) Such as, Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a database design course at Sheffield University and discussed some of the issues they faced, regarding teaching, learning and assessments. Likewise, Wang and Chen ( 2014 ) summarized the problems mainly in teaching of the traditional database theory and application. According to the authors the teaching method is outdated and does not focus on the important combination of theory and practice. Moreover, Efendiouglu and Yelken ( 2010 ) investigated the effects of two different methods Programmed Instruction (PI) and Meaningful Learning (ML) on primary school teacher candidates’ academic achievements and attitudes toward computer-based education, and to define their views on these methods. The results show that PI is not favoured for teaching applications because of its behavioural structure Table 8 .

Students become creative and innovative when the try to study on their own and also from different resources rather than curriculum books only. In the modern era, there are various resources available on both online and offline platforms. Modern teaching methods must emphasize on making the students independent from the curriculum books and educate them to learn independently(Amadio et al., 2003 ; Cai & Gao, 2019 ; Martin et al., 2013 ). Also, in the work of Kawash et al. ( 2020 ) proposed he group study-based learning approach called Graded Group Activities (GGAs). In this method students team up in order to take the exam as a group. On the other hand, few studies have emphasized on course content to prepare students for the final exams such as, Zheng and Dong ( 2011 ) have discussed the issues of computer science teaching with particular focus on database systems, where different characteristics of the course, teaching content and suggestions to teach this course effectively have been presented.

As technology is evolving at rapid speed, so students need to have practical experience from the start. Basic theoretical concepts of database are important but they are of no use without its implementation in real world projects. Most of the students study in the institutes with the aim of only clearing the exams with the help of theoretical knowledge and very few students want to have practical experience(Wang & Chen, 2014 ; Zheng & Dong, 2011 ). To reduce the gap between the theory and its implementation, authors have proposed teaching methods to develop the student’s interest in the real-world projects (Naik & Gajjar, 2021 ; Svahnberg et al., 2008 ; Taipalus et al., 2018 ). Moreover, Juxiang and Zhihong ( 2012 ) have proposed that the teaching organization starts from application scenarios, and associate database theoretical knowledge with the process from analysis, modeling to establishing database application. Also, Svahnberg et al. ( 2008 ) explained that in particular conditions, there is a possibility to use students as subjects for experimental studies in DSE and influencing them by providing responses that are in line with industrial practice.

On the other hand, Nelson et al. ( 2003 ) evaluated the different teaching methods used to teach different modules of database in the School of Computing and Technology at the University of Sunder- land. They outlined suggestions for changes to the database curriculum to further integrate research and state-of-the-art systems in databases.

Database curriculum has been revisited many times in the form of guidelines that not only present the contents but also suggest approximate time to cover different topics. According to the ACM curriculum guidelines (Lunt et al., 2008 ) for the undergraduate programs in computer science, the overall coverage time for this course is 46.50 h distributed in such a way that 11 h is the total coverage time for the core topics such as, Information Models (4 core hours), Database Systems (3 core hours) and Data Modeling (4 course hours). Whereas, the remaining hours are allocated for elective topics such as Indexing, Relational Databases, Query Languages, Relational Database Design, Transaction Processing, Distributed Databases, Physical Database Design, Data Mining, Information Storage and Retrieval, Hypermedia, Multimedia Systems, and Digital Libraries(Marshall, 2012 ). While, according to the ACM curriculum guidelines ( 2013 ) for undergraduate programs in computer science, this course should be completed in 15 weeks with two and half hour lecture per week and lab session of four hours per week on average (Brady et al., 2004 ). Thus, the revised version emphasizes on the practice based learning with the help of lab component. Numerous organizations have exerted efforts in this field to classify DSE (Dietrich et al., 2008 ). DSE model curricula, bodies of knowledge (BOKs), and some standardization aspects in this field are discussed below:

Model curricula

There are standard bodies who set the curriculum guidelines for teaching undergraduate degree programs in computing disciplines. Curricula which include the guidelines to teach database are: Computer Engineering Curricula (CEC) (Meier et al., 2008 ), Information Technology Curricula (ITC) (Alrumaih, 2016 ), Computing Curriculum Software Engineering (CCSE) (Meyer, 2001 ), Cyber Security Curricula (CSC) (Brady et al., 2004 ; Bishop et al., 2017 ).

Bodies of knowledge (BOK)

A BOK includes the set of thoughts and activities related to the professional area, while in model curriculum set of guidelines are given to address the education issues (Sahami et al., 2011 ). Database body of Knowledge comprises of (a) The Data Management Body of Knowledge (DM- BOK), (b) Software Engineering Education Knowledge (SEEK) (Sobel, 2003 ) (Sobel, 2003 ), and (c) The SE body of knowledge (SWEBOK) (Swebok Evolution: IEEE Computer Society n.d. ).

Apart from the model curricula, and bodies of knowledge, there also exist some standards related to the database and its different modules: ISO/IEC 9075–1:2016 (Computing Curricula, 1991 ), ISO/IEC 10,026–1: 1998 (Suryn, 2003 ).

We also utilize advices from some studies (Elberzhager et al., 2012 ; Keele et al., 2007 ) to search for relevant papers. In order to conduct this systematic study, it is essential to formulate the primary research questions (Mushtaq et al., 2017 ). Since the data management techniques and software are evolving rapidly, the database curriculum should also be updated accordingly to meet these new requirements. Some authors have described ways of updating the content of courses to keep pace with specific developments in the field and others have developed new database curricula to keep up with the new data management techniques.

Furthermore, some authors have suggested updates for the database curriculum based on the continuously evolving technology and introduction of big data. For instance Bhogal et al. ( 2012 ) have shown that database curricula need to be updated and modernized, which can be achieved by extending the current database concepts that cover the strategies to handle the ever changing user requirements and how database technology has evolved to meet the requirements. Likewise, Picciano ( 2012 ) examines the evolving world of big data and analytics in American higher education. According to the author, the “data driven” decision making method should be used to help the institutes evaluate strategies that can improve retention and update the curriculum that has big data basic concepts and applications, since data driven decision making has already entered in the big data and learning analytic era. Furthermore, Marshall ( 2011 ) presented the challenges faced when developing a curriculum for a Computer Science degree program in the South African context that is earmarked for international recognition. According to the author, the Curricula needs to adhere both to the policy and content requirements in order to be rated as being of a particular quality.

Similarly, some studies (Abourezq & Idrissi, 2016 ; Mingyu et al., 2017 ) described big data influence from a social perspective and also proceeded with the gaps in database curriculum of computer science, especially, in the big data era and discovers the teaching improvements in practical and theoretical teaching mode, teaching content and teaching practice platform in database curriculum. Also Silva et al. ( 2016 ) propose teaching SQL as a general language that can be used in a wide range of database systems from traditional relational database management systems to big data systems.

On the other hand, different authors have developed a database curriculum based on the different academic background of students. Such as, Dean and Milani ( 1995 ) have recommended changes in computer science curricula based on the practice in United Stated Military Academy (USMA). They emphasized greatly on the practical demonstration of the topic rather than the theoretical explanation. Especially, for the non-computer science major students. Furthermore, Urban and Dietrich ( 2001 ) described the development of a second course on database systems for undergraduates, preparing students for the advanced database concepts that they will exercise in the industry. They also shared their experience with teaching the course, elaborating on the topics and assignments. Also, Andersson et al. ( 2019 ) proposed variations in core topics of database management course for the students with the engineering background. Moreover, Dietrich et al. ( 2014 ) described two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is that the educators, in diverse academic disciplines, should be able to incorporate these animations in their existing courses to meet their pedagogical needs.

The information systems have evolved into large scale distributed systems that store and process a huge amount of data across different servers, and process them using different distributed data processing frameworks. This evolution has given birth to new paradigms in database systems domain termed as NoSQL and Big Data systems, which significantly deviate from conventional relational and distributed database management systems. It is pertinent to mention that in order to offer a sustainable and practical CS education, these new paradigms and methodologies as shown in Fig.  5 should be included into database education (Kleiner, 2015 ). Tables 9 and 10 shows the summarized findings of the curriculum based reviewed studies. This section also proposed appropriate text book based on the theory, project, and practice-based teaching methodology as shown in Table 9 . The proposed books are selected purely on the bases of their usage in top universities around the world such as, Massachusetts Institute of Technology, Stanford University, Harvard University, University of Oxford, University of Cambridge and, University of Singapore and the coverage of core topics mentioned in the database curriculum.

Concepts in Database Systems Education (Kleiner, 2015 )

6.1.2 RQ.2 Evolution of DSE research

This section discusses the evolution of database while focusing the DSE over the past 25 years as shown in Fig.  6 .

Evolution of DSE studies

This study shows that there is significant increase in research in DSE after 2004 with 78% of the selected papers are published after 2004. The main reason of this outcome is that some of the papers are published in well-recognized channels like IEEE Transactions on Education, ACM Transactions on Computing Education, International Conference on Computer Science and Education (ICCSE), and Teaching, Learning and Assessment of Database (TLAD) workshop. It is also evident that several of these papers were published before 2004 and only a few articles were published during late 1990s. This is because of the fact that DSE started to gain interest after the introduction of Body of Knowledge and DSE standards. The data intensive scientific discovery has been discussed as the fourth paradigm (Hey et al., 2009 ): where the first involves empirical science and observations; second contains theoretical science and mathematically driven insights; third considers computational science and simulation driven insights; while the fourth involves data driven insights of modern scientific research.

Over the past few decades, students have gone from attending one-room class to having the world at their fingertips, and it is a great challenge for the instructors to develop the interest of students in learning database. This challenge has led to the development of the different types of interactive tools to help the instructors teach DSE in this technology oriented era. Keeping the importance of interactive tools in DSE in perspective, various authors have proposed different interactive tools over the years, such as during 1995–2003, when different authors proposed various interactive tools. Some studies (Abut & Ozturk, 1997 ; Mcintyre et al., 1995 ) introduced state of the art interactive tools to teach and enhance the collaborative learning among the students. Similarly, during 2004–2005 more interactive tools in the field of DSE were proposed such as Pahl et al. ( 2004 ), Connolly et al. ( 2005 ) introduced multimedia system based interactive model and game based collaborative learning environment.

The Internet has started to become more common in the first decade of the twenty-first century and its positive impact on the education sector was undeniable. Cost effective, student teacher peer interaction, keeping in touch with the latest information were the main reasons which made the instructors employ web-based tools to teach database in the education sector. Due to this spike in the demand of web-based tools, authors also started to introduce new instruments to assist with teaching database. In 2007 Regueras et al. ( 2007 ) proposed an e-learning tool named QUEST with a feedback module to help the students to learn from their mistakes. Similarly, in 2010, multiple authors have proposed and evaluated various web-based tools. Cvetanovic et al. ( 2010 ) proposed ADVICE with the functionality to monitor student’s progress, while, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Furthermore, Nelson and Fatimazahra ( 2010 ) evaluated different web-based tools to highlight the complexities of using these web-based instruments.

Technology has changed the teaching methods in the education sector but technology cannot replace teachers, and despite the amount of time most students spend online, virtual learning will never recreate the teacher-student bond. In the modern era, innovation in technology used in educational sectors is not meant to replace the instructors or teaching methods.

During the 1990s some studies (Dietrich & Urban, 1996 ; Urban & Dietrich, 1997 ) proposed learning and teaching methods respectively keeping the evolving technology in view. The highlight of their work was project deliverables and assignments where students progressively advanced to a step-by-step extension, from a tutorial exercise and then attempting more difficult extension of assignment.

During 2002–2007 various authors have discussed a number of teaching and learning methods to keep up the pace with the ever changing database technology, such as Connolly and Begg ( 2006 ) proposing a constructive approach to teach database analysis and design. Similarly, Prince and Felder ( 2006 ) reviewed the effectiveness of inquiry learning, problem based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. Also, McIntyre et al. (Mcintyre et al., 1995 ) brought to light the impact of convergence of European Union (EU) in different universities across Europe. They suggested a reconstruction of teaching and learning methodologies in order to effectively teach database.

During 2008–2013 more work had been done to address the different methods of teaching and learning in the field of DSE, like the work of Dominguez and Jaime ( 2010 ) who proposed an active learning approach. The focus of their study was to develop the interest of students in designing and developing databases. Also, Zheng and Dong ( 2011 ) have highlighted various characteristics of the database course and its teaching content. Similarly, Yuelan et al. ( 2011 ) have reformed database teaching methods. The main focus of their study were the Modern ways of education, project driven approach, strengthening the experimental aspects, and improving the traditional assessment method. Likewise, Al-Shuaily ( 2012 ) has explored 4 cognitive factors that can affect the learning process of database. The main focus of their study was to facilitate the students in learning SQL. Subsequently, Chen et al. ( 2012 ) also proposed scaffolding-based concept mapping strategy. This strategy helps the students to better understand database management courses. Correspondingly, Martin et al. ( 2013 ) discussed various collaborative learning techniques in the field of DSE while keeping database as an introductory course.

In the years between 2014 and 2021, research in the field of DSE increased, which was the main reason that the most of teaching, learning and assessment methods were proposed and discussed during this period. Rashid and Al-Radhy ( 2014 ) discussed the issues of traditional teaching, learning, assessing methods of database courses at different universities in Kurdistan and the main focus of their study being reformation issues, such as absence of teaching determination and contradiction between content and theory. Similarly, Wang and Chen ( 2014 ) summarized the main problems in teaching the traditional database theory and its application. Curriculum assessment mode was the main focus of their study. Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a databases design course at Sheffield University. Their focus of study included was to teach the database design module to a diverse group of students from different backgrounds. Rashid ( 2015 ) discussed some important features of database courses, whereby reforming the conventional teaching, learning, and assessing strategies of database courses at universities were the main focus of this study. Kui et al. ( 2018 ) reformed the teaching mode of database courses based on flipped classroom. Initiative learning of database courses was their main focus in this study. Similarly, Zhang et al. ( 2018 ) discussed several effective classroom teaching measures. The main focus of their study was teaching content, teaching methods, teaching evaluation and assessment methods. Cai and Gao ( 2019 ) also carried out the teaching reforms in the database course of liberal arts. Diversified teaching modes, such as flipping classroom, case oriented teaching and task oriented were the focus of their study. Teaching Kawash et al. ( 2020 ) proposed a learning approach called Graded Group Activities (GGAs). Their main focus of the study was reforming learning and assessment method.

Database course covers several topics that range from data modeling to data implementation and examination. Over the years, various authors have given their suggestions to update these topics in database curriculum to meet the requirements of modern technologies. On the other hand, authors have also proposed a new curriculum for the students of different academic backgrounds and different areas. These reformations in curriculum helped the students in their preparation, practically and theoretically, and enabled them to compete in the competitive market after graduation.

During 2003 and 2006 authors have proposed various suggestions to update and develop computer science curriculum across different universities. Robbert and Ricardo ( 2003 ) evaluated three reviews from 1999 to 2002 that were given to the groups of educators. The focus of their study was to highlight the trends that occurred in database curriculum. Also, Calero et al. ( 2003 ) proposed a first draft for this Database Body of Knowledge (DBBOK). Database (DB), Database Design (DBD), Database Administration (DBAd), Database Application (DBAp) and Advance Databases (ADVDB) were the main focus of their study. Furthermore, Conklin and Heinrichs (Conklin & Heinrichs, 2005 ) compared the content included in 13 database textbooks and the main focus of their study was IS 2002, CC2001, and CC2004 model curricula.

The years from 2007 and 2011, authors managed to developed various database curricula, like Luo et al. ( 2008 ) developed curricula in Zhejiang University City College. The aim of their study to nurture students to be qualified computer scientists. Likewise, Dietrich et al. ( 2008 ) proposed the techniques to assess the development of an advanced database course. The purpose behind the addition of an advanced database course at undergraduate level was to prepare the students to respond to industrial requirements. Also, Marshall ( 2011 ) developed a new database curriculum for Computer Science degree program in the South African context.

During 2012 and 2021 various authors suggested updates for the database curriculum such as Bhogal et al. ( 2012 ) who suggested updating and modernizing the database curriculum. Data management and data analytics were the focus of their study. Similarly, Picciano ( 2012 ) examined the curriculum in the higher level of American education. The focus of their study was big data and analytics. Also, Zhanquan et al. ( 2016 ) proposed the design for the course content and teaching methods in the classroom. Massive Open Online Courses (MOOCs) were the focus of their study. Likewise, Mingyu et al. ( 2017 ) suggested updating the database curriculum while keeping new technology concerning the database in perspective. The focus of their study was big data.

The above discussion clearly shows that the SQL is most discussed topic in the literature where more than 25% of the studies have discussed it in the previous decade as shown in Fig.  7 . It is pertinent to mention that other SQL databases such as Oracle, MS access are discussed under the SQL banner (Chen et al., 2012 ; Hou & Chen, 2010 ; Wang & Chen, 2014 ). It is mainly because of its ability to handle data in a relational database management system and direct implementation of database theoretical concepts. Also, other database topics such as transaction management, application programming etc. are also the main highlights of the topics discussed in the literature.

Evolution of Database topics discussed in literature

7 Research synthesis, advice for instructors, and way forward

This section presents the synthesized information extracted after reading and analyzing the research articles considered in this study. To this end, it firstly contextualizes the tools and methods to help the instructors find suitable tools and methods for their settings. Similarly, developments in curriculum design have also been discussed. Subsequently, general advice for instructors have been discussed. Lastly, promising future research directions for developing new tools, methods, and for revising the curriculum have also been discussed in this section.

7.1 Methods, tools, and curriculum

Methods and tools.

Web-based tools proposed by Cvetanovic et al. ( 2010 ) and Wang et al. ( 2010 ) have been quite useful, as they are growing increasingly pertinent as online mode of education is prevalent all around the globe during COVID-19. On the other hand, interactive tools and smart class room methodology has also been used successfully to develop the interest of students in database class. (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ; Canedo et al., 2021 ; Ko et al., 2021 ).

One of the most promising combination of methodology and tool has been proposed by Cvetanovic et al. ( 2010 ), whereby they developed a tool named ADVICE that helps students learn and implement database concepts while using project centric methodology, while a game based collaborative learning environment was proposed by Connolly et al. ( 2005 ) that involves a methodology comprising of modeling, articulation, feedback, and exploration. As a whole, project centric teaching (Connolly & Begg, 2006 ; Domínguez & Jaime, 2010 ) and teaching database design and problem solving skills Wang and Chen ( 2014 ), are two successful approaches for DSE. Whereas, other studies (Urban & Dietrich, 1997 ) proposed teaching methods that are more inclined towards practicing database concepts. While a topic specific approach has been proposed by Abbasi et al. ( 2016 ), Taipalus et al. ( 2018 ) and Silva et al. ( 2016 ) to teach and learn SQL. On the other hand, Cai and Gao ( 2019 ) developed a teaching method for students who do not have a computer science background. Lastly, some useful ways for defining assessments for DSE have been proposed by Kawash et al. ( 2020 ) and Zhang et al. ( 2018 ).

Curriculum of database adopted by various institutes around the world does not address how to teach the database course to the students who do not have a strong computer science background. Such as Marshall ( 2012 ), Luo et al. ( 2008 ) and Zhanquan et al. ( 2016 ) have proposed the updates in current database curriculum for the students who are not from computer science background. While Abid et al. ( 2015 ) proposed a combined course content and various methodologies that can be used for teaching database systems course. On the other hand, current database curriculum does not include the topics related to latest technologies in database domain. This factor was discussed by many other studies as well (Bhogal et al., 2012 ; Mehmood et al., 2020 ; Picciano, 2012 ).

7.2 Guidelines for instructors

The major conclusion of this study are the suggestions based on the impact and importance for instructors who are teaching DSE. Furthermore, an overview of productivity of every method can be provided by the empirical studies. These instructions are for instructors which are the focal audience of this study. These suggestions are subjective opinions after literature analysis in form of guidelines according to the authors and their meaning and purpose were maintained. According to the literature reviewed, various issues have been found in this section. Some other issues were also found, but those were not relevant to DSE. Following are some suggestions that provide interesting information:

7.2.1 Project centric and applied approach

To inculcate database development skills for the students, basic elements of database development need to be incorporated into teaching and learning at all levels including undergraduate studies (Bakar et al., 2011 ). To fulfill this objective, instructors should also improve the data quality in DSE by assigning the projects and assignments to the students where they can assess, measure and improve the data quality using already deployed databases. They should demonstrate that the quality of data is determined not only by the effective design of a database, but also through the perception of the end user (Mathieu & Khalil, 1997 )

The gap between the database course theory and industrial practice is big. Fresh graduate students find it difficult to cope up with the industrial pressure because of the contrast between what they have been taught in institutes and its application in industry (Allsopp et al., 2006 ). Involve top performers from classes in industrial projects so that they are able to acquiring sufficient knowledge and practice, especially for post graduate courses. There must be some other activities in which industry practitioners come and present the real projects and also share their industrial experiences with the students. The gap between theoretical and the practical sides of database has been identified by Myers and Skinner ( 1997 ). In order to build practical DS concepts, instructors should provide the students an accurate view of reality and proper tools.

7.2.2 Importance of software development standards and impact of DB in software success

They should have the strategies, ability and skills that can align the DSE course with the contemporary Global Software Development (GSD) (Akbar & Safdar, 2015 ; Damian et al., 2006 ).

Enable the students to explain the approaches to problem solving, development tools and methodologies. Also, the DS courses are usually taught in normal lecture format. The result of this method is that students cannot see the influence on the success or failure of projects because they do not realize the importance of DS activities.

7.2.3 Pedagogy and the use of education technology

Some studies have shown that teaching through play and practical activities helps to improve the knowledge and learning outcome of students (Dicheva et al., 2015 ).

Interactive classrooms can help the instructors to deliver their lecture in a more effective way by using virtual white board, digital textbooks, and data over network(Abut & Ozturk, 1997 ). We suggest that in order to follow the new concept of smart classroom, instructors should use the experience of Yau and Karim ( 2003 ) which benefits in cooperative learning among students and can also be adopted in DSE.

The instructors also need to update themselves with full spectrum of technology in education, in general, and for DSE, in particular. This is becoming more imperative as during COVID the world is relying strongly on the use of technology, particularly in education sector.

7.2.4 Periodic Curriculum Revision

There is also a need to revisit the existing series of courses periodically, so that they are able to offer the following benefits: (a) include the modern day database system concepts; (b) can be offered as a specialization track; (c) a specialized undergraduate degree program may also be designed.

7.3 DSE: Way forward

This research combines a significant work done on DSE at one place, thus providing a point to find better ways forward in order to improvise different possible dimensions for improving the teaching process of a database system course in future. This section discusses technology, methods, and modifications in curriculum would most impact the delivery of lectures in coming years.

Several tools have already been developed for effective teaching and learning in database systems. However, there is a great room for developing new tools. Recent rise of the notion of “serious games” is marking its success in several domains. Majority of the research work discussed in this review revolves around web-based tools. The success of serious games invites researchers to explore this new paradigm of developing useful tools for learning and practice database systems concepts.

Likewise, due to COVID-19 the world is setting up new norms, which are expected to affect the methods of teaching as well. This invites the researchers to design, develop, and test flexible tools for online teaching in a more interactive manner. At the same time, it is also imperative to devise new techniques for assessments, especially conducting online exams at massive scale. Moreover, the researchers can implement the idea of instructional design in web-based teaching in which an online classroom can be designed around the learners’ unique backgrounds and effectively delivering the concepts that are considered to be highly important by the instructors.

The teaching, learning and assessment methods discussed in this study can help the instructors to improve their methods in order to teach the database system course in a better way. It is noticed that only 16% of authors have the assessment methods as their focus of study, which clearly highlights that there is still plenty of work needed to be done in this particular domain. Assessment techniques in the database course will help the learners to learn from their mistakes. Also, instructors must realize that there is a massive gap between database theory and practice which can only be reduced with maximum practice and real world database projects.

Similarly, the technology is continuously influencing the development and expansion of modern education, whereas the instructors’ abilities to teach using online platforms are critical to the quality of online education.

In the same way, the ideas like flipped classroom in which students have to prepare the lesson prior to the class can be implemented on web-based teaching. This ensures that the class time can be used for further discussion of the lesson, share ideas and allow students to interact in a dynamic learning environment.

The increasing impact of big data systems, and data science and its anticipated impact on the job market invites the researchers to revisit the fundamental course of database systems as well. There is a need to extend the boundaries of existing contents by including the concepts related to distributed big data systems data storage, processing, and transaction management, with possible glimpse of modern tools and technologies.

As a whole, an interesting and long term extension is to establish a generic and comprehensive framework that engages all the stakeholders with the support of technology to make the teaching, learning, practicing, and assessing easier and more effective.

8 Conclusion

This SLR presents review on the research work published in the area of database system education, with particular focus on teaching the first course in database systems. The study was carried out by systematically selecting research papers published between 1995 and 2021. Based on the study, a high level categorization presents a taxonomy of the published under the heads of Tools, Methods, and Curriculum. All the selected articles were evaluated on the basis of a quality criteria. Several methods have been developed to effectively teach the database course. These methods focus on improving learning experience, improve student satisfaction, improve students’ course performance, or support the instructors. Similarly, many tools have been developed, whereby some tools are topic based, while others are general purpose tools that apply for whole course. Similarly, the curriculum development activities have also been discussed, where some guidelines provided by ACM/IEEE along with certain standards have been discussed. Apart from this, the evolution in these three areas has also been presented which shows that the researchers have been presenting many different teaching methods throughout the selected period; however, there is a decrease in research articles that address the curriculum and tools in the past five years. Besides, some guidelines for the instructors have also been shared. Also, this SLR proposes a way forward in DSE by emphasizing on the tools: that need to be developed to facilitate instructors and students especially post Covid-19 era, methods: to be adopted by the instructors to close the gap between the theory and practical, Database curricula update after the introduction of emerging technologies such as big data and data science. We also urge that the recognized publication venues for database research including VLDB, ICDM, EDBT should also consider publishing articles related to DSE. The study also highlights the importance of reviving the curricula, tools, and methodologies to cater for recent advancements in the field of database systems.

Data availability

Not Applicable.

Code availability

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Ishaq, M., Abid, A., Farooq, M.S. et al. Advances in database systems education: Methods, tools, curricula, and way forward. Educ Inf Technol 28 , 2681–2725 (2023). https://doi.org/10.1007/s10639-022-11293-0

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Advances in database systems education: Methods, tools, curricula, and way forward

Muhammad ishaq.

1 Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan

2 Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan

3 Department of Computer Science, University of Management and Technology, Lahore, Pakistan

Muhammad Shoaib Farooq

Muhammad faraz manzoor.

4 Department of Computer Science, Lahore Garrison University, Lahore, Pakistan

Uzma Farooq

Kamran abid.

5 Department of Electrical Engineering, University of the Punjab, Lahore, Pakistan

Mamoun Abu Helou

6 Faculty of Information Technology, Al Istiqlal University, Jericho, Palestine

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Not Applicable.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students’ interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.

Introduction

Database systems play a pivotal role in the successful implementation of the information systems to ensure the smooth running of many different organizations and companies (Etemad & Küpçü, 2018 ; Morien, 2006 ). Therefore, at least one course about the fundamentals of database systems is taught in every computing and information systems degree (Nagataki et al., 2013 ). Database System Education (DSE) is concerned with different aspects of data management while developing software (Park et al., 2017 ). The IEEE/ACM computing curricula guidelines endorse 30–50 dedicated hours for teaching fundamentals of design and implementation of database systems so as to build a very strong theoretical and practical understanding of the DSE topics (Cvetanovic et al., 2010 ).

Practically, most of the universities offer one user-oriented course at undergraduate level that covers topics related to the data modeling and design, querying, and a limited number of hours on theory (Conklin & Heinrichs, 2005 ; Robbert & Ricardo, 2003 ), where it is often debatable whether to utilize a design-first or query-first approach. Furthermore, in order to update the course contents, some recent trends, including big data and the notion of NoSQL should also be introduced in this basic course (Dietrich et al., 2008 ; Garcia-Molina, 2008 ). Whereas, the graduate course is more theoretical and includes topics related to DB architecture, transactions, concurrency, reliability, distribution, parallelism, replication, query optimization, along with some specialized classes.

Researchers have designed a variety of tools for making different concepts of introductory database course more interesting and easier to teach and learn interactively (Brusilovsky et al., 2010 ) either using visual support (Nagataki et al., 2013 ), or with the help of gamification (Fisher & Khine, 2006 ). Similarly, the instructors have been improvising different methods to teach (Abid et al., 2015 ; Domínguez & Jaime, 2010 ) and evaluate (Kawash et al., 2020 ) this theoretical and practical course. Also, the emerging and hot topics such as cloud computing and big data has also created the need to revise the curriculum and methods to teach DSE (Manzoor et al., 2020 ).

The research in database systems education has evolved over the years with respect to modern contents influenced by technological advancements, supportive tools to engage the learners for better learning, and improvisations in teaching and assessment methods. Particularly, in recent years there is a shift from self-describing data-driven systems to a problem-driven paradigm that is the bottom-up approach where data exists before being designed. This mainly relies on scientific, quantitative, and empirical methods for building models, while pushing the boundaries of typical data management by involving mathematics, statistics, data mining, and machine learning, thus opening a multidisciplinary perspective. Hence, it is important to devote a few lectures to introducing the relevance of such advance topics.

Researchers have provided useful review articles on other areas including Introductory Programming Language (Mehmood et al., 2020 ), use of gamification (Obaid et al., 2020 ), research trends in the use of enterprise service bus (Aziz et al., 2020 ), and the role of IoT in agriculture (Farooq et al., 2019 , 2020 ) However, to the best of our knowledge, no such study was found in the area of database systems education. Therefore, this study discusses research work published in different areas of database systems education involving curricula, tools, and approaches that have been proposed to teach an introductory course on database systems in an effective manner. The rest of the article has been structured in the following manner: Sect.  2 presents related work and provides a comparison of the related surveys with this study. Section  3 presents the research methodology for this study. Section  4 analyses the major findings of the literature reviewed in this research and categorizes it into different important aspects. Section  5 represents advices for the instructors and future directions. Lastly, Sect.  6 concludes the article.

Related work

Systematic Literature Reviews have been found to be a very useful artifact for covering and understanding a domain. A number of interesting review studies have been found in different fields (Farooq et al., 2021 ; Ishaq et al., 2021 ). Review articles are generally categorized into narrative or traditional reviews (Abid et al., 2016 ; Ramzan et al., 2019 ), systematic literature review (Naeem et al., 2020 ) and meta reviews or mapping study (Aria & Cuccurullo, 2017 ; Cobo et al., 2012 ; Tehseen et al., 2020 ). This study presents a systematic literature review on database system education.

The database systems education has been discussed from many different perspectives which include teaching and learning methods, curriculum development, and the facilitation of instructors and students by developing different tools. For instance, a number of research articles have been published focusing on developing tools for teaching database systems course (Abut & Ozturk, 1997 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Furthermore, few authors have evaluated the DSE tools by conducting surveys and performing empirical experiments so as to gauge the effectiveness of these tools and their degree of acceptance among important stakeholders, teachers and students (Brusilovsky et al., 2010 ; Nelson & Fatimazahra, 2010 ). On the other hand, some case studies have also been discussed to evaluate the effectiveness of the improvised approaches and developed tools. For example, Regueras et al. ( 2007 ) presented a case study using the QUEST system, in which e-learning strategies are used to teach the database course at undergraduate level, while, Myers and Skinner ( 1997 ) identified the conflicts that arise when theories in text books regarding the development of databases do not work on specific applications.

Another important facet of DSE research focuses on the curriculum design and evolution for database systems, whereby (Alrumaih, 2016 ; Bhogal et al., 2012 ; Cvetanovic et al., 2010 ; Sahami et al., 2011 ) have proposed solutions for improvements in database curriculum for the better understanding of DSE among the students, while also keeping the evolving technology into the perspective. Similarly, Mingyu et al. ( 2017 ) have shared their experience in reforming the DSE curriculum by adding topics related to Big Data. A few authors have also developed and evaluated different tools to help the instructors teaching DSE.

There are further studies which focus on different aspects including specialized tools for specific topics in DSE (Mcintyre et al, 1995 ; Nelson & Fatimazahra, 2010 ). For instance, Mcintyre et al. ( 1995 ) conducted a survey about using state of the art software tools to teach advanced relational database design courses at Cleveland State University. However, the authors did not discuss the DSE curricula and pedagogy in their study. Similarly, a review has been conducted by Nelson and Fatimazahra ( 2010 ) to highlight the fact that the understanding of basic knowledge of database is important for students of the computer science domain as well as those belonging to other domains. They highlighted the issues encountered while teaching the database course in universities and suggested the instructors investigate these difficulties so as to make this course more effective for the students. Although authors have discussed and analyzed the tools to teach database, the tools are yet to be categorized according to different methods and research types within DSE. There also exists an interesting systematic mapping study by Taipalus and Seppänen ( 2020 ) that focuses on teaching SQL which is a specific topic of DSE. Whereby, they categorized the selected primary studies into six categories based on their research types. They utilized directed content analysis, such as, student errors in query formulation, characteristics and presentation of the exercise database, specific or non-specific teaching approach suggestions, patterns and visualization, and easing teacher workload.

Another relevant study that focuses on collaborative learning techniques to teach the database course has been conducted by Martin et al. ( 2013 ) This research discusses collaborative learning techniques and adapted it for the introductory database course at the Barcelona School of Informatics. The motive of the authors was to introduce active learning methods to improve learning and encourage the acquisition of competence. However, the focus of the study was only on a few methods for teaching the course of database systems, while other important perspectives, including database curricula, and tools for teaching DSE were not discussed in this study.

The above discussion shows that a considerable amount of research work has been conducted in the field of DSE to propose various teaching methods; develop and test different supportive tools, techniques, and strategies; and to improve the curricula for DSE. However, to the best of our knowledge, there is no study that puts all these relevant and pertinent aspects together while also classifying and discussing the supporting methods, and techniques. This review is considerably different from previous studies. Table ​ Table1 1 highlights the differences between this study and other relevant studies in the field of DSE using ✓ and – symbol reflecting "included" and "not included" respectively. Therefore, this study aims to conduct a systematic mapping study on DSE that focuses on compiling, classifying, and discussing the existing work related to pedagogy, supporting tools, and curricula.

Comparison with other related research articles

Study	(Mcintyre et al., )	(Myers & Skinner, )	(Beecham et al., )	(Dietrich et al., )	(Regueras et al., )	(Nelson & Fatimazahra, )	(Martin et al., )	(Abbasi et al., )	(Luxton-Reilly et al., )	(Taipalus & Seppänen, )	This article
Focus	Database	Database	Software Engineering	Database	Database	Database	Database	OOP	Programming	Data Base	Database System
Research Types Classifications	–	–	–	–	–	–	–	–	–
Teaching Methods	–		–	–	–	–		–
Tools to aid teaching		-					–
Curricula considered	–	–	–		–	–		–		–
Evolution	–	–	–	–	–	–	–	–	–	–
Year	1995	1997	2008	2008	2009	2015	2013	2017	2018	2020	2022

Research methodology

In order to preserve the principal aim of this study, which is to review the research conducted in the area of database systems education, a piece of advice has been collected from existing methods described in various studies (Elberzhager et al., 2012 ; Keele et al., 2007 ; Mushtaq et al., 2017 ) to search for the relevant papers. Thus, proper research objectives were formulated, and based on them appropriate research questions and search strategy were formulated as shown in Fig.  1 .

Research objectives

The Following are the research objectives of this study:

• i. To find high quality research work in DSE.
• ii. To categorize different aspects of DSE covered by other researchers in the field.
• iii. To provide a thorough discussion of the existing work in this study to provide useful information in the form of evolution, teaching guidelines, and future research directions of the instructors.

Research questions

In order to fulfill the research objectives, some relevant research questions have been formulated. These questions along with their motivations have been presented in Table ​ Table2 2 .

Study selection results

No	Research questions	Motivations
RQ1	What are the developments in DSE with respect to tools, methods, and curriculum?	- Identify focal areas of research in DSE - Discuss the work done in each area
RQ2	How the research in DSE evolved in past 25 years?	- Discuss the focus of research in different time spans while mapping it onto the technological advancement

Search strategy

The Following search string used to find relevant articles to conduct this study. “Database” AND (“System” OR “Management”) AND (“Education*” OR “Train*” OR “Tech*” OR “Learn*” OR “Guide*” OR “Curricul*”).

Articles have been taken from different sources i.e. IEEE, Springer, ACM, Science Direct and other well-known journals and conferences such as Wiley Online Library, PLOS and ArXiv. The planning for search to find the primary study in the field of DSE is a vital task.

Study selection

A total of 29,370 initial studies were found. These articles went through a selection process, and two authors were designated to shortlist the articles based on the defined inclusion criteria as shown in Fig.  2 . Their conflicts were resolved by involving a third author; while the inclusion/exclusion criteria were also refined after resolving the conflicts as shown in Table ​ Table3. 3 . Cohen’s Kappa coefficient 0.89 was observed between the two authors who selected the articles, which reflects almost perfect agreement between them (Landis & Koch, 1977 ). While, the number of papers in different stages of the selection process for all involved portals has been presented in Table ​ Table4 4 .

Selection criteria

IC	Inclusion criteria
IC 1	The study related to the database and education
IC 2	The years of research publication must be from 1995 to 2022
IC 3	Only full length papers are included
IC 4	Research papers written in English language are included
EC	Exclusion criteria
EC1	Incomplete papers, i.e., presentation, posters or essay
EC2	Research articles without abstract
EC3	Research articles other than English language
EC4	Papers that do not include education as their primary focus

Phase	Process	Selection stage	IEEE	Springer	ACM	Elsevier	Others	Total
1	Search	Search string	500	5312	10,802	5696	7045	29,370
2	Screening	Title	153	121	115	133	87	609
3	Screening	Abstract	45	23	29	21	40	158
4	Screening	Full text	10	1	20	2	37	70

Title based search: Papers that are irrelevant based on their title are manually excluded in the first stage. At this stage, there was a large portion of irrelevant papers. Only 609 papers remained after this stage.

Abstract based search: At this stage, abstracts of the selected papers in the previous stage are studied and the papers are categorized for the analysis along with research approach. After this stage only 152 papers were left.

Full text based analysis: Empirical quality of the selected articles in the previous stage is evaluated at this stage. The analysis of full text of the article has been conducted. The total of 70 papers were extracted from 152 papers for primary study. Following questions are defined for the conduction of final data extraction.

Quality assessment criteria

Following are the criteria used to assess the quality of the selected primary studies. This quality assessment was conducted by two authors as explained above.

• The study focuses on curricula, tools, approach, or assessments in DSE, the possible answers were Yes (1), No (0)
• The study presents a solution to the problem in DSE, the possible answers to this question were Yes (1), Partially (0.5), No (0)
• The study focuses on empirical results, Yes (1), No (0)

Score pattern of publication channels

Channel type	Quartile number	Score
Journal Quartile Ranking	Q1	2
	Q2	1.5
	Q3	1
	Q4	0.5
	Other	0
Conference/Workshop/ Symposium/Core Ranking	Core A	1.5
	Core B	1
	Core C	0.5
	Other	0

Almost 50.00% of papers had scored more than average and 33.33% of papers had scored between the average range i.e., 2.50–3.50. Some articles with the score below 2.50 have also been included in this study as they present some useful information and were published in education-based journals. Also, these studies discuss important demography and technology based aspects that are directly related to DSE.

Threats to validity

The validity of this study could be influenced by the following factors during the literature of this publication.

Construct validity

In this study this validity identifies the primary study for research (Elberzhager et al., 2012 ). To ensure that many primary studies have been included in this literature two authors have proposed possible search keywords in multiple repetitions. Search string is comprised of different terms related to DS and education. Though, list might be incomplete, count of final papers found can be changed by the alternative terms (Ampatzoglou et al., 2013 ). IEEE digital library, Science direct, ACM digital library, Wiley Online Library, PLOS, ArXiv and Google scholar are the main libraries where search is done. We believe according to the statistics of search engines of literature the most research can be found on these digital libraries (Garousi et al., 2013 ). Researchers also searched related papers in main DS research sites (VLDB, ICDM, EDBT) in order to minimize the risk of missing important publication.

Including the papers that does not belong to top journals or conferences may reduce the quality of primary studies in this research but it indicates that the representativeness of the primary studies is improved. However, certain papers which were not from the top publication sources are included because of their relativeness wisth the literature, even though they reduce the average score for primary studies. It also reduces the possibility of alteration of results which might have caused by the improper handling of duplicate papers. Some cases of duplications were found which were inspected later whether they were the same study or not. The two authors who have conducted the search has taken the final decision to the select the papers. If there is no agreement between then there must be discussion until an agreement is reached.

Internal validity

This validity deals with extraction and data analysis (Elberzhager et al., 2012 ). Two authors carried out the data extraction and primary studies classification. While the conflicts between them were resolved by involving a third author. The Kappa coefficient was 0.89, according to Landis and Koch ( 1977 ), this value indicates almost perfect level of agreement between the authors that reduces this threat significantly.

Conclusion validity

This threat deals with the identification of improper results which may cause the improper conclusions. In this case this threat deals with the factors like missing studies and wrong data extraction (Ampatzoglou et al., 2013 ). The objective of this is to limit these factors so that other authors can perform study and produce the proper conclusions (Elberzhager et al., 2012 ).

Interpretation of results might be affected by the selection and classification of primary studies and analyzing the selected study. Previous section has clearly described each step performed in primary study selection and data extraction activity to minimize this threat. The traceability between the result and data extracted was supported through the different charts. In our point of view, slight difference based on the publication selection and misclassification would not alter the main results.

External validity

This threat deals with the simplification of this research (Mateo et al., 2012 ). The results of this study were only considered that related to the DSE filed and validation of the conclusions extracted from this study only concerns the DSE context. The selected study representativeness was not affected because there was no restriction on time to find the published research. Therefore, this external validity threat is not valid in the context of this research. DS researchers can take search string and the paper classification scheme represented in this study as an initial point and more papers can be searched and categorized according to this scheme.

Analysis of compiled research articles

This section presents the analysis of the compiled research articles carefully selected for this study. It presents the findings with respect to the research questions described in Table ​ Table2 2 .

Selection results

A total of 70 papers were identified and analyzed for the answers of RQs described above. Table ​ Table6 6 represents a list of the nominated papers with detail of the classification results and their quality assessment scores.

Classification and quality assessment of selected articles

Ref	Channel	Year	Research Type	a	b	c	d	Total
Tools				Quality Assessment
(Mcintyre et al., )	Journal	1995	Review	1	1	0	2	4
(Abut & Ozturk, )	Conference	1997	Experiment	1	1	0	0	2
(Yau & Karim, )	Conference	2003	Experiment	1	0.5	0	1	2.5
(Pahl et al., )	Journal	2004	Experiment	1	1	0	0	2
(Connolly et al., )	Conference	2005	Experiment	1	0.5	1	1	3.5
(Regueras et al., )	Conference	2007	Case Study	1	1	1	0	3
(Sciore, )	Symposium	2007	Case Study	1	0	1	1.5	3.5
(Holliday & Wang, )	Conference	2009	Experiment	1	0.5	1	0.5	3
(Brusilovsky et al., )	Journal	2010	Experiment	1	1	1	2	5
(Cvetanovic et al., )	Journal	2010	Experiment	1	1	0	2	4
(Nelson & Fatimazahra, )	Journal	2010	Review	1	1	0	1	3
(Wang et al., )	Conference	2010	Experiment	1	1	0	1.5	3.5
(Nagataki et al., )	Journal	2013	Experiment	0	1	1	2	4
(Yue, )	Journal	2013	Experiment	1	1	1	1.5	4.5
(Abelló Gamazo et al., )	Journal	2016	Experiment	1	1	1	2	5
(Taipalus & Perälä, )	Symposium	2019	Review	1	1	1	1.5	4.5
Methods				Quality Assessment
(Dietrich & Urban, )	Conference	1996	Review	1	1	0	1.5	3.5
(Urban & Dietrich, )	Journal	1997	Experiment	1	1	0	0	2
(Nelson et al., )	Workshop	2003	Review	1	1	0	0	2
(Amadio, )	Conference	2003	Experiment	1	0.5	1	0.5	3
(Connolly & Begg, )	Journal	2006	Experiment	1	1	0	2	4
(Morien, )	Journal	2006	Experiment	1	0.5	1	2	4.5
(Prince & Felder, )	Journal	2006	Review	0	0.5	0	2	2.5
(Martinez-González & Duffing, )	Journal	2007	Review	1	1	0	2	4
(Gudivada et al., )	Conference	2007	Review	1	0.5	0	0	1.5
(Svahnberg et al., )	Symposium	2008	Review	1	0	0	1.5	2.5
(Brusilovsky et al., )	Conference	2008	Experiment	1	0.5	1	1.5	4
(Dominguez & Jaime, )	Journal	2010	Experiment	1	1	1	2	5
(Efendiouglu & Yelken )	Journal	2010	Experiment	1	1	1	0	3
(Hou & Chen, )	Conference	2010	Review	1	0.5	1	0	2.5
(Yuelan et al., )	Conference	2011	Experiment	1	0.5	0	0	1.5
(Zheng & Dong, )	Conference	2011	Review	1	1	0	1	3
(Al-Shuaily, )	Workshop	2012	Review	1	1	1	0	3
(Juxiang & Zhihong, )	Conference	2012	Review	1	0.5	0	0	1.5
(Chen et al., )	Journal	2012	Review	1	1	1	2	5
(Martin et al., )	Journal	2013	Review	1	1	1	2	5
(Rashid & Al-Radhy, )	conference	2014	Review	1	0.5	1	0	2.5
(Wang & Chen, )	Conference	2014	Experiment	1	0	1	0	2
(Dicheva et al., )	Journal	2015	Review	1	1	0	1	3
(Rashid, )	Journal	2015	Review	1	0.5	1	2	4.5
(Etemad & Küpçü, )	Journal	2018	Experiment	0	0.5	1	2	3.5
(Kui et al., )	Conference	2018	Experiment	1	1	0	1	3
(Taipalus et al., )	Journal	2018	Review	1	1	0	2	4
(Zhang et al., )	conference	2018	Experiment	1	1	1	0	3
(Shebaro, )	Journal	2018	Review	1	0.5	1	0	2.5
(Cai & Gao, )	Conference	2019	Review	1	1	0	0	2
(Kawash et al., )	Symposium	2020	Experiment	1	1	1	1.5	4.5
(Taipalus & Seppänen, )	Journal	2020	Review	1	1	1	2	5
(Canedo et al., )	Journal	2021	Experiment	1	1	1	1	4
(Naik & Gajjar, )	Journal	2021	Case Study	1	1	1	0	3
(Ko et al., )	Journal	2021	Review	1	1	1	2	5
(Sibia et al., )	Workshop	2022	Case Study	1	1	1	0	3
Curriculum				Quality Assessment
(Dean & Milani, )	Conference	1995	Experiment	1	0.5	1	0.5	3
(Urban & Dietrich, )	Symposium	2001	Case Study	1	0	1	1.5	3.5
(Calero et al., )	Journal	2003	Review	1	1	0	2	4
(Robbert & Ricardo, )	Conference	2003	Review	1	1	0	1.5	3.5
(Adams et al., )	Journal	2004	Experiment	1	1	0	0	2
(Conklin & Heinrichs, )	Journal	2005	Review	1	1	1	0	3
(Dietrich et al., )	Journal	2008	Case Study	0	1	1	2	4
(Luo et al., )	Conference	2008	Experiment	1	1	1	0	3
(Marshall, )	Conference	2011	Review	1	1	1	0	3
(Bhogal et al., )	Workshop	2012	Case Study	1	1	0	0	2
(Picciano, )	Journal	2012	Review	1	1	0	0	2
(Abid et al., )	Journal	2015	Review	1	1	1	1	4
(Taipalus & Seppänen, )	Journal	2015	Experiment	1	1	1	2	5
(Abourezq & Idrissi, )	Journal	2016	Experiment	1	1	0	0.5	2.5
(Silva et al., )	Conference	2016	Experiment	1	1	0	1.5	3.5
(Zhanquan et al., )	Journal	2016	Review	1	1	1	0	3
(Mingyu et al., )	Conference	2017	Experiment	1	1	1	0	3
(Andersson et al., )	Conference	2019	Review	1	0.5	0	0	1.5

RQ1.Categorization of research work in DSE field

The analysis in this study reveals that the literature can be categorized as: Tools: any additional application that helps instructors in teaching and students in learning. Methods: any improvisation aimed at improving pedagogy or cognition. Curriculum: refers to the course content domains and their relative importance in a degree program, as shown in Fig.  3 .

Taxonomy of DSE study types

Most of the articles provide a solution by gathering the data and also prove the novelty of their research through results. These papers are categorized as experiments w.r.t. their research types. Whereas, some of them case study papers which are used to generate an in depth, multifaceted understanding of a complex issue in its real-life context, while few others are review studies analyzing the previously used approaches. On the other hand, a majority of included articles have evaluated their results with the help of experiments, while others conducted reviews to establish an opinion as shown in Fig.  4 .

Cross Mapping of DSE study type and research Types

Educational tools, especially those related to technology, are making their place in market faster than ever before (Calderon et al., 2011 ). The transition to active learning approaches, with the learner more engaged in the process rather than passively taking in information, necessitates a variety of tools to help ensure success. As with most educational initiatives, time should be taken to consider the goals of the activity, the type of learners, and the tools needed to meet the goals. Constant reassessment of tools is important to discover innovation and reforms that improve teaching and learning (Irby & Wilkerson, 2003 ). For this purpose, various type of educational tools such as, interactive, web-based and game based have been introduced to aid the instructors in order to explain the topic in more effective way.

The inclusion of technology into the classroom may help learners to compete in the competitive market when approaching the start of their career. It is important for the instructors to acknowledge that the students are more interested in using technology to learn database course instead of merely being taught traditional theory, project, and practice-based methods of teaching (Adams et al., 2004 ). Keeping these aspects in view many authors have done significant research which includes web-based and interactive tools to help the learners gain better understanding of basic database concepts.

Great research has been conducted with the focus of students learning. In this study we have discussed the students learning supportive with two major finding’s objectives i.e., tools which prove to be more helpful than other tools. Whereas, proposed tools with same outcome as traditional classroom environment. Such as, Abut and Ozturk ( 1997 ) proposed an interactive classroom environment to conduct database classes. The online tools such as electronic “Whiteboard”, electronic textbooks, advance telecommunication networks and few other resources such as Matlab and World Wide Web were the main highlights of their proposed smart classroom. Also, Pahl et al. ( 2004 ) presented an interactive multimedia-based system for the knowledge and skill oriented Web-based education of database course students. The authors had differentiated their proposed classroom environment from traditional classroom-based approach by using tool mediated independent learning and training in an authentic setting. On the other hand, some authors have also evaluated the educational tools based on their usage and impact on students’ learning. For example, Brusilovsky et al. ( 2010 )s evaluated the technical and conceptual difficulties of using several interactive educational tools in the context of a single course. A combined Exploratorium has been presented for database courses and an experimental platform, which delivers modified access to numerous types of interactive learning activities.

Also, Taipalus and Perälä ( 2019 ) investigated the types of errors that are persistent in writing SQL by the students. The authors also contemplated the errors while mapping them onto different query concepts. Moreover, Abelló Gamazo et al. ( 2016 ) presented a software tool for the e-assessment of relational database skills named LearnSQL. The proposed software allows the automatic and efficient e-learning and e-assessment of relational database skills. Apart from these, Yue ( 2013 ) proposed the database tool named Sakila as a unified platform to support instructions and multiple assignments of a graduate database course for five semesters. According to this study, students find this tool more useful and interesting than the highly simplified databases developed by the instructor, or obtained from textbook. On the other hand, authors have proposed tools with the main objective to help the student’s grip on the topic by addressing the pedagogical problems in using the educational tools. Connolly et al. ( 2005 ) discussed some of the pedagogical problems sustaining the development of a constructive learning environment using problem-based learning, a simulation game and interactive visualizations to help teach database analysis and design. Also, Yau and Karim ( 2003 ) proposed smart classroom with prevalent computing technology which will facilitate collaborative learning among the learners. The major aim of this smart classroom is to improve the quality of interaction between the instructors and students during lecture.

Student satisfaction is also an important factor for the educational tools to more effective. While it supports in students learning process it should also be flexible to achieve the student’s confidence by making it as per student’s needs (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ). Also, Cvetanovic et al. ( 2010 ) has proposed a web-based educational system named ADVICE. The proposed solution helps the students to reduce the gap between DBMS, theory and its practice. On the other hand, authors have enhanced the already existing educational tools in the traditional classroom environment to addressed the student’s concerns (Nelson & Fatimazahra, 2010 ; Regueras et al., 2007 ) Table ​ Table7 7 .

Tools: Adopted in DSE and their impacts

Objective	Findings	References	Target Topic/ exposition platform
Support of Students’ learning	More supportive	• (Abut & Ozturk, )	• Data models and data modelling principles • IDLE (the Interactive Database Learning Environment)
		• (Pahl et al., )	• Data models • IDLE
		• (Brusilovsky et al., )	• SQL • SQL-Knot, SQL-Lab
		• Conceptual database design, Logical database design, Physical database design • Online games	• SQL • Interactive
		• (Abbasi et al., )	• Relational Database • LearnSQL
		• (Yue, )	• Relational Calculus, XML generation, XPath, and XQuery • Sakila
		• (Nelson & Fatimazahra, )	• Introductory Database topics • TLAD
	Same as others	• (Connolly et al., )	• Conceptual database design, Logical database design, Physical database design • Online games
		• (Yau & Karim, )	• Introductory Database topics • RCSM
Students’ Satisfaction	Satisfied	• (Brusilovsky et al., )	• SQL • SQL-Knot, SQL-Lab
		• (Cvetanovic et al., )	• SQL, formal query languages, and normalization • ADVICE
		• (Connolly et al., )
		• (Pahl et al., )	• Data models • IDLE
	Similar satisfaction as compared to traditional classroom environment	• (Nelson & Fatimazahra, )	• Introductory Database topics • TLAD
		• (Regueras et al., )	• Entity Relationship Model • QUEST
Students’ motivation towards database development	Same impact as other approaches	• (Nagataki et al., )	• SQL • sAccess
	Helped students to develop better database development strategies	• (Brusilovsky et al., )	• SQL • SQL-Knot, SQL-Lab
		• (Mcintyre et al., )	• Relational Database Design • Expert IT system
Students’ course performance	Better performance	• (Cvetanovic et al., )	• SQL, formal query languages, and normalization • ADVICE
		• (Wang et al., )	• Entity Relationship Model, SQL • MeTube
		• (Holliday & Wang, )	• MySQL • MeTube
		• (Taipalus & Perälä, )	• SQL • Interactive
	Same performance as other approaches	• (Pahl et al., )	• Data models • IDLE
		• (Yue, )	• Relational Calculus, XML generation, XPath, and XQuery • Sakila
Student and instructor interaction percentage	Increased	• (Abut & Ozturk, )	• Introductory Database topics • “Whiteboard”
		• (Yau & Karim, )	• Introductory Database topics • RCSM
		• (Taipalus & Perälä, )	• SQL • Interactive

Hands on database development is the main concern in most of the institute as well as in industry. However, tools assisting the students in database development and query writing is still major concern especially in SQL (Brusilovsky et al., 2010 ; Nagataki et al., 2013 ).

Student’s grades reflect their conceptual clarity and database development skills. They are also important to secure jobs and scholarships after passing out, which is why it is important to have the educational learning tools to help the students to perform well in the exams (Cvetanovic et al., 2010 ; Taipalus et al., 2018 ). While, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Subsequently, existing educational tools needs to be upgraded or replaced by the more suitable assessment oriented interactive tools to attend challenging students needs (Pahl et al., 2004 ; Yuelan et al., 2011 ).

One other objective of developing the educational tools is to increase the interaction between the students and the instructors. In the modern era, almost every institute follows the student centered learning(SCL). In SCL the interaction between students and instructor increases with most of the interaction involves from the students. In order to support SCL the educational based interactive and web-based tools need to assign more roles to students than the instructors (Abbasi et al., 2016 ; Taipalus & Perälä, 2019 ; Yau & Karim, 2003 ).

Theory versus practice is still one of the main issues in DSE teaching methods. The traditional teaching method supports theory first and then the concepts learned in the theoretical lectures implemented in the lab. Whereas, others think that it is better to start by teaching how to write query, which should be followed by teaching the design principles for database, while a limited amount of credit hours are also allocated for the general database theory topics. This part of the article discusses different trends of teaching and learning style along with curriculum and assessments methods discussed in DSE literature.

A variety of teaching methods have been designed, experimented, and evaluated by different researchers (Yuelan et al., 2011 ; Chen et al., 2012 ; Connolly & Begg, 2006 ). Some authors have reformed teaching methods based on the requirements of modern way of delivering lectures such as Yuelan et al. ( 2011 ) reform teaching method by using various approaches e.g. a) Modern ways of education: includes multimedia sound, animation, and simulating the process and working of database systems to motivate and inspire the students. b) Project driven approach: aims to make the students familiar with system operations by implementing a project. c) Strengthening the experimental aspects: to help the students get a strong grip on the basic knowledge of database and also enable them to adopt a self-learning ability. d) Improving the traditional assessment method: the students should turn in their research and development work as the content of the exam, so that they can solve their problem on their own.

The main aim of any teaching method is to make student learn the subject effectively. Student must show interest in order to gain something from the lectures delivered by the instructors. For this, teaching methods should be interactive and interesting enough to develop the interest of the students in the subject. Students can show interest in the subject by asking more relative questions or completing the home task and assignments on time. Authors have proposed few teaching methods to make topic more interesting such as, Chen et al. ( 2012 ) proposed a scaffold concept mapping strategy, which considers a student’s prior knowledge, and provides flexible learning aids (scaffolding and fading) for reading and drawing concept maps. Also, Connolly & Begg (200s6) examined different problems in database analysis and design teaching, and proposed a teaching approach driven by principles found in the constructivist epistemology to overcome these problems. This constructivist approach is based on the cognitive apprenticeship model and project-based learning. Similarly, Domínguez & Jaime ( 2010 ) proposed an active method for database design through practical tasks development in a face-to-face course. They analyzed results of five academic years using quasi experimental. The first three years a traditional strategy was followed and a course management system was used as material repository. On the other hand, Dietrich and Urban ( 1996 ) have described the use of cooperative group learning concepts in support of an undergraduate database management course. They have designed the project deliverables in such a way that students develop skills for database implementation. Similarly, Zhang et al. ( 2018 ) have discussed several effective classroom teaching measures from the aspects of the innovation of teaching content, teaching methods, teaching evaluation and assessment methods. They have practiced the various teaching measures by implementing the database technologies and applications in Qinghai University. Moreover, Hou and Chen ( 2010 ) proposed a new teaching method based on blending learning theory, which merges traditional and constructivist methods. They adopted the method by applying the blending learning theory on Access Database programming course teaching.

Problem solving skills is a key aspect to any type of learning at any age. Student must possess this skill to tackle the hurdles in institute and also in industry. Create mind and innovative students find various and unique ways to solve the daily task which is why they are more likeable to secure good grades and jobs. Authors have been working to introduce teaching methods to develop problem solving skills in the students(Al-Shuaily, 2012 ; Cai & Gao, 2019 ; Martinez-González & Duffing, 2007 ; Gudivada et al., 2007 ). For instance, Al-Shuaily ( 2012 ) has explored four cognitive factors such as i) Novices’ ability in understanding, ii) Novices’ ability to translate, iii) Novice’s ability to write, iv) Novices’ skills that might influence SQL teaching, and learning methods and approaches. Also, Cai and Gao ( 2019 ) have reformed the teaching method in the database course of two higher education institutes in China. Skills and knowledge, innovation ability, and data abstraction were the main objective of their study. Similarly, Martinez-González and Duffing ( 2007 ) analyzed the impact of convergence of European Union (EU) in different universities across Europe. According to their study, these institutes need to restructure their degree program and teaching methodologies. Moreover, Gudivada et al. ( 2007 ) proposed a student’s learning method to work with the large datasets. they have used the Amazon Web Services API and.NET/C# application to extract a subset of the product database to enhance student learning in a relational database course.

On the other hand, authors have also evaluated the traditional teaching methods to enhance the problem-solving skills among the students(Eaglestone & Nunes, 2004 ; Wang & Chen, 2014 ; Efendiouglu & Yelken, 2010 ) Such as, Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a database design course at Sheffield University and discussed some of the issues they faced, regarding teaching, learning and assessments. Likewise, Wang and Chen ( 2014 ) summarized the problems mainly in teaching of the traditional database theory and application. According to the authors the teaching method is outdated and does not focus on the important combination of theory and practice. Moreover, Efendiouglu and Yelken ( 2010 ) investigated the effects of two different methods Programmed Instruction (PI) and Meaningful Learning (ML) on primary school teacher candidates’ academic achievements and attitudes toward computer-based education, and to define their views on these methods. The results show that PI is not favoured for teaching applications because of its behavioural structure Table ​ Table8 8 .

Methods: Teaching approaches adopted in DSE

Objective	Findings	References	Target Topic/ Approach or Method
Develop interest in Subject	Students begin to ask more relative questions	• (Chen et al., )	• Data modeling, relational databases, database query languages • Scaffolded Concept
		• (Connolly & Begg, )	• Database concepts, Database Analysis and Design, Implementation • Constructivist-Based Approach
		• (Dominguez & Jaime, )	• Database design • Project-based learning
		• (Rashid & Al-Radhy, )	• Database Analysis and Design • Project based learning, Assessment based learning
		• (Yuelan et al., )	• Principles of Database, SQL Server • Project-driven approach
		• (Taipalus & Seppänen, )	• SQL • Group learning and projects
		• (Brusilovsky et al., )	• SQL • SQL Exploratorium
		• (Hou & Chen, )	• Access • Blending Learning
	Same effect as others traditional teaching methods	• (Dietrich & Urban, )	• ER Model, Relational Design, SQL • Teaching and learning strategies
		• (Kui et al., )	• E-R model, relational model, SQL • Flipped Classroom
		• (Rashid, )	• Entity Relational Database, Relational Algebra, Normalization, • Learning and Assessment Methods
		• (Zhang et al., )	• Data Models, Physical Data Design • Project teaching mode, Discussion teaching mode, Demonstrative teaching mode
Develop problem solving skills	Students become creative and try new methods to solve tasks	• (Al-Shuaily, )	• SQL • Cognitive task, Comprehension Task
		• (Cai & Gao, )	• E-R model, relational model, SQL • Database Course for Liberal Arts Majors
		• (Martin et al., )	• SQL and relational algebra, The relational model, Transaction management • Collaborative Learning
		• (Martinez-González & Duffing, )	• Data Models, Physical Data Design, SQL • European convergence in higher education
		• (Prince & Felder, )	• SQL • Inductive teaching and learning
		• (Urban & Dietrich, )	• Relational database mapping and prototyping, Database system implementation • cooperative group project based learning
		• (Gudivada et al., )	• SQL, Logical design, Physical Design • Working with large datasets from Amazon
	Use same methods as mentioned in books	• (Eaglestone & Nunes, )	• SQL, ER Model • Pedagogical model, teaching and learning strategies
		• (Wang et al., )	• SQL Server and Oracle • Refine Teaching Method
		• (Efendiouglu & Yelken )	• SQL • Programmed instruction and meaningful learning
Motivate students to explore topics through independent study	Students begin to read books and internet to enhance their knowledge independently or in groups	• (Cai & Gao, )	• SQL, E-R model, relational model • Database Course for Liberal Arts Majors
		• (Kawash et al., )	• SQL, Entity Relationship, Relational model • Group Exams
		• (Martin et al., )	• SQL, Relational Model, UML • Collaborative Learning
		• (Martinez-González & Duffing, )	• SQL, Data Models, Physical Data Design • European convergence in higher education
		• (Amadio, )	• SQL Programming • Team Learning
	Students stick to the course content	• (Morien, )	• Entity modeling, relational modelling • Teaching Reform
		• (Eaglestone & Nunes, )	• SQL, ER Model • Pedagogical model, teaching and learning strategies
		• (Zheng & Dong, )	• SQL, ER Model • Teaching Reform and Practice
Focus on theory and practical Gap	Students begin to apply theoretical knowledge on developing database applications	• (Al-Shuaily, )	• SQL • Cognitive task, Comprehension Task
		• (Etemad & Küpçü, )	• SQL • cooperative group project-based learning
		• (Svahnberg et al., )	• SQL • Industrial project-based learning
		• (Taipalus et al., )	• SQL • Group learning and projects
		• (Juxiang & Zhihong, )	• SQL, ER Model • Computational Thinking
		• (Connolly & Begg, )	• Database concepts, Database Analysis and Design, Implementation • Constructivist-Based Approach
		• (Rashid & Al-Radhy, )	• Database Analysis and Design • Project based learning, Assessment based learning
		• (Naik & Gajjar, )	• database designing, transaction management, SQL • ENABLE, Project based learning
	Students only focus on theory to clear exams	• (Wang et al., )	• SQL Server and Oracle • Refine Teaching Method
		• (Zheng & Dong, )	• SQL, ER Model • Teaching Reform and Practice
		• (Nelson et al., )	• Advanced relational design, UML, data warehousing • Teaching Methods, Assessment Methods

Students become creative and innovative when the try to study on their own and also from different resources rather than curriculum books only. In the modern era, there are various resources available on both online and offline platforms. Modern teaching methods must emphasize on making the students independent from the curriculum books and educate them to learn independently(Amadio et al., 2003 ; Cai & Gao, 2019 ; Martin et al., 2013 ). Also, in the work of Kawash et al. ( 2020 ) proposed he group study-based learning approach called Graded Group Activities (GGAs). In this method students team up in order to take the exam as a group. On the other hand, few studies have emphasized on course content to prepare students for the final exams such as, Zheng and Dong ( 2011 ) have discussed the issues of computer science teaching with particular focus on database systems, where different characteristics of the course, teaching content and suggestions to teach this course effectively have been presented.

As technology is evolving at rapid speed, so students need to have practical experience from the start. Basic theoretical concepts of database are important but they are of no use without its implementation in real world projects. Most of the students study in the institutes with the aim of only clearing the exams with the help of theoretical knowledge and very few students want to have practical experience(Wang & Chen, 2014 ; Zheng & Dong, 2011 ). To reduce the gap between the theory and its implementation, authors have proposed teaching methods to develop the student’s interest in the real-world projects (Naik & Gajjar, 2021 ; Svahnberg et al., 2008 ; Taipalus et al., 2018 ). Moreover, Juxiang and Zhihong ( 2012 ) have proposed that the teaching organization starts from application scenarios, and associate database theoretical knowledge with the process from analysis, modeling to establishing database application. Also, Svahnberg et al. ( 2008 ) explained that in particular conditions, there is a possibility to use students as subjects for experimental studies in DSE and influencing them by providing responses that are in line with industrial practice.

On the other hand, Nelson et al. ( 2003 ) evaluated the different teaching methods used to teach different modules of database in the School of Computing and Technology at the University of Sunder- land. They outlined suggestions for changes to the database curriculum to further integrate research and state-of-the-art systems in databases.

• III. Curriculum

Database curriculum has been revisited many times in the form of guidelines that not only present the contents but also suggest approximate time to cover different topics. According to the ACM curriculum guidelines (Lunt et al., 2008 ) for the undergraduate programs in computer science, the overall coverage time for this course is 46.50 h distributed in such a way that 11 h is the total coverage time for the core topics such as, Information Models (4 core hours), Database Systems (3 core hours) and Data Modeling (4 course hours). Whereas, the remaining hours are allocated for elective topics such as Indexing, Relational Databases, Query Languages, Relational Database Design, Transaction Processing, Distributed Databases, Physical Database Design, Data Mining, Information Storage and Retrieval, Hypermedia, Multimedia Systems, and Digital Libraries(Marshall, 2012 ). While, according to the ACM curriculum guidelines ( 2013 ) for undergraduate programs in computer science, this course should be completed in 15 weeks with two and half hour lecture per week and lab session of four hours per week on average (Brady et al., 2004 ). Thus, the revised version emphasizes on the practice based learning with the help of lab component. Numerous organizations have exerted efforts in this field to classify DSE (Dietrich et al., 2008 ). DSE model curricula, bodies of knowledge (BOKs), and some standardization aspects in this field are discussed below:

Model curricula

There are standard bodies who set the curriculum guidelines for teaching undergraduate degree programs in computing disciplines. Curricula which include the guidelines to teach database are: Computer Engineering Curricula (CEC) (Meier et al., 2008 ), Information Technology Curricula (ITC) (Alrumaih, 2016 ), Computing Curriculum Software Engineering (CCSE) (Meyer, 2001 ), Cyber Security Curricula (CSC) (Brady et al., 2004 ; Bishop et al., 2017 ).

Bodies of knowledge (BOK)

A BOK includes the set of thoughts and activities related to the professional area, while in model curriculum set of guidelines are given to address the education issues (Sahami et al., 2011 ). Database body of Knowledge comprises of (a) The Data Management Body of Knowledge (DM- BOK), (b) Software Engineering Education Knowledge (SEEK) (Sobel, 2003 ) (Sobel, 2003 ), and (c) The SE body of knowledge (SWEBOK) (Swebok Evolution: IEEE Computer Society n.d. ).

Apart from the model curricula, and bodies of knowledge, there also exist some standards related to the database and its different modules: ISO/IEC 9075–1:2016 (Computing Curricula, 1991 ), ISO/IEC 10,026–1: 1998 (Suryn, 2003 ).

We also utilize advices from some studies (Elberzhager et al., 2012 ; Keele et al., 2007 ) to search for relevant papers. In order to conduct this systematic study, it is essential to formulate the primary research questions (Mushtaq et al., 2017 ). Since the data management techniques and software are evolving rapidly, the database curriculum should also be updated accordingly to meet these new requirements. Some authors have described ways of updating the content of courses to keep pace with specific developments in the field and others have developed new database curricula to keep up with the new data management techniques.

Furthermore, some authors have suggested updates for the database curriculum based on the continuously evolving technology and introduction of big data. For instance Bhogal et al. ( 2012 ) have shown that database curricula need to be updated and modernized, which can be achieved by extending the current database concepts that cover the strategies to handle the ever changing user requirements and how database technology has evolved to meet the requirements. Likewise, Picciano ( 2012 ) examines the evolving world of big data and analytics in American higher education. According to the author, the “data driven” decision making method should be used to help the institutes evaluate strategies that can improve retention and update the curriculum that has big data basic concepts and applications, since data driven decision making has already entered in the big data and learning analytic era. Furthermore, Marshall ( 2011 ) presented the challenges faced when developing a curriculum for a Computer Science degree program in the South African context that is earmarked for international recognition. According to the author, the Curricula needs to adhere both to the policy and content requirements in order to be rated as being of a particular quality.

Similarly, some studies (Abourezq & Idrissi, 2016 ; Mingyu et al., 2017 ) described big data influence from a social perspective and also proceeded with the gaps in database curriculum of computer science, especially, in the big data era and discovers the teaching improvements in practical and theoretical teaching mode, teaching content and teaching practice platform in database curriculum. Also Silva et al. ( 2016 ) propose teaching SQL as a general language that can be used in a wide range of database systems from traditional relational database management systems to big data systems.

On the other hand, different authors have developed a database curriculum based on the different academic background of students. Such as, Dean and Milani ( 1995 ) have recommended changes in computer science curricula based on the practice in United Stated Military Academy (USMA). They emphasized greatly on the practical demonstration of the topic rather than the theoretical explanation. Especially, for the non-computer science major students. Furthermore, Urban and Dietrich ( 2001 ) described the development of a second course on database systems for undergraduates, preparing students for the advanced database concepts that they will exercise in the industry. They also shared their experience with teaching the course, elaborating on the topics and assignments. Also, Andersson et al. ( 2019 ) proposed variations in core topics of database management course for the students with the engineering background. Moreover, Dietrich et al. ( 2014 ) described two animations developed with images and color that visually and dynamically introduce fundamental relational database concepts and querying to students of many majors. The goal is that the educators, in diverse academic disciplines, should be able to incorporate these animations in their existing courses to meet their pedagogical needs.

The information systems have evolved into large scale distributed systems that store and process a huge amount of data across different servers, and process them using different distributed data processing frameworks. This evolution has given birth to new paradigms in database systems domain termed as NoSQL and Big Data systems, which significantly deviate from conventional relational and distributed database management systems. It is pertinent to mention that in order to offer a sustainable and practical CS education, these new paradigms and methodologies as shown in Fig.  5 should be included into database education (Kleiner, 2015 ). Tables ​ Tables9 9 and ​ and10 10 shows the summarized findings of the curriculum based reviewed studies. This section also proposed appropriate text book based on the theory, project, and practice-based teaching methodology as shown in Table ​ Table9. 9 . The proposed books are selected purely on the bases of their usage in top universities around the world such as, Massachusetts Institute of Technology, Stanford University, Harvard University, University of Oxford, University of Cambridge and, University of Singapore and the coverage of core topics mentioned in the database curriculum.

Concepts in Database Systems Education (Kleiner, 2015 )

Recommended text books for DSE

Methodology	Book title	Author(s)	Edition	Year
Theory	Database Management Systems	Ramakrishnan, Raghu, and Johannes Gehrke	3	2002
	Database Systems: The Complete Book	Garcia-Molina, Ullman and Widom	2	2008
	Introduction to Database Systems	C. J. Date Addison-Wesley	8	2003
	Introduction to Database Systems	S. Bressan and B. Catania	1	2005
	Database system concepts	Silberschatz, A., Korth, H.F. and Sudarshan, S	7	2019
	A first course in database systems	Ullman, J. and Widom, J	3	2007
Project	Modern Database Management	Jeffrey A. Hoffer, Ramesh Venkataraman and HeikkiTopi	12	2015
	Database Systems: A Practical Approach to Design, Implementation, and Management	Thomas M. Connolly,Carolyn E. Begg	6	2015
Practice	Fundamentals of SQL Programming	R. A. Mata-Toledo and P. Cushman. Schaum’s	1	2000
	Readings in Database Systems (The Red Book)	Hellerstein, Joseph, and Michael Stonebraker	4	2005

Curriculum: Findings of Reviewed Literature

Objective	Findings	References	Topic(s)/ Curricula	Standard bodies
Recommendations and revisions	Proposed variations based on the scope in the region	• (Abourezq & Idrissi, )	• Big Data, SQL • Computer Science Curricula	• CS 2008
		• (Bhogal et al., )	• Big Data • Computer Science/Engineering Curriculum	• CS 2008/CE 2004
		• (Mingyu et al., )	• Big Data, NoSQL • Computer Science Curricula	• CS 2013
		• (Picciano, )	• Big Data • Computer Science Curricula	• CS 2008
		• (Silva et al., )	• Big Data, MapReduce, NoSQL • and NewSQL • Computer Science Curricula	• CS 2013
		• (Calero et al., )	• Database Design, Database Administration, Database Application • SWEBOK, DBBOK	• N/A
		• (Conklin & Heinrichs, )	• Database theory and database practice • Computer Science Curricula	• IS 2002 • CC2001 • CC2004
		• (Zhanquan et al., )	• Database principles design • Coursera, Udacity, edX	• N/A
		• (Robbert & Ricardo, )	• Data Models, Physical Data Design, SQL • Computer Science Curricula	• CC 2001
		• (Luo et al., )	• SQL Server and Oracle • Computer Science Curricula	• CC 2004
		• (Dietrich & Urban, )	• Object oriented database (OODB) systems; object relational database (ORDB) systems • Curriculum and Laboratory Improvement Educational Materials Development (CCLI EMD)	• N/A
		• (Marshall, )	• Data Models, Physical Data Design, Database Schema and Design, SQL • CS-BoK	• N/A
	Proposed variations based on the educational background of the students	• (Dean & Milani, )	• SQL • Computer Science Curricula	• ACM/IEEE Computing Curricula
		• (Dietrich et al., )	• Relational Databases • Computer Science Curricula	• CC 2008
		• (Urban & Dietrich, )	• Relational algebra, Relational calculus, and SQL • Engineering Curriculum 2000	• CC 2001
		• (Andersson et al., )	• ER Model, Relational Model, SQL • Engineering Curriculum	• CE 2000
Relating Curriculum to assessment	Proposed variations based on the assessment methods	• (Abid et al., )	• Data Models, Physical Data Design, Database Schema and Design, SQL • Computer Science Curricula	• CS 2008
		• (Adams et al., )	• ER, EER, and UML • Computer Science Curricula	• CC 2001

RQ.2 Evolution of DSE research

This section discusses the evolution of database while focusing the DSE over the past 25 years as shown in Fig.  6 .

Evolution of DSE studies

This study shows that there is significant increase in research in DSE after 2004 with 78% of the selected papers are published after 2004. The main reason of this outcome is that some of the papers are published in well-recognized channels like IEEE Transactions on Education, ACM Transactions on Computing Education, International Conference on Computer Science and Education (ICCSE), and Teaching, Learning and Assessment of Database (TLAD) workshop. It is also evident that several of these papers were published before 2004 and only a few articles were published during late 1990s. This is because of the fact that DSE started to gain interest after the introduction of Body of Knowledge and DSE standards. The data intensive scientific discovery has been discussed as the fourth paradigm (Hey et al., 2009 ): where the first involves empirical science and observations; second contains theoretical science and mathematically driven insights; third considers computational science and simulation driven insights; while the fourth involves data driven insights of modern scientific research.

Over the past few decades, students have gone from attending one-room class to having the world at their fingertips, and it is a great challenge for the instructors to develop the interest of students in learning database. This challenge has led to the development of the different types of interactive tools to help the instructors teach DSE in this technology oriented era. Keeping the importance of interactive tools in DSE in perspective, various authors have proposed different interactive tools over the years, such as during 1995–2003, when different authors proposed various interactive tools. Some studies (Abut & Ozturk, 1997 ; Mcintyre et al., 1995 ) introduced state of the art interactive tools to teach and enhance the collaborative learning among the students. Similarly, during 2004–2005 more interactive tools in the field of DSE were proposed such as Pahl et al. ( 2004 ), Connolly et al. ( 2005 ) introduced multimedia system based interactive model and game based collaborative learning environment.

The Internet has started to become more common in the first decade of the twenty-first century and its positive impact on the education sector was undeniable. Cost effective, student teacher peer interaction, keeping in touch with the latest information were the main reasons which made the instructors employ web-based tools to teach database in the education sector. Due to this spike in the demand of web-based tools, authors also started to introduce new instruments to assist with teaching database. In 2007 Regueras et al. ( 2007 ) proposed an e-learning tool named QUEST with a feedback module to help the students to learn from their mistakes. Similarly, in 2010, multiple authors have proposed and evaluated various web-based tools. Cvetanovic et al. ( 2010 ) proposed ADVICE with the functionality to monitor student’s progress, while, few authors (Wang et al., 2010 ) proposed Metube which is a variation of YouTube. Furthermore, Nelson and Fatimazahra ( 2010 ) evaluated different web-based tools to highlight the complexities of using these web-based instruments.

Technology has changed the teaching methods in the education sector but technology cannot replace teachers, and despite the amount of time most students spend online, virtual learning will never recreate the teacher-student bond. In the modern era, innovation in technology used in educational sectors is not meant to replace the instructors or teaching methods.

During the 1990s some studies (Dietrich & Urban, 1996 ; Urban & Dietrich, 1997 ) proposed learning and teaching methods respectively keeping the evolving technology in view. The highlight of their work was project deliverables and assignments where students progressively advanced to a step-by-step extension, from a tutorial exercise and then attempting more difficult extension of assignment.

During 2002–2007 various authors have discussed a number of teaching and learning methods to keep up the pace with the ever changing database technology, such as Connolly and Begg ( 2006 ) proposing a constructive approach to teach database analysis and design. Similarly, Prince and Felder ( 2006 ) reviewed the effectiveness of inquiry learning, problem based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching. Also, McIntyre et al. (Mcintyre et al., 1995 ) brought to light the impact of convergence of European Union (EU) in different universities across Europe. They suggested a reconstruction of teaching and learning methodologies in order to effectively teach database.

During 2008–2013 more work had been done to address the different methods of teaching and learning in the field of DSE, like the work of Dominguez and Jaime ( 2010 ) who proposed an active learning approach. The focus of their study was to develop the interest of students in designing and developing databases. Also, Zheng and Dong ( 2011 ) have highlighted various characteristics of the database course and its teaching content. Similarly, Yuelan et al. ( 2011 ) have reformed database teaching methods. The main focus of their study were the Modern ways of education, project driven approach, strengthening the experimental aspects, and improving the traditional assessment method. Likewise, Al-Shuaily ( 2012 ) has explored 4 cognitive factors that can affect the learning process of database. The main focus of their study was to facilitate the students in learning SQL. Subsequently, Chen et al. ( 2012 ) also proposed scaffolding-based concept mapping strategy. This strategy helps the students to better understand database management courses. Correspondingly, Martin et al. ( 2013 ) discussed various collaborative learning techniques in the field of DSE while keeping database as an introductory course.

In the years between 2014 and 2021, research in the field of DSE increased, which was the main reason that the most of teaching, learning and assessment methods were proposed and discussed during this period. Rashid and Al-Radhy ( 2014 ) discussed the issues of traditional teaching, learning, assessing methods of database courses at different universities in Kurdistan and the main focus of their study being reformation issues, such as absence of teaching determination and contradiction between content and theory. Similarly, Wang and Chen ( 2014 ) summarized the main problems in teaching the traditional database theory and its application. Curriculum assessment mode was the main focus of their study. Eaglestone and Nunes ( 2004 ) shared their experiences of delivering a databases design course at Sheffield University. Their focus of study included was to teach the database design module to a diverse group of students from different backgrounds. Rashid ( 2015 ) discussed some important features of database courses, whereby reforming the conventional teaching, learning, and assessing strategies of database courses at universities were the main focus of this study. Kui et al. ( 2018 ) reformed the teaching mode of database courses based on flipped classroom. Initiative learning of database courses was their main focus in this study. Similarly, Zhang et al. ( 2018 ) discussed several effective classroom teaching measures. The main focus of their study was teaching content, teaching methods, teaching evaluation and assessment methods. Cai and Gao ( 2019 ) also carried out the teaching reforms in the database course of liberal arts. Diversified teaching modes, such as flipping classroom, case oriented teaching and task oriented were the focus of their study. Teaching Kawash et al. ( 2020 ) proposed a learning approach called Graded Group Activities (GGAs). Their main focus of the study was reforming learning and assessment method.

Database course covers several topics that range from data modeling to data implementation and examination. Over the years, various authors have given their suggestions to update these topics in database curriculum to meet the requirements of modern technologies. On the other hand, authors have also proposed a new curriculum for the students of different academic backgrounds and different areas. These reformations in curriculum helped the students in their preparation, practically and theoretically, and enabled them to compete in the competitive market after graduation.

During 2003 and 2006 authors have proposed various suggestions to update and develop computer science curriculum across different universities. Robbert and Ricardo ( 2003 ) evaluated three reviews from 1999 to 2002 that were given to the groups of educators. The focus of their study was to highlight the trends that occurred in database curriculum. Also, Calero et al. ( 2003 ) proposed a first draft for this Database Body of Knowledge (DBBOK). Database (DB), Database Design (DBD), Database Administration (DBAd), Database Application (DBAp) and Advance Databases (ADVDB) were the main focus of their study. Furthermore, Conklin and Heinrichs (Conklin & Heinrichs, 2005 ) compared the content included in 13 database textbooks and the main focus of their study was IS 2002, CC2001, and CC2004 model curricula.

The years from 2007 and 2011, authors managed to developed various database curricula, like Luo et al. ( 2008 ) developed curricula in Zhejiang University City College. The aim of their study to nurture students to be qualified computer scientists. Likewise, Dietrich et al. ( 2008 ) proposed the techniques to assess the development of an advanced database course. The purpose behind the addition of an advanced database course at undergraduate level was to prepare the students to respond to industrial requirements. Also, Marshall ( 2011 ) developed a new database curriculum for Computer Science degree program in the South African context.

During 2012 and 2021 various authors suggested updates for the database curriculum such as Bhogal et al. ( 2012 ) who suggested updating and modernizing the database curriculum. Data management and data analytics were the focus of their study. Similarly, Picciano ( 2012 ) examined the curriculum in the higher level of American education. The focus of their study was big data and analytics. Also, Zhanquan et al. ( 2016 ) proposed the design for the course content and teaching methods in the classroom. Massive Open Online Courses (MOOCs) were the focus of their study. Likewise, Mingyu et al. ( 2017 ) suggested updating the database curriculum while keeping new technology concerning the database in perspective. The focus of their study was big data.

The above discussion clearly shows that the SQL is most discussed topic in the literature where more than 25% of the studies have discussed it in the previous decade as shown in Fig.  7 . It is pertinent to mention that other SQL databases such as Oracle, MS access are discussed under the SQL banner (Chen et al., 2012 ; Hou & Chen, 2010 ; Wang & Chen, 2014 ). It is mainly because of its ability to handle data in a relational database management system and direct implementation of database theoretical concepts. Also, other database topics such as transaction management, application programming etc. are also the main highlights of the topics discussed in the literature.

Evolution of Database topics discussed in literature

Research synthesis, advice for instructors, and way forward

This section presents the synthesized information extracted after reading and analyzing the research articles considered in this study. To this end, it firstly contextualizes the tools and methods to help the instructors find suitable tools and methods for their settings. Similarly, developments in curriculum design have also been discussed. Subsequently, general advice for instructors have been discussed. Lastly, promising future research directions for developing new tools, methods, and for revising the curriculum have also been discussed in this section.

Methods, tools, and curriculum

Methods and tools.

Web-based tools proposed by Cvetanovic et al. ( 2010 ) and Wang et al. ( 2010 ) have been quite useful, as they are growing increasingly pertinent as online mode of education is prevalent all around the globe during COVID-19. On the other hand, interactive tools and smart class room methodology has also been used successfully to develop the interest of students in database class. (Brusilovsky et al., 2010 ; Connolly et al., 2005 ; Pahl et al., 2004 ; Canedo et al., 2021 ; Ko et al., 2021 ).

One of the most promising combination of methodology and tool has been proposed by Cvetanovic et al. ( 2010 ), whereby they developed a tool named ADVICE that helps students learn and implement database concepts while using project centric methodology, while a game based collaborative learning environment was proposed by Connolly et al. ( 2005 ) that involves a methodology comprising of modeling, articulation, feedback, and exploration. As a whole, project centric teaching (Connolly & Begg, 2006 ; Domínguez & Jaime, 2010 ) and teaching database design and problem solving skills Wang and Chen ( 2014 ), are two successful approaches for DSE. Whereas, other studies (Urban & Dietrich, 1997 ) proposed teaching methods that are more inclined towards practicing database concepts. While a topic specific approach has been proposed by Abbasi et al. ( 2016 ), Taipalus et al. ( 2018 ) and Silva et al. ( 2016 ) to teach and learn SQL. On the other hand, Cai and Gao ( 2019 ) developed a teaching method for students who do not have a computer science background. Lastly, some useful ways for defining assessments for DSE have been proposed by Kawash et al. ( 2020 ) and Zhang et al. ( 2018 ).

Curriculum of database adopted by various institutes around the world does not address how to teach the database course to the students who do not have a strong computer science background. Such as Marshall ( 2012 ), Luo et al. ( 2008 ) and Zhanquan et al. ( 2016 ) have proposed the updates in current database curriculum for the students who are not from computer science background. While Abid et al. ( 2015 ) proposed a combined course content and various methodologies that can be used for teaching database systems course. On the other hand, current database curriculum does not include the topics related to latest technologies in database domain. This factor was discussed by many other studies as well (Bhogal et al., 2012 ; Mehmood et al., 2020 ; Picciano, 2012 ).

Guidelines for instructors

The major conclusion of this study are the suggestions based on the impact and importance for instructors who are teaching DSE. Furthermore, an overview of productivity of every method can be provided by the empirical studies. These instructions are for instructors which are the focal audience of this study. These suggestions are subjective opinions after literature analysis in form of guidelines according to the authors and their meaning and purpose were maintained. According to the literature reviewed, various issues have been found in this section. Some other issues were also found, but those were not relevant to DSE. Following are some suggestions that provide interesting information:

Project centric and applied approach

• To inculcate database development skills for the students, basic elements of database development need to be incorporated into teaching and learning at all levels including undergraduate studies (Bakar et al., 2011 ). To fulfill this objective, instructors should also improve the data quality in DSE by assigning the projects and assignments to the students where they can assess, measure and improve the data quality using already deployed databases. They should demonstrate that the quality of data is determined not only by the effective design of a database, but also through the perception of the end user (Mathieu & Khalil, 1997 )
• The gap between the database course theory and industrial practice is big. Fresh graduate students find it difficult to cope up with the industrial pressure because of the contrast between what they have been taught in institutes and its application in industry (Allsopp et al., 2006 ). Involve top performers from classes in industrial projects so that they are able to acquiring sufficient knowledge and practice, especially for post graduate courses. There must be some other activities in which industry practitioners come and present the real projects and also share their industrial experiences with the students. The gap between theoretical and the practical sides of database has been identified by Myers and Skinner ( 1997 ). In order to build practical DS concepts, instructors should provide the students an accurate view of reality and proper tools.

Importance of software development standards and impact of DB in software success

• They should have the strategies, ability and skills that can align the DSE course with the contemporary Global Software Development (GSD) (Akbar & Safdar, 2015 ; Damian et al., 2006 ).
• Enable the students to explain the approaches to problem solving, development tools and methodologies. Also, the DS courses are usually taught in normal lecture format. The result of this method is that students cannot see the influence on the success or failure of projects because they do not realize the importance of DS activities.

Pedagogy and the use of education technology

• Some studies have shown that teaching through play and practical activities helps to improve the knowledge and learning outcome of students (Dicheva et al., 2015 ).
• Interactive classrooms can help the instructors to deliver their lecture in a more effective way by using virtual white board, digital textbooks, and data over network(Abut & Ozturk, 1997 ). We suggest that in order to follow the new concept of smart classroom, instructors should use the experience of Yau and Karim ( 2003 ) which benefits in cooperative learning among students and can also be adopted in DSE.
• The instructors also need to update themselves with full spectrum of technology in education, in general, and for DSE, in particular. This is becoming more imperative as during COVID the world is relying strongly on the use of technology, particularly in education sector.

Periodic Curriculum Revision

• There is also a need to revisit the existing series of courses periodically, so that they are able to offer the following benefits: (a) include the modern day database system concepts; (b) can be offered as a specialization track; (c) a specialized undergraduate degree program may also be designed.

DSE: Way forward

This research combines a significant work done on DSE at one place, thus providing a point to find better ways forward in order to improvise different possible dimensions for improving the teaching process of a database system course in future. This section discusses technology, methods, and modifications in curriculum would most impact the delivery of lectures in coming years.

Several tools have already been developed for effective teaching and learning in database systems. However, there is a great room for developing new tools. Recent rise of the notion of “serious games” is marking its success in several domains. Majority of the research work discussed in this review revolves around web-based tools. The success of serious games invites researchers to explore this new paradigm of developing useful tools for learning and practice database systems concepts.

Likewise, due to COVID-19 the world is setting up new norms, which are expected to affect the methods of teaching as well. This invites the researchers to design, develop, and test flexible tools for online teaching in a more interactive manner. At the same time, it is also imperative to devise new techniques for assessments, especially conducting online exams at massive scale. Moreover, the researchers can implement the idea of instructional design in web-based teaching in which an online classroom can be designed around the learners’ unique backgrounds and effectively delivering the concepts that are considered to be highly important by the instructors.

The teaching, learning and assessment methods discussed in this study can help the instructors to improve their methods in order to teach the database system course in a better way. It is noticed that only 16% of authors have the assessment methods as their focus of study, which clearly highlights that there is still plenty of work needed to be done in this particular domain. Assessment techniques in the database course will help the learners to learn from their mistakes. Also, instructors must realize that there is a massive gap between database theory and practice which can only be reduced with maximum practice and real world database projects.

Similarly, the technology is continuously influencing the development and expansion of modern education, whereas the instructors’ abilities to teach using online platforms are critical to the quality of online education.

In the same way, the ideas like flipped classroom in which students have to prepare the lesson prior to the class can be implemented on web-based teaching. This ensures that the class time can be used for further discussion of the lesson, share ideas and allow students to interact in a dynamic learning environment.

The increasing impact of big data systems, and data science and its anticipated impact on the job market invites the researchers to revisit the fundamental course of database systems as well. There is a need to extend the boundaries of existing contents by including the concepts related to distributed big data systems data storage, processing, and transaction management, with possible glimpse of modern tools and technologies.

As a whole, an interesting and long term extension is to establish a generic and comprehensive framework that engages all the stakeholders with the support of technology to make the teaching, learning, practicing, and assessing easier and more effective.

This SLR presents review on the research work published in the area of database system education, with particular focus on teaching the first course in database systems. The study was carried out by systematically selecting research papers published between 1995 and 2021. Based on the study, a high level categorization presents a taxonomy of the published under the heads of Tools, Methods, and Curriculum. All the selected articles were evaluated on the basis of a quality criteria. Several methods have been developed to effectively teach the database course. These methods focus on improving learning experience, improve student satisfaction, improve students’ course performance, or support the instructors. Similarly, many tools have been developed, whereby some tools are topic based, while others are general purpose tools that apply for whole course. Similarly, the curriculum development activities have also been discussed, where some guidelines provided by ACM/IEEE along with certain standards have been discussed. Apart from this, the evolution in these three areas has also been presented which shows that the researchers have been presenting many different teaching methods throughout the selected period; however, there is a decrease in research articles that address the curriculum and tools in the past five years. Besides, some guidelines for the instructors have also been shared. Also, this SLR proposes a way forward in DSE by emphasizing on the tools: that need to be developed to facilitate instructors and students especially post Covid-19 era, methods: to be adopted by the instructors to close the gap between the theory and practical, Database curricula update after the introduction of emerging technologies such as big data and data science. We also urge that the recognized publication venues for database research including VLDB, ICDM, EDBT should also consider publishing articles related to DSE. The study also highlights the importance of reviving the curricula, tools, and methodologies to cater for recent advancements in the field of database systems.

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DBMS Tutorial – Learn Database Management System

Basic of dbms.

• Introduction of DBMS (Database Management System) - Set 1
• History of DBMS
• Advantages of Database Management System

Disadvantages of DBMS

• Application of DBMS
• Need for DBMS
• DBMS Architecture 1-level, 2-Level, 3-Level
• Difference between File System and DBMS

Entity Relationship Model

• Introduction of ER Model
• Structural Constraints of Relationships in ER Model
• Difference between entity, entity set and entity type
• Difference between Strong and Weak Entity
• Generalization, Specialization and Aggregation in ER Model
• Recursive Relationships in ER diagrams
• Relational Model
• Introduction of Relational Model and Codd Rules in DBMS
• Types of Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign)
• Anomalies in Relational Model
• Mapping from ER Model to Relational Model
• Strategies for Schema design in DBMS

Relational Algebra

• Introduction of Relational Algebra in DBMS
• Basic Operators in Relational Algebra
• Extended Operators in Relational Algebra
• SQL Joins (Inner, Left, Right and Full Join)
• Join operation Vs Nested query in DBMS
• Tuple Relational Calculus (TRC) in DBMS
• Domain Relational Calculus in DBMS

Functional Dependencies

• Functional Dependency and Attribute Closure
• Armstrong's Axioms in Functional Dependency in DBMS
• Equivalence of Functional Dependencies
• Canonical Cover of Functional Dependencies in DBMS

Normalisation

• Introduction of Database Normalization
• Normal Forms in DBMS
• First Normal Form (1NF)
• Second Normal Form (2NF)
• Boyce-Codd Normal Form (BCNF)
• Introduction of 4th and 5th Normal Form in DBMS
• The Problem of Redundancy in Database
• Database Management System | Dependency Preserving Decomposition
• Lossless Decomposition in DBMS
• Lossless Join and Dependency Preserving Decomposition
• Denormalization in Databases

Transactions and Concurrency Control

• Concurrency Control in DBMS
• ACID Properties in DBMS
• Implementation of Locking in DBMS
• Lock Based Concurrency Control Protocol in DBMS
• Graph Based Concurrency Control Protocol in DBMS
• Two Phase Locking Protocol
• Multiple Granularity Locking in DBMS
• Polygraph to check View Serializability in DBMS
• Log based Recovery in DBMS
• Timestamp based Concurrency Control
• Dirty Read in SQL
• Types of Schedules in DBMS
• Conflict Serializability in DBMS
• Condition of schedules to View-equivalent
• Recoverability in DBMS
• Precedence Graph for Testing Conflict Serializability in DBMS
• Database Recovery Techniques in DBMS
• Starvation in DBMS
• Deadlock in DBMS
• Types of Schedules based Recoverability in DBMS
• Why recovery is needed in DBMS

Indexing, B and B+ trees

• Indexing in Databases - Set 1
• Introduction of B-Tree
• Insert Operation in B-Tree
• Delete Operation in B-Tree
• Introduction of B+ Tree
• Bitmap Indexing in DBMS
• Inverted Index
• Difference between Inverted Index and Forward Index
• SQL Queries on Clustered and Non-Clustered Indexes

File organization

• File Organization in DBMS - Set 1
• File Organization in DBMS | Set 2
• File Organization in DBMS | Set 3

DBMS Interview questions and Last minute notes

• Last Minute Notes - DBMS
• Commonly asked DBMS interview questions
• Commonly asked DBMS interview questions | Set 2

DBMS GATE Previous Year Questions

• Database Management System - GATE CSE Previous Year Questions
• Database Management Systems | Set 2
• Database Management Systems | Set 3
• Database Management Systems | Set 4
• Database Management Systems | Set 5
• Database Management Systems | Set 6
• Database Management Systems | Set 7
• Database Management Systems | Set 8

Database Management System is a software or technology used to manage data from a database. Some popular databases are MySQL, Oracle, MongoDB, etc. DBMS provides many operations e.g. creating a database, Storing in the database, updating an existing database, delete from the database. DBMS is a system that enables you to store, modify and retrieve data in an organized way. It also provides security to the database.

In this Database Management System tutorial you’ll learn basic to advanced topics like ER model, Relational Model, Relation Algebra, Normalization, File Organization, etc.

Table of Content

Introduction :

Entity relationship model :, relational model :, relational algebra :, functional dependencies :, normalisation :, transactions and concurrency control :, indexing, b and b+ trees :, file organization:, advanced topics :, sql tutorial, advantages of dbms, quick links :.

• DBMS Introduction | Set 1
• DBMS Introduction | Set 2 (3-Tier Architecture)
• DBMS Architecture 2-level 3-level
• Need For DBMS
• Data Abstraction and Data Independence
• Database Objects
• Multimedia Database
• Categories of End Users
• Use of DBMS in System Software
• Choice of DBMS | Economic factors
• Enhanced ER Model
• Minimization of ER Diagram
• ER Model: Generalization, Specialization and Aggregation
• Recursive Relationships
• Impedance Mismatch
• Relational Model and CODD Rules
• Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign)
• Number of possible Superkeys
• Strategies for Schema design
• Schema Integration
• Star Schema in Data Warehouse modeling
• Data Warehouse Modeling | Snowflake Schema
• Dimensional Data Modeling

>> Quiz on ER and Relational Model

• Introduction
• Basic Operators
• Extended Operators
• Inner Join vs Outer Join
• Join operation Vs nested query
• DBMS | Tupple Relational Calculus
• Row oriented vs. column oriented data stores
• How to solve Relational Algebra Problems for GATE
• How to Solve Relational Algebra Problems for GATE
• Finding Attribute Closure and Candidate Keys using Functional Dependencies
• Armstrong’s Axioms in Functional Dependency
• Canonical Cover
• Normal Forms
• Minimum relations satisfying 1NF
• The Problem of redundancy in Database
• Dependency Preserving Decomposition
• Lossless Join Decomposition
• LossLess Join and Dependency Preserving Decomposition
• How to find the Highest Normal Form of a Relation
• Domain Key normal form
• Introduction of 4th and 5th Normal form
• DBMS | Data Replication

>> Quiz on Normal Forms

• ACID Properties
• Concurrency Control -Introduction
• Concurrency Control Protocols – Lock Based Protocol
• Concurrency Control Protocol | Graph Based Protocol
• Concurrency Control Protocol | Two Phase Locking (2-PL)-I
• Concurrency Control Protocol | Two Phase Locking (2-PL)-II
• Concurrency Control Protocol | Two Phase Locking (2-PL)-III
• Concurrency Control Protocol | Multiple Granularity Locking
• Concurrency Control Protocol | Thomas Write Rule
• Concurrency Control | Polygraph to check View Serializabilty
• DBMS | Log based recovery
• Timestamp Ordering Protocols
• Introduction to TimeStamp and Deadlock Prevention Schemes
• Dirty read in SQL
• Types of Schedules
• Conflict Serializability
• View Serializability
• How to test if two schedules are View Equal or not ?
• Recoverability of Schedules
• Precedence Graph for testing Conflict Serializabilty
• Transaction Isolation Levels in DBMS
• Database Recovery Techniques
• DBMS | OLAP vs OLTP
• Types of OLAP Systems
• DBMS | Types of Recoverability of Schedules and easiest way to test schedule | Set 2
• Web Information Retrieval | Vector Space Model
• Why recovery is needed?

>> Quiz on Transactions and concurrency control

• Indexing and its Types
• B-Tree | Set 1 (Introduction)
• B-Tree | Set 2 (Insert)
• B-Tree | Set 3 (Delete)
• B+ Tree (Introduction)
• Bitmap Indexing
• SQL queries on clustered and non-clustered Indexes

>> Practice questions on B and B+ Trees >> Quizzes on Indexing, B and B+ Trees

• File Organization – Set 1
• File Organization – Set 2 (Hashing in DBMS)
• File Organization – Set 3
• File Organization – Set 4

>> Quiz on File structures

• Query Optimization
• How to store a password in database?
• Storage Area Networks
• Network attached storage
• Data Warehousing
• Data Warehouse Architecture
• Characteristics and Functions of Data warehouse
• Difficulties of Implementing Data Warehouses
• Data Mining
• Data Mining | KDD process
• Data Mining | Sources of Data that can be mined
• ODBMS – Definition and overview
• Architecture of HBase
• Apache HBase
• Architecture and Working of Hive
• Apache Hive
• Difference between Hive and HBase
• Difference between RDBMS and HBase
• Challenges of database security
• Federated database management system issues
• Distributed Database System
• Functions of Distributed Database System
• Semantic Heterogeneity
• Advantages of Distributed database
• Comparison – Centralized, Decentralized and Distributed Systems
• Characteristics of Biological Data (Genome Data Management)
• Data Management issues in Mobile database
• Future Works in Geographic Information System
• Difference between Structured, Semi-structured and Unstructured data
• SQL | Tutorials
• Quiz on SQL

DBMS practices questions :

• Database Management Systems | Set 1
• Database Management Systems | Set 9
• Database Management Systems | Set 10
• Database Management Systems | Set 11

There are some following reasons to learn DBMS:

• Organizing and management of data: DBMS helps in managing large amounts of data in an organized manner. It provides features like create, edit, delete, and read.
• Data Security: DBMS provides Security to the data from the unauthorized person.
• Improved decision-making: From stored data in the database we can generate graphs, reports, and many visualizations which helps in decision-making.
• Consistency: In a traditional database model all things are manual or inconsistent, but DBMS enables to automation of the operations by queries.
• Complexity: DBMS can be hard to design, implement, and manage, needing specialized knowledge.
• Cost: High setup costs, including hardware, software, and skilled personnel, can be expensive. Ongoing maintenance adds to the cost.
• Performance Overhead: DBMS might slow down simple tasks due to their extra features and general-purpose nature.
• Security Risks: Centralizing data can create security risks. If the system is hacked, all data could be compromised.
• Resource Intensive: DBMS need a lot of memory, storage, and processing power, which can be costly.
• Data Integrity Issues: Complex systems can lead to data integrity problems if not managed well.

Understanding Database Management Systems (DBMS) is essential for managing and organizing data effectively. This DBMS tutorial has introduced you to key concepts like database models, SQL queries, normalization, and data security. With this knowledge, you can design efficient databases, maintain data integrity, and improve performance.

Database Management System(DBMS) – FAQs

Q.1 what is database.

A database is a collection of organized data which can easily be created, updated, accessed, and managed. Records are kept maintained in tables or objects. A tuple (row) represents a single entry in a table. DBMS manipulates data from the database in the form of queries given by the user.

Q.2 What are different languages present in DBMS?

DDL (Data Definition Language) : These are the collection of commands which are required to define the database. E.g., CREATE, ALTER, RENAME, TRUNCATE, DROP, etc. DML (Data Manipulation Language) : These are the collection of commands which are required to manipulate the data stored in a database. E.g., SELECT, UPDATE, INSERT, DELETE, etc. DCL (Data Control Language) : These are the collection of commands which are dealt with the user permissions and controls of the database system. E.g, GRANT, and REVOKE. TCL (Transaction Control Language) : These are the collection of commands which are required to deal with the transaction of the database. E.g., COMMIT, ROLLBACK, and SAVEPOINT.

Q.3 What are the ACID properties in DBMS?

The full form of ACID is Atomicity, Consistency, Isolation, and Durability these are the properties of DBMS that ensure a safe and secure way of sharing data among multiple users. A  – Atomic: All changes to the data must be performed successfully or not at all. C  – Consistent: Data must be in a consistent state before and after the transaction. I  – Isolated: No other process can change the data while the transaction is going on. D  – Durable: The changes made by a transaction must persist.

Q.4 What are the Advantages of DBMS?

The followings are the few advantages of DBMS : Data Sharing:  Data from the same database can be shared by multiple users at the same time. Integrity:  It allows the data stored in an organized and refined manner. Data Independence:  It allows changing the data structure without changing the composition of executing programs. Data Security:  DBMS comes with the tools to make the storage and transfer of databases secure and reliable. Authentication and encryption are the tools used in DBMS for data security.

• Last Minutes Notes(LMNs) on DBMS
• Quizzes on DBMS
• Practice Problems on DBMS
• DBMS interview questions | Set 1
• DBMS interview questions | Set 2

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The Plight of the Modern DBA

This month, we are going to take a high-level look into the state of modern database administration. If you read the title of this article, you most certainly noticed the word “plight.” That word was chosen specifically for the situation modern DBAs find themselves in. If you look up that term, you’ll see words such as “difficult,” “trying,” and “unpleasant” in the definition. If you do not agree that DBAs are finding themselves in a plight, perhaps you will by the time you read through this column.

The Current Landscape

Let’s start by taking a quick look at the current DBMS landscape that DBAs must administer. The database world is in tumult these days. There are new requirements and new capabilities that organizations are adopting and integrating into their data persistence infrastructure all the time. The world is no longer relational/SQL- only. Organizations are adopting NoSQL database systems to support specific use cases and types of workloads. This is increasing the complexity of how data is managed.

Another related trend is what Gartner refers to as hybrid transactional analytical processing (HTAP). This enables multiple engines within a single DBMS to deliver both transaction processing and analytics without requiring a separate DBMS for each. But DBAs still must understand how to administer and optimize for each type of engine.

Even with HTAP and multi-model database systems, most organizations run multiple DBMSs. Organizations, on average, have between 100 and 500 database instances running across multiple platforms and products. And these instances are not a single brand, or even data model, of database system.

Polyglot Persistance

This brings us to the term “polyglot persistence” that came about as part of the NoSQL movement. It simply means that you should use the right database platform for each requirement or use case instead of trying to force fit them all into a single DBMS. There are many different underlying technologies and models, and each type of NoSQL data platform has its own pros and cons as well as different use cases. The DBA must understand each of these to be successful.

But it is not just NoSQL that is driving organizations to run multiple DBMSs. Many organizations have more than one relational DBMS. They may run Db2 on the mainframe and Linux, Oracle on UNIX, and SQL Server on Windows, and perhaps have a few MySQL instances, too.

And DBAs are managing a lot of different database instances. A study conducted by Unisphere Research showed that more than one-fourth of DBAs oversee in excess of 100 database instances! And not all of those instances will be from the same vendor. Consider the sheer number of DBMSs and vendors out there these days.

The bottom line is that the multi-DBMS organization is a de facto standard, and it is likely to stay that way well into the future. This means that things are getting more and more heterogeneous and, therefore, more complex.

But there are many additional factors that contribute to the plight of the modern DBA when you examine the modern IT infrastructure and application development trends. Consider that all of the following trends are currently impacting data and data management: AI and machine learning, big data and data growth, cloud computing, data breaches, DevOps and agile, IoT, and regulatory compliance. DBAs need to be knowledgeable about all of them because they impact the way data is stored, accessed, managed, and analyzed.

Combine those with the ongoing trend of tight IT budgets, and it is easy to see how DBAs could have difficulty adapting their capabilities to support all of the changes, while at the same time dealing with performance issues, designing and managing change for database structures, supporting developers, managing backup and recovery, and ensuring data availability. It really doesn’t leave a lot of time for keeping up with the new skills required to do a good job as a DBA.

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Major US carriers restore some flight operations amid global cyber outage

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Reporting by Shivansh Tiwary, Maria Ponnezhath and Shivani Tanna ; Additional reporting by Chandni Shah, Nathan Gomes, Surbhi Mishra, Angela Christy and David Shepardson; writing by Abinaya Vijayaraghavan and Shubham Kalia; Editing by Nivedita Bhattacharjee and Sriraj Kalluvila

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Michigan state pension fund makes $6.6 million bitcoin ETF investment

The State of Michigan Retirement System, which oversees some $143.9 million in pension fund assets for state employees, has invested $6.6 million in the ARK 21Shares Bitcoin ETF , it disclosed in a regulatory filing on Friday.