assignment in conditional operator

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Conditional (ternary) operator

The conditional (ternary) operator is the only JavaScript operator that takes three operands: a condition followed by a question mark ( ? ), then an expression to execute if the condition is truthy followed by a colon ( : ), and finally the expression to execute if the condition is falsy . This operator is frequently used as an alternative to an if...else statement.

An expression whose value is used as a condition.

An expression which is executed if the condition evaluates to a truthy value (one which equals or can be converted to true ).

An expression which is executed if the condition is falsy (that is, has a value which can be converted to false ).

Description

Besides false , possible falsy expressions are: null , NaN , 0 , the empty string ( "" ), and undefined . If condition is any of these, the result of the conditional expression will be the result of executing the expression exprIfFalse .

A simple example

Handling null values.

One common usage is to handle a value that may be null :

Conditional chains

The ternary operator is right-associative, which means it can be "chained" in the following way, similar to an if … else if … else if … else chain:

This is equivalent to the following if...else chain.

Specifications

Browser compatibility.

BCD tables only load in the browser with JavaScript enabled. Enable JavaScript to view data.

• Nullish coalescing operator ( ?? )
• Optional chaining ( ?. )
• Making decisions in your code — conditionals
• Expressions and operators guide

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?: operator - the ternary conditional operator

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The conditional operator ?: , also known as the ternary conditional operator, evaluates a Boolean expression and returns the result of one of the two expressions, depending on whether the Boolean expression evaluates to true or false , as the following example shows:

As the preceding example shows, the syntax for the conditional operator is as follows:

The condition expression must evaluate to true or false . If condition evaluates to true , the consequent expression is evaluated, and its result becomes the result of the operation. If condition evaluates to false , the alternative expression is evaluated, and its result becomes the result of the operation. Only consequent or alternative is evaluated. Conditional expressions are target-typed. That is, if a target type of a conditional expression is known, the types of consequent and alternative must be implicitly convertible to the target type, as the following example shows:

If a target type of a conditional expression is unknown (for example, when you use the var keyword) or the type of consequent and alternative must be the same or there must be an implicit conversion from one type to the other:

The conditional operator is right-associative, that is, an expression of the form

is evaluated as

You can use the following mnemonic device to remember how the conditional operator is evaluated:

Conditional ref expression

A conditional ref expression conditionally returns a variable reference, as the following example shows:

You can ref assign the result of a conditional ref expression, use it as a reference return or pass it as a ref , out , in , or ref readonly method parameter . You can also assign to the result of a conditional ref expression, as the preceding example shows.

The syntax for a conditional ref expression is as follows:

Like the conditional operator, a conditional ref expression evaluates only one of the two expressions: either consequent or alternative .

In a conditional ref expression, the type of consequent and alternative must be the same. Conditional ref expressions aren't target-typed.

Conditional operator and an if statement

Use of the conditional operator instead of an if statement might result in more concise code in cases when you need conditionally to compute a value. The following example demonstrates two ways to classify an integer as negative or nonnegative:

Operator overloadability

A user-defined type can't overload the conditional operator.

C# language specification

For more information, see the Conditional operator section of the C# language specification .

Specifications for newer features are:

• Target-typed conditional expression
• Simplify conditional expression (style rule IDE0075)
• C# operators and expressions
• if statement
• ?. and ?[] operators
• ?? and ??= operators
• ref keyword

Coming soon: Throughout 2024 we will be phasing out GitHub Issues as the feedback mechanism for content and replacing it with a new feedback system. For more information see: https://aka.ms/ContentUserFeedback .

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Additional resources

Verilog Conditional Operator

Just what the heck is that question mark doing.

Have you ever come across a strange looking piece of Verilog code that has a question mark in the middle of it? A question mark in the middle of a line of code looks so bizarre; they’re supposed to go at the end of sentences! However in Verilog the ? operator is a very useful one, but it does take a bit of getting used to.

The question mark is known in Verilog as a conditional operator though in other programming languages it also is referred to as a ternary operator , an inline if , or a ternary if . It is used as a short-hand way to write a conditional expression in Verilog (rather than using if/else statements). Let’s look at how it is used:

Here, condition is the check that the code is performing. This condition might be things like, “Is the value in A greater than the value in B?” or “Is A=1?”. Depending on if this condition evaluates to true, the first expression is chosen. If the condition evaluates to false, the part after the colon is chosen. I wrote an example of this. The code below is really elegant stuff. The way I look at the question mark operator is I say to myself, “Tell me about the value in r_Check. If it’s true, then return “HI THERE” if it’s false, then return “POTATO”. You can also use the conditional operator to assign signals , as shown with the signal w_Test1 in the example below. Assigning signals with the conditional operator is useful!

Nested Conditional Operators

There are examples in which it might be useful to combine two or more conditional operators in a single assignment. Consider the truth table below. The truth table shows a 2-input truth table. You need to know the value of both r_Sel[1] and r_Sel[0] to determine the value of the output w_Out. This could be achieved with a bunch of if-else if-else if combinations, or a case statement, but it’s much cleaner and simpler to use the conditional operator to achieve the same goal.

Learn Verilog

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Java Tutorial

Control statements, java object class, java inheritance, java polymorphism, java abstraction, java encapsulation, java oops misc.

Conditional AND

The operator is applied between two Boolean expressions. It is denoted by the two AND operators (&&). It returns true if and only if both expressions are true, else returns false.

Conditional OR

The operator is applied between two Boolean expressions. It is denoted by the two OR operator (||). It returns true if any of the expression is true, else returns false.

Let's create a Java program and use the conditional operator.

ConditionalOperatorExample.java

Ternary Operator

The meaning of ternary is composed of three parts. The ternary operator (? :) consists of three operands. It is used to evaluate Boolean expressions. The operator decides which value will be assigned to the variable. It is the only conditional operator that accepts three operands. It can be used instead of the if-else statement. It makes the code much more easy, readable, and shorter.

Note: Every code using an if-else statement cannot be replaced with a ternary operator.

The above statement states that if the condition returns true, expression1 gets executed, else the expression2 gets executed and the final result stored in a variable.

Let's understand the ternary operator through the flowchart.

TernaryOperatorExample.java

Let's see another example that evaluates the largest of three numbers using the ternary operator.

LargestNumberExample.java

In the above program, we have taken three variables x, y, and z having the values 69, 89, and 79, respectively. The expression (x > y) ? (x > z ? x : z) : (y > z ? y : z) evaluates the largest number among three numbers and store the final result in the variable largestNumber. Let's understand the execution order of the expression.

First, it checks the expression (x > y) . If it returns true the expression (x > z ? x : z) gets executed, else the expression (y > z ? y : z) gets executed.

When the expression (x > z ? x : z) gets executed, it further checks the condition x > z . If the condition returns true the value of x is returned, else the value of z is returned.

When the expression (y > z ? y : z) gets executed it further checks the condition y > z . If the condition returns true the value of y is returned, else the value of z is returned.

Therefore, we get the largest of three numbers using the ternary operator.

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Microsoft fabric updates blog.

Microsoft Fabric May 2024 Update

• Monthly Update

Welcome to the May 2024 update.  

Here are a few, select highlights of the many we have for Fabric. You can now ask Copilot questions about data in your model, Model Explorer and authoring calculation groups in Power BI desktop is now generally available, and Real-Time Intelligence provides a complete end-to-end solution for ingesting, processing, analyzing, visualizing, monitoring, and acting on events.

There is much more to explore, please continue to read on. 

Microsoft Build Announcements

At Microsoft Build 2024, we are thrilled to announce a huge array of innovations coming to the Microsoft Fabric platform that will make Microsoft Fabric’s capabilities even more robust and even customizable to meet the unique needs of each organization. To learn more about these changes, read the “ Unlock real-time insights with AI-powered analytics in Microsoft Fabric ” announcement blog by Arun Ulag.

Fabric Roadmap Update

Last October at the Microsoft Power Platform Community Conference we  announced the release of the Microsoft Fabric Roadmap . Today we have updated that roadmap to include the next semester of Fabric innovations. As promised, we have merged Power BI into this roadmap to give you a single, unified road map for all of Microsoft Fabric. You can find the Fabric Roadmap at  https://aka.ms/FabricRoadmap .

We will be innovating our Roadmap over the coming year and would love to hear your recommendation ways that we can make this experience better for you. Please submit suggestions at  https://aka.ms/FabricIdeas .

Earn a discount on your Microsoft Fabric certification exam!  

We’d like to thank the thousands of you who completed the Fabric AI Skills Challenge and earned a free voucher for Exam DP-600 which leads to the Fabric Analytics Engineer Associate certification.   

If you earned a free voucher, you can find redemption instructions in your email. We recommend that you schedule your exam now, before your discount voucher expires on June 24 th . All exams must be scheduled and completed by this date.    

If you need a little more help with exam prep, visit the Fabric Career Hub which has expert-led training, exam crams, practice tests and more.  

Missed the Fabric AI Skills Challenge? We have you covered. For a limited time , you could earn a 50% exam discount by taking the Fabric 30 Days to Learn It Challenge .  

Modern Tooltip now on by Default

Matrix layouts, line updates, on-object interaction updates, publish to folders in public preview, you can now ask copilot questions about data in your model (preview), announcing general availability of dax query view, copilot to write and explain dax queries in dax query view public preview updates, new manage relationships dialog, refreshing calculated columns and calculated tables referencing directquery sources with single sign-on, announcing general availability of model explorer and authoring calculation groups in power bi desktop, microsoft entra id sso support for oracle database, certified connector updates, view reports in onedrive and sharepoint with live connected semantic models, storytelling in powerpoint – image mode in the power bi add-in for powerpoint, storytelling in powerpoint – data updated notification, git integration support for direct lake semantic models.

• Editor’s pick of the quarter
• New visuals in AppSource
• Financial Reporting Matrix by Profitbase
• Horizon Chart by Powerviz

Milestone Trend Analysis Chart by Nova Silva

• Sunburst Chart by Powerviz
• Stacked Bar Chart with Line by JTA

Fabric Automation

Streamlining fabric admin apis, microsoft fabric workload development kit, external data sharing, apis for onelake data access roles, shortcuts to on-premises and network-restricted data, copilot for data warehouse, unlocking insights through time: time travel in data warehouse, copy into enhancements, faster workspace resource assignment powered by just in time database attachment, runtime 1.3 (apache spark 3.5, delta lake 3.1, r 4.3.3, python 3.11) – public preview.

• Native Execution Engine for Fabric Runtime 1.2 (Apache Spark 3.4) – Public Preview 

Spark Run Series Analysis

Comment @tagging in notebook, notebook ribbon upgrade, notebook metadata update notification, environment is ga now, rest api support for workspace data engineering/science settings, fabric user data functions (private preview), introducing api for graphql in microsoft fabric (preview), copilot will be enabled by default, the ai and copilot setting will be automatically delegated to capacity admins, abuse monitoring no longer stores your data, real-time hub, source from real-time hub in enhanced eventstream, use real-time hub to get data in kql database in eventhouse, get data from real-time hub within reflexes, eventstream edit and live modes, default and derived streams, route streams based on content in enhanced eventstream, eventhouse is now generally available, eventhouse onelake availability is now generally available, create a database shortcut to another kql database, support for ai anomaly detector, copilot for real-time intelligence, eventhouse tenant level private endpoint support, visualize data with real-time dashboards, new experience for data exploration, create triggers from real-time hub, set alert on real-time dashboards, taking action through fabric items, general availability of the power query sdk for vs code, refresh the refresh history dialog, introducing data workflows in data factory, introducing trusted workspace access in fabric data pipelines.

• Introducing Blob Storage Event Triggers for Data Pipelines
• Parent/child pipeline pattern monitoring improvements

Fabric Spark job definition activity now available

Hd insight activity now available, modern get data experience in data pipeline.

Power BI tooltips are embarking on an evolution to enhance their functionality. To lay the groundwork, we are introducing the modern tooltip as the new default , a feature that many users may already recognize from its previous preview status. This change is more than just an upgrade; it’s the first step in a series of remarkable improvements. These future developments promise to revolutionize tooltip management and customization, offering possibilities that were previously only imaginable. As we prepare for the general availability of the modern tooltip, this is an excellent opportunity for users to become familiar with its features and capabilities. 

Discover the full potential of the new tooltip feature by visiting our dedicated blog . Dive into the details and explore the comprehensive vision we’ve crafted for tooltips, designed to enhance your Power BI experience. 

We’ve listened to our community’s feedback on improving our tabular visuals (Table and Matrix), and we’re excited to initiate their transformation. Drawing inspiration from the familiar PivotTable in Excel , we aim to build new features and capabilities upon a stronger foundation. In our May update, we’re introducing ‘ Layouts for Matrix .’ Now, you can select from compact , outline , or tabular layouts to alter the arrangement of components in a manner akin to Excel. 

As an extension of the new layout options, report creators can now craft custom layout patterns by repeating row headers. This powerful control, inspired by Excel’s PivotTable layout, enables the creation of a matrix that closely resembles the look and feel of a table. This enhancement not only provides greater flexibility but also brings a touch of Excel’s intuitive design to Power BI’s matrix visuals. Only available for Outline and Tabular layouts.

To further align with Excel’s functionality, report creators now have the option to insert blank rows within the matrix. This feature allows for the separation of higher-level row header categories, significantly enhancing the readability of the report. It’s a thoughtful addition that brings a new level of clarity and organization to Power BI’s matrix visuals and opens a path for future enhancements for totals/subtotals and rows/column headers. 

We understand your eagerness to delve deeper into the matrix layouts and grasp how these enhancements fulfill the highly requested features by our community. Find out more and join the conversation in our dedicated blog , where we unravel the details and share the community-driven vision behind these improvements. 

Following last month’s introduction of the initial line enhancements, May brings a groundbreaking set of line capabilities that are set to transform your Power BI experience: 

• Hide/Show lines : Gain control over the visibility of your lines for a cleaner, more focused report. 
• Customized line pattern : Tailor the pattern of your lines to match the style and context of your data. 
• Auto-scaled line pattern : Ensure your line patterns scale perfectly with your data, maintaining consistency and clarity. 
• Line dash cap : Customize the end caps of your customized dashed lines for a polished, professional look. 
• Line upgrades across other line types : Experience improvements in reference lines, forecast lines, leader lines, small multiple gridlines, and the new card’s divider line. 

These enhancements are not to be missed. We recommend visiting our dedicated blog for an in-depth exploration of all the new capabilities added to lines, keeping you informed and up to date. 

This May release, we’re excited to introduce on-object formatting support for Small multiples , Waterfall , and Matrix visuals. This new feature allows users to interact directly with these visuals for a more intuitive and efficient formatting experience. By double-clicking on any of these visuals, users can now right-click on the specific visual component they wish to format, bringing up a convenient mini-toolbar. This streamlined approach not only saves time but also enhances the user’s ability to customize and refine their reports with ease. 

We’re also thrilled to announce a significant enhancement to the mobile reporting experience with the introduction of the pane manager for the mobile layout view. This innovative feature empowers users to effortlessly open and close panels via a dedicated menu, streamlining the design process of mobile reports. 

We recently announced a public preview for folders in workspaces, allowing you to create a hierarchical structure for organizing and managing your items. In the latest Desktop release, you can now publish your reports to specific folders in your workspace.  

When you publish a report, you can choose the specific workspace and folder for your report. The interface is simplistic and easy to understand, making organizing your Power BI content from Desktop better than ever. 

To publish reports to specific folders in the service, make sure the “Publish dialogs support folder selection” setting is enabled in the Preview features tab in the Options menu. 

Learn more about folders in workspaces.   

We’re excited to preview a new capability for Power BI Copilot allowing you to ask questions about the data in your model! You could already ask questions about the data present in the visuals on your report pages – and now you can go deeper by getting answers directly from the underlying model. Just ask questions about your data, and if the answer isn’t already on your report, Copilot will then query your model for the data instead and return the answer to your question in the form of a visual! 

We’re starting this capability off in both Edit and View modes in Power BI Service. Because this is a preview feature, you’ll need to enable it via the preview toggle in the Copilot pane. You can learn more about all the details of the feature in our announcement post here! (will link to announcement post)  

We are excited to announce the general availability of DAX query view. DAX query view is the fourth view in Power BI Desktop to run DAX queries on your semantic model.  

DAX query view comes with several ways to help you be as productive as possible with DAX queries. 

• Quick queries. Have the DAX query written for you from the context menu of tables, columns, or measures in the Data pane of DAX query view. Get the top 100 rows of a table, statistics of a column, or DAX formula of a measure to edit and validate in just a couple clicks! 
• DirectQuery model authors can also use DAX query view. View the data in your tables whenever you want! 
• Create and edit measures. Edit one or multiple measures at once. Make changes and see the change in action in a DA query. Then update the model when you are ready. All in DAX query view! 
• See the DAX query of visuals. Investigate the visuals DAX query in DAX query view. Go to the Performance Analyzer pane and choose “Run in DAX query view”. 
• Write DAX queries. You can create DAX queries with Intellisense, formatting, commenting/uncommenting, and syntax highlighting. And additional professional code editing experiences such as “Change all occurrences” and block folding to expand and collapse sections. Even expanded find and replace options with regex. 

Learn more about DAX query view with these resources: 

• Deep dive blog: https://powerbi.microsoft.com/blog/deep-dive-into-dax-query-view-and-writing-dax-queries/  
• Learn more: https://learn.microsoft.com/power-bi/transform-model/dax-query-view  
• Video: https://youtu.be/oPGGYLKhTOA?si=YKUp1j8GoHHsqdZo  

DAX query view includes an inline Fabric Copilot to write and explain DAX queries, which remains in public preview. This month we have made the following updates. 

• Run the DAX query before you keep it . Previously the Run button was disabled until the generated DAX query was accepted or Copilot was closed. Now you can Run the DAX query then decide to Keep or Discard the DAX query. 

2. Conversationally build the DAX query. Previously the DAX query generated was not considered if you typed additional prompts and you had to keep the DAX query, select it again, then use Copilot again to adjust. Now you can simply adjust by typing in additional user prompts.   

3. Syntax checks on the generated DAX query. Previously there was no syntax check before the generated DAX query was returned. Now the syntax is checked, and the prompt automatically retried once. If the retry is also invalid, the generated DAX query is returned with a note that there is an issue, giving you the option to rephrase your request or fix the generated DAX query. 

4. Inspire buttons to get you started with Copilot. Previously nothing happened until a prompt was entered. Now click any of these buttons to quickly see what you can do with Copilot! 

Learn more about DAX queries with Copilot with these resources: 

• Deep dive blog: https://powerbi.microsoft.com/en-us/blog/deep-dive-into-dax-query-view-with-copilot/  
• Learn more: https://learn.microsoft.com/en-us/dax/dax-copilot  
• Video: https://www.youtube.com/watch?v=0kE3TE34oLM  

We are excited to introduce you to the redesigned ‘Manage relationships’ dialog in Power BI Desktop! To open this dialog simply select the ‘Manage relationships’ button in the modeling ribbon.

Once opened, you’ll find a comprehensive view of all your relationships, along with their key properties, all in one convenient location. From here you can create new relationships or edit an existing one.

Additionally, you have the option to filter and focus on specific relationships in your model based on cardinality and cross filter direction. 

Learn more about creating and managing relationships in Power BI Desktop in our documentation . 

Ever since we released composite models on Power BI semantic models and Analysis Services , you have been asking us to support the refresh of calculated columns and tables in the Service. This month, we have enabled the refresh of calculated columns and tables in Service for any DirectQuery source that uses single sign-on authentication. This includes the sources you use when working with composite models on Power BI semantic models and Analysis Services.  

Previously, the refresh of a semantic model that uses a DirectQuery source with single-sign-on authentication failed with one of the following error messages: “Refresh is not supported for datasets with a calculated table or calculated column that depends on a table which references Analysis Services using DirectQuery.” or “Refresh over a dataset with a calculated table or a calculated column which references a Direct Query data source is not supported.” 

Starting today, you can successfully refresh the calculated table and calculated columns in a semantic model in the Service using specific credentials as long as: 

• You used a shareable cloud connection and assigned it and/or.
• Enabled granular access control for all data connection types.

Here’s how to do this: 

• Create and publish your semantic model that uses a single sign-on DirectQuery source. This can be a composite model but doesn’t have to be. 
• In the semantic model settings, under Gateway and cloud connections , map each single sign-on DirectQuery connection to a specific connection. If you don’t have a specific connection yet, select ‘Create a connection’ to create it: 

• If you are creating a new connection, fill out the connection details and click Create , making sure to select ‘Use SSO via Azure AD for DirectQuery queries: 

• Finally, select the connection for each single sign-on DirectQuery source and select Apply : 

2. Either refresh the semantic model manually or plan a scheduled refresh to confirm the refresh now works successfully. Congratulations, you have successfully set up refresh for semantic models with a single sign-on DirectQuery connection that uses calculated columns or calculated tables!

We are excited to announce the general availability of Model Explorer in the Model view of Power BI, including the authoring of calculation groups. Semantic modeling is even easier with an at-a-glance tree view with item counts, search, and in context paths to edit the semantic model items with Model Explorer. Top level semantic model properties are also available as well as the option to quickly create relationships in the properties pane. Additionally, the styling for the Data pane is updated to Fluent UI also used in Office and Teams.  

A popular community request from the Ideas forum, authoring calculation groups is also included in Model Explorer. Calculation groups significantly reduce the number of redundant measures by allowing you to define DAX formulas as calculation items that can be applied to existing measures. For example, define a year over year, prior month, conversion, or whatever your report needs in DAX formula once as a calculation item and reuse it with existing measures. This can reduce the number of measures you need to create and make the maintenance of the business logic simpler.  

Available in both Power BI Desktop and when editing a semantic model in the workspace, take your semantic model authoring to the next level today!  

Learn more about Model Explorer and authoring calculation groups with these resources: 

• Use Model explorer in Power BI (preview) – Power BI | Microsoft Learn  
• Create calculation groups in Power BI (preview) – Power BI | Microsoft Learn  

Data connectivity  

We’re happy to announce that the Oracle database connector has been enhanced this month with the addition of Single Sign-On support in the Power BI service with Microsoft Entra ID authentication.  

Microsoft Entra ID SSO enables single sign-on to access data sources that rely on Microsoft Entra ID based authentication. When you configure Microsoft Entra SSO for an applicable data source, queries run under the Microsoft Entra identity of the user that interacts with the Power BI report. 

We’re pleased to announce the new and updated connectors in this release:   

• [New] OneStream : The OneStream Power BI Connector enables you to seamlessly connect Power BI to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category. 
• [New] Zendesk Data : A new connector developed by the Zendesk team that aims to go beyond the functionality of the existing Zendesk legacy connector created by Microsoft. Learn more about what this new connector brings. 
• [New] CCH Tagetik 
• [Update] Azure Databricks  

Are you interested in creating your own connector and publishing it for your customers? Learn more about the Power Query SDK and the Connector Certification program .   

Last May, we announced the integration between Power BI and OneDrive and SharePoint. Previously, this capability was limited to only reports with data in import mode. We’re excited to announce that you can now seamlessly view Power BI reports with live connected data directly in OneDrive and SharePoint! 

When working on Power BI Desktop with a report live connected to a semantic model in the service, you can easily share a link to collaborate with others on your team and allow them to quickly view the report in their browser. We’ve made it easier than ever to access the latest data updates without ever leaving your familiar OneDrive and SharePoint environments. This integration streamlines your workflows and allows you to access reports within the platforms you already use. With collaboration at the heart of this improvement, teams can work together more effectively to make informed decisions by leveraging live connected semantic models without being limited to data only in import mode.  

Utilizing OneDrive and SharePoint allows you to take advantage of built-in version control, always have your files available in the cloud, and utilize familiar and simplistic sharing.  

While you told us that you appreciate the ability to limit the image view to only those who have permission to view the report, you asked for changes for the “Public snapshot” mode.   

To address some of the feedback we got from you, we have made a few more changes in this area.  

• Add-ins that were saved as “Public snapshot” can be printed and will not require that you go over all the slides and load the add-ins for permission check before the public image is made visible. 
• You can use the “Show as saved image” on add-ins that were saved as “Public snapshot”. This will replace the entire add-in with an image representation of it, so the load time might be faster when you are presenting your presentation. 

Many of us keep presentations open for a long time, which might cause the data in the presentation to become outdated.  

To make sure you have in your slides the data you need, we added a new notification that tells you if more up to date data exists in Power BI and offers you the option to refresh and get the latest data from Power BI. 

Developers 

Direct Lake semantic models are now supported in Fabric Git Integration , enabling streamlined version control, enhanced collaboration among developers, and the establishment of CI/CD pipelines for your semantic models using Direct Lake. 

Learn more about version control, testing, and deployment of Power BI content in our Power BI implementation planning documentation: https://learn.microsoft.com/power-bi/guidance/powerbi-implementation-planning-content-lifecycle-management-overview  

Visualizations 

Editor’s pick of the quarter .

– Animator for Power BI     Innofalls Charts     SuperTables     Sankey Diagram for Power BI by ChartExpo     Dynamic KPI Card by Sereviso     Shielded HTML Viewer     Text search slicer  

New visuals in AppSource 

Mapa Polski – Województwa, Powiaty, Gminy   Workstream   Income Statement Table  

Gas Detection Chart  

Seasonality Chart   PlanIn BI – Data Refresh Service  

Chart Flare  

PictoBar   ProgBar  

Counter Calendar   Donut Chart image  

Financial Reporting Matrix by Profitbase 

Making financial statements with a proper layout has just become easier with the latest version of the Financial Reporting Matrix. 

Users are now able to specify which rows should be classified as cost-rows, which will make it easier to get the conditional formatting of variances correctly: 

Selecting a row, and ticking “is cost” will tag the row as cost. This can be used in conditional formatting to make sure that positive variances on expenses are a bad for the result, while a positive variance on an income row is good for the result. 

The new version also includes more flexibility in measuring placement and column subtotals. 

Measures can be placed either: 

• Default (below column headers) 
• Above column headers 

• Conditionally hide columns 
• + much more 

Highlighted new features:  

• Measure placement – In rows  
• Select Column Subtotals  
• New Format Pane design 
• Row Options  

Get the visual from AppSource and find more videos here ! 

Horizon Chart by Powerviz  

A Horizon Chart is an advanced visual, for time-series data, revealing trends and anomalies. It displays stacked data layers, allowing users to compare multiple categories while maintaining data clarity. Horizon Charts are particularly useful to monitor and analyze complex data over time, making this a valuable visual for data analysis and decision-making. 

Key Features:  

• Horizon Styles: Choose Natural, Linear, or Step with adjustable scaling. 
• Layer: Layer data by range or custom criteria. Display positive and negative values together or separately on top. 
• Reference Line : Highlight patterns with X-axis lines and labels. 
• Colors: Apply 30+ color palettes and use FX rules for dynamic coloring. 
• Ranking: Filter Top/Bottom N values, with “Others”. 
• Gridline: Add gridlines to the X and Y axis.  
• Custom Tooltip: Add highest, lowest, mean, and median points without additional DAX. 
• Themes: Save designs and share seamlessly with JSON files. 

Other features included are ranking, annotation, grid view, show condition, and accessibility support.  

Business Use Cases: Time-Series Data Comparison, Environmental Monitoring, Anomaly Detection 

🔗 Try Horizon Chart for FREE from AppSource  

📊 Check out all features of the visual: Demo file  

📃 Step-by-step instructions: Documentation  

💡 YouTube Video: Video Link  

📍 Learn more about visuals: https://powerviz.ai/  

✅ Follow Powerviz : https://lnkd.in/gN_9Sa6U  

Exciting news! Thanks to your valuable feedback, we’ve enhanced our Milestone Trend Analysis Chart even further. We’re thrilled to announce that you can now switch between horizontal and vertical orientations, catering to your preferred visualization style.

The Milestone Trend Analysis (MTA) Chart remains your go-to tool for swiftly identifying deadline trends, empowering you to take timely corrective actions. With this update, we aim to enhance deadline awareness among project participants and stakeholders alike. 

In our latest version, we seamlessly navigate between horizontal and vertical views within the familiar Power BI interface. No need to adapt to a new user interface – enjoy the same ease of use with added flexibility. Plus, it benefits from supported features like themes, interactive selection, and tooltips. 

What’s more, ours is the only Microsoft Certified Milestone Trend Analysis Chart for Power BI, ensuring reliability and compatibility with the platform. 

Ready to experience the enhanced Milestone Trend Analysis Chart? Download it from AppSource today and explore its capabilities with your own data – try for free!  

We welcome any questions or feedback at our website: https://visuals.novasilva.com/ . Try it out and elevate your project management insights now! 

Sunburst Chart by Powerviz  

Powerviz’s Sunburst Chart is an interactive tool for hierarchical data visualization. With this chart, you can easily visualize multiple columns in a hierarchy and uncover valuable insights. The concentric circle design helps in displaying part-to-whole relationships. 

• Arc Customization: Customize shapes and patterns. 
• Color Scheme: Accessible palettes with 30+ options. 
• Centre Circle: Design an inner circle with layers. Add text, measure, icons, and images. 
• Conditional Formatting: Easily identify outliers based on measure or category rules. 
• Labels: Smart data labels for readability. 
• Image Labels: Add an image as an outer label. 
• Interactivity: Zoom, drill down, cross-filtering, and tooltip features. 

Other features included are annotation, grid view, show condition, and accessibility support.  

Business Use Cases:   

• Sales and Marketing: Market share analysis and customer segmentation. 
• Finance : Department budgets and expenditures distribution. 
• Operations : Supply chain management. 
• Education : Course structure, curriculum creation. 
• Human Resources : Organization structure, employee demographics.

🔗 Try Sunburst Chart for FREE from AppSource  

Stacked Bar Chart with Line by JTA  

Clustered bar chart with the possibility to stack one of the bars  

Stacked Bar Chart with Line by JTA seamlessly merges the simplicity of a traditional bar chart with the versatility of a stacked bar, revolutionizing the way you showcase multiple datasets in a single, cohesive display. 

Unlocking a new dimension of insight, our visual features a dynamic line that provides a snapshot of data trends at a glance. Navigate through your data effortlessly with multiple configurations, gaining a swift and comprehensive understanding of your information. 

Tailor your visual experience with an array of functionalities and customization options, enabling you to effortlessly compare a primary metric with the performance of an entire set. The flexibility to customize the visual according to your unique preferences empowers you to harness the full potential of your data. 

Features of Stacked Bar Chart with Line:  

• Stack the second bar 
• Format the Axis and Gridlines 
• Add a legend 
• Format the colors and text 
• Add a line chart 
• Format the line 
• Add marks to the line 
• Format the labels for bars and line 

If you liked what you saw, you can try it for yourself and find more information here . Also, if you want to download it, you can find the visual package on the AppSource . 

We have added an exciting new feature to our Combo PRO, Combo Bar PRO, and Timeline PRO visuals – Legend field support . The Legend field makes it easy to visually split series values into smaller segments, without the need to use measures or create separate series. Simply add a column with category names that are adjacent to the series values, and the visual will do the following:  

• Display separate segments as a stack or cluster, showing how each segment contributed to the total Series value. 
• Create legend items for each segment to quickly show/hide them without filtering.  
• Apply custom fill colors to each segment.  
• Show each segment value in the tooltip 

Read more about the Legend field on our blog article  

Drill Down Combo PRO is made for creators who want to build visually stunning and user-friendly reports. Cross-chart filtering and intuitive drill down interactions make data exploration easy and fun for any user. Furthermore, you can choose between three chart types – columns, lines, or areas; and feature up to 25 different series in the same visual and configure each series independently.  

📊 Get Drill Down Combo PRO on AppSource  

🌐 Visit Drill Down Combo PRO product page  

Documentation | ZoomCharts Website | Follow ZoomCharts on LinkedIn  

We are thrilled to announce that Fabric Core REST APIs are now generally available! This marks a significant milestone in the evolution of Microsoft Fabric, a platform that has been meticulously designed to empower developers and businesses alike with a comprehensive suite of tools and services. 

The Core REST APIs are the backbone of Microsoft Fabric, providing the essential building blocks for a myriad of functionalities within the platform. They are designed to improve efficiency, reduce manual effort, increase accuracy, and lead to faster processing times. These APIs help with scale operations more easily and efficiently as the volume of work grows, automate repeatable processes with consistency, and enable integration with other systems and applications, providing a streamlined and efficient data pipeline. 

The Microsoft Fabric Core APIs encompasses a range of functionalities, including: 

• Workspace management: APIs to manage workspaces, including permissions.  
• Item management: APIs for creating, reading, updating, and deleting items, with partial support for data source discovery and granular permissions management planned for the near future. 
• Job and tenant management: APIs to manage jobs, tenants, and users within the platform. 

These APIs adhere to industry standards and best practices, ensuring a unified developer experience that is both coherent and easy to use. 

For developers looking to dive into the details of the Microsoft Fabric Core APIs, comprehensive documentation is available. This includes guidelines on API usage, examples, and articles managed in a centralized repository for ease of access and discoverability. The documentation is continuously updated to reflect the latest features and improvements, ensuring that developers have the most current information at their fingertips. See Microsoft Fabric REST API documentation  

We’re excited to share an important update we made to the Fabric Admin APIs. This enhancement is designed to simplify your automation experience. Now, you can manage both Power BI and the new Fabric items (previously referred to as artifacts) using the same set of APIs. Before this enhancement, you had to navigate using two different APIs—one for Power BI items and another for new Fabric items. That’s no longer the case. 

The APIs we’ve updated include GetItem , ListItems , GetItemAccessDetails , and GetAccessEntities . These enhancements mean you can now query and manage all your items through a single API call, regardless of whether they’re Fabric types or Power BI types. We hope this update makes your work more straightforward and helps you accomplish your tasks more efficiently. 

We’re thrilled to announce the public preview of the Microsoft Fabric workload development kit. This feature now extends to additional workloads and offers a robust developer toolkit for designing, developing, and interoperating with Microsoft Fabric using frontend SDKs and backend REST APIs. Introducing the Microsoft Fabric Workload Development Kit . 

The Microsoft Fabric platform now provides a mechanism for ISVs and developers to integrate their new and existing applications natively into Fabric’s workload hub. This integration provides the ability to add net new capabilities to Fabric in a consistent experience without leaving their Fabric workspace, thereby accelerating data driven outcomes from Microsoft Fabric. 

By downloading and leveraging the development kit , ISVs and software developers can build and scale existing and new applications on Microsoft Fabric and offer them via the Azure Marketplace without the need to ever leave the Fabric environment. 

The development kit provides a comprehensive guide and sample code for creating custom item types that can be added to the Fabric workspace. These item types can leverage the Fabric frontend SDKs and backend REST APIs to interact with other Fabric capabilities, such as data ingestion, transformation, orchestration, visualization, and collaboration. You can also embed your own data application into the Fabric item editor using the Fabric native experience components, such as the header, toolbar, navigation pane, and status bar. This way, you can offer consistent and seamless user experience across different Fabric workloads. 

This is a call to action for ISVs, software developers, and system integrators. Let’s leverage this opportunity to create more integrated and seamless experiences for our users. 

We’re excited about this journey and look forward to seeing the innovative workloads from our developer community. 

We are proud to announce the public preview of external data sharing. Sharing data across organizations has become a standard part of day-to-day business for many of our customers. External data sharing, built on top of OneLake shortcuts, enables seamless, in-place sharing of data, allowing you to maintain a single copy of data even when sharing data across tenant boundaries. Whether you’re sharing data with customers, manufacturers, suppliers, consultants, or partners; the applications are endless. 

How external data sharing works  

Sharing data across tenants is as simple as any other share operation in Fabric. To share data, navigate to the item to be shared, click on the context menu, and then click on External data share . Select the folder or table you want to share and click Save and continue . Enter the email address and an optional message and then click Send . 

The data consumer will receive an email containing a share link. They can click on the link to accept the share and access the data within their own tenant. 

Click here for more details about external data sharing . 

Following the release of OneLake data access roles in public preview, the OneLake team is excited to announce the availability of APIs for managing data access roles. These APIs can be used to programmatically manage granular data access for your lakehouses. Manage all aspects of role management such as creating new roles, editing existing ones, or changing memberships in a programmatic way.  

Check out the API reference to learn more. 

Do you have data stored on-premises or behind a firewall that you want to access and analyze with Microsoft Fabric? With OneLake shortcuts, you can bring on-premises or network-restricted data into OneLake, without any data movement or duplication. Simply install the Fabric on-premises data gateway and create a shortcut to your S3 compatible, Amazon S3, or Google Cloud Storage data source. Then use any of Fabric’s powerful analytics engines and OneLake open APIs to explore, transform, and visualize your data in the cloud. 

Try it out today and unlock the full potential of your data with OneLake shortcuts! 

Data Warehouse 

We are excited to announce Copilot for Data Warehouse in public preview! Copilot for Data Warehouse is an AI assistant that helps developers generate insights through T-SQL exploratory analysis. Copilot is contextualized your warehouse’s schema. With this feature, data engineers and data analysts can use Copilot to: 

• Generate T-SQL queries for data analysis.  
• Explain and add in-line code comments for existing T-SQL queries. 
• Fix broken T-SQL code. 
• Receive answers regarding general data warehousing tasks and operations. 

There are 3 areas where Copilot is surfaced in the Data Warehouse SQL Query Editor: 

• Code completions when writing a T-SQL query. 
• Chat panel to interact with the Copilot in natural language. 
• Quick action buttons to fix and explain T-SQL queries. 

Learn more about Copilot for Data Warehouse: aka.ms/data-warehouse-copilot-docs. Copilot for Data Warehouse is currently only available in the Warehouse. Copilot in the SQL analytics endpoint is coming soon. 

As data volumes continue to grow in today’s rapidly evolving world of Artificial Intelligence, it is crucial to reflect on historical data. It empowers businesses to derive valuable insights that aid in making well-informed decisions for the future. Preserving multiple historical data versions not only incurred significant costs but also presented challenges in upholding data integrity, resulting in a notable impact on query performance. So, we are thrilled to announce the ability to query the historical data through time travel at the T-SQL statement level which helps unlock the evolution of data over time. 

The Fabric warehouse retains historical versions of tables for seven calendar days. This retention allows for querying the tables as if they existed at any point within the retention timeframe. Time travel clause can be included in any top level SELECT statement. For complex queries that involve multiple tables, joins, stored procedures, or views, the timestamp is applied just once for the entire query instead of specifying the same timestamp for each table within the same query. This ensures the entire query is executed with reference to the specified timestamp, maintaining the data’s uniformity and integrity throughout the query execution. 

From historical trend analysis and forecasting to compliance management, stable reporting and real-time decision support, the benefits of time travel extend across multiple business operations. Embrace the capability of time travel to navigate the data-driven landscape and gain a competitive edge in today’s fast-paced world of Artificial Intelligence. 

We are excited to announce not one but two new enhancements to the Copy Into feature for Fabric Warehouse: Copy Into with Entra ID Authentication and Copy Into for Firewall-Enabled Storage!

Entra ID Authentication  

When authenticating storage accounts in your environment, the executing user’s Entra ID will now be used by default. This ensures that you can leverage A ccess C ontrol L ists and R ole – B ased a ccess c ontrol to authenticate to your storage accounts when using Copy Into. Currently, only organizational accounts are supported.  

How to Use Entra ID Authentication  

• Ensure your Entra ID organizational account has access to the underlying storage and can execute the Copy Into statement on your Fabric Warehouse.  
• Run your Copy Into statement without specifying any credentials; the Entra ID organizational account will be used as the default authentication mechanism.  

Copy into firewall-enabled storage

The Copy Into for firewall-enabled storage leverages the trusted workspace access functionality ( Trusted workspace access in Microsoft Fabric (preview) – Microsoft Fabric | Microsoft Learn ) to establish a secure and seamless connection between Fabric and your storage accounts. Secure access can be enabled for both blob and ADLS Gen2 storage accounts. Secure access with Copy Into is available for warehouses in workspaces with Fabric Capacities (F64 or higher).  

To learn more about Copy into , please refer to COPY INTO (Transact-SQL) – Azure Synapse Analytics and Microsoft Fabric | Microsoft Learn  

We are excited to announce the launch of our new feature, Just in Time Database Attachment, which will significantly enhance your first experience, such as when connecting to the Datawarehouse or SQL endpoint or simply opening an item. These actions trigger the workspace resource assignment process, where, among other actions, we attach all necessary metadata of your items, Data warehouses and SQL endpoints, which can be a long process, particularly for workspaces that have a high number of items.  

This feature is designed to attach your desired database during the activation process of your workspace, allowing you to execute queries immediately and avoid unnecessary delays. However, all other databases will be attached asynchronously in the background while you are able to execute queries, ensuring a smooth and efficient experience. 

Data Engineering 

We are advancing Fabric Runtime 1.3 from an Experimental Public Preview to a full Public Preview. Our Apache Spark-based big data execution engine, optimized for both data engineering and science workflows, has been updated and fully integrated into the Fabric platform. 

The enhancements in Fabric Runtime 1.3 include the incorporation of Delta Lake 3.1, compatibility with Python 3.11, support for Starter Pools, integration with Environment and library management capabilities. Additionally, Fabric Runtime now enriches the data science experience by supporting the R language and integrating Copilot. 

Native Execution Engine for Fabric Runtime 1.2 (Apache Spark 3.4) – Public Preview – eskot 

We are pleased to share that the Native Execution Engine for Fabric Runtime 1.2 is currently available in public preview. The Native Execution Engine can greatly enhance the performance for your Spark jobs and queries. The engine has been rewritten in C++ and operates in columnar mode and uses vectorized processing. The Native Execution Engine offers superior query performance – encompassing data processing, ETL, data science, and interactive queries – all directly on your data lake. Overall, Fabric Saprk delivers a 4x speed-up on the sum of execution time of all 99 queries in the TPC-DS 1TB benchmark when compared against Apache Spark.  This engine is fully compatible with Apache Spark™ APIs (including Spark SQL API). 

It is seamless to use with no code changes – activate it and go. Enable it in your environment for your notebooks and your SJDs. 

This feature is in the public preview, at this stage of the preview, there is no additional cost associated with using it. 

We are excited to announce the Spark Monitoring Run Series Analysis features, which allow you to analyze the run duration trend and performance comparison for Pipeline Spark activity recurring run instances and repetitive Spark run activities from the same Notebook or Spark Job Definition.   

• Run Series Comparison: Users can compare the duration of a Notebook run with that of previous runs and evaluate the input and output data to understand the reasons behind prolonged run durations.  
• Outlier Detection and Analysis: The system can detect outliers in the run series and analyze them to pinpoint potential contributing factors. 
• Detailed Run Instance Analysis: Clicking on a specific run instance provides detailed information on time distribution, which can be used to identify performance enhancement opportunities. 
• Configuration Insights : Users can view the Spark configuration used for each run, including auto-tuned configurations for Spark SQL queries in auto-tune enabled Notebook runs. 

You can access the new feature from the item’s recent runs panel and Spark application monitoring page. 

We are excited to announce that Notebook now supports the ability to tag others in comments, just like the familiar functionality of using Office products!   

When you select a section of code in a cell, you can add a comment with your insights and tag one or more teammates to collaborate or brainstorm on the specifics. This intuitive enhancement is designed to amplify collaboration in your daily development work. 

Moreover, you can easily configure the permissions when tagging someone who doesn’t have the permission, to make sure your code asset is well managed. 

We are thrilled to unveil a significant enhancement to the Fabric notebook ribbon, designed to elevate your data science and engineering workflows. 

In the new version, you will find the new Session connect control on the Home tab, and now you can start a standard session without needing to run a code cell. 

You can also easily spin up a High concurrency session and share the session across multiple notebooks to improve the compute resource utilization. And you can easily attach/leave a high concurrency session with a single click. 

The “ View session information ” can navigate you to the session information dialog, where you can find a lot of useful detailed information, as well as configure the session timeout. The diagnostics info is essentially helpful when you need support for notebook issues. 

Now you can easily access the powerful “ Data Wrangler ” on Home tab with the new ribbon! You can explore your data with the fancy low-code experience of data wrangler, and the pandas DataFrames and Spark DataFrames are all supported.   

We recently made some changes to the Fabric notebook metadata to ensure compliance and consistency: 

Notebook file content: 

• The keyword “trident” has been replaced with “dependencies” in the notebook content. This adjustment ensures consistency and compliance. 
• Notebook Git format: 
• The preface of the notebook has been modified from “# Synapse Analytics notebook source” to “# Fabric notebook source”. 
• Additionally, the keyword “synapse” has been updated to “dependencies” in the Git repo. 

The above changes will be marked as ‘uncommitted’ for one time if your workspace is connected to Git. No action is needed in terms of these changes , and there won’t be any breaking scenario within the Fabric platform . If you have any further updates or questions, feel free to share with us. 

We are thrilled to announce that the environment is now a generally available item in Microsoft Fabric. During this GA timeframe, we have shipped a few new features of Environment. 

• Git support  

The environment is now Git supported. You can check-in the environment into your Git repo and manipulate the environment locally with its YAML representations and custom library files. After updating the changes from local to Fabric portal, you can publish them by manual action or through REST API. 

• Deployment pipeline  

Deploying environments from one workspace to another is supported.  Now, you can deploy the code items and their dependent environments together from development to test and even production. 

With the REST APIs, you can have the code-first experience with the same abilities through Fabric portal. We provide a set of powerful APIs to ensure you the efficiency in managing your environment. You can create new environments, update libraries and Spark compute, publish the changes, delete an environment, attach the environment to a notebook, etc., all actions can be done locally in the tools of your choice. The article – Best practice of managing environments with REST API could help you get started with several real-world scenarios.  

• Resources folder   

Resources folder enables managing small resources in the development cycle. The files uploaded in the environment can be accessed from notebooks once they’re attached to the same environment. The manipulation of the files and folders of resources happens in real-time. It could be super powerful, especially when you are collaborating with others. 

Sharing your environment with others is also available. We provide several sharing options. By default, the view permission is shared. If you want the recipient to have access to view and use the contents of the environment, sharing without permission customization is the best option. Furthermore, you can grant editing permission to allow recipients to update this environment or grant share permission to allow recipients to reshare this environment with their existing permissions. 

We are excited to announce the REST api support for Fabric Data Engineering/Science workspace settings.  Data Engineering/Science settings allows users to create/manage their Spark compute, select the default runtime/default environment, enable or disable high concurrency mode or ML autologging.  

Now with the REST api support for the Data Engineering/Science settings, you would be able to  

• Choose the default pool for a Fabric Workspace 
• Configure the max nodes for Starter pools 
• Create/Update/Delete the existing Custom Pools, Autoscale and Dynamic allocation properties  
• Choose Workspace Default Runtime and Environment  
• Select a default runtime 
• Select the default environment for the Fabric workspace  
• Enable or Disable High Concurrency Mode 
• Enable or Disable ML Auto logging.  

Learn more about the Workspace Spark Settings API in our API documentation Workspace Settings – REST API (Spark) | Microsoft Learn  

We are excited to give you a sneak peek at the preview of User Data Functions in Microsoft Fabric. User Data Functions gives developers and data engineers the ability to easily write and run applications that integrate with resources in the Fabric Platform. Data engineering often presents challenges with data quality or complex data analytics processing in data pipelines, and using ETL tools may present limited flexibility and ability to customize to your needs. This is where User data functions can be used to run data transformation tasks and perform complex business logic by connecting to your data sources and other workloads in Fabric.  

During preview, you will be able to use the following features:  

• Use the Fabric portal to create new User Data Functions, view and test them.  
• Write your functions using C#.   
• Use the Visual Studio Code extension to create and edit your functions.  
• Connect to the following Fabric-native data sources: Data Warehouse, Lakehouse and Mirrored Databases.   

You can now create a fully managed GraphQL API in Fabric to interact with your data in a simple, flexible, and powerful way. We’re excited to announce the public preview of API for GraphQL, a data access layer that allows us to query multiple data sources quickly and efficiently in Fabric by leveraging a widely adopted and familiar API technology that returns more data with less client requests.  With the new API for GraphQL in Fabric, data engineers and scientists can create data APIs to connect to different data sources, use the APIs in their workflows, or share the API endpoints with app development teams to speed up and streamline data analytics application development in your business. 

You can get started with the API for GraphQL in Fabric by creating an API, attaching a supported data source, then selecting specific data sets you want to expose through the API. Fabric builds the GraphQL schema automatically based on your data, you can test and prototype queries directly in our graphical in-browser GraphQL development environment (API editor), and applications are ready to connect in minutes. 

Currently, the following supported data sources can be exposed through the Fabric API for GraphQL: 

• Microsoft Fabric Data Warehouse 
• Microsoft Fabric Lakehouse via SQL Analytics Endpoint 
• Microsoft Fabric Mirrored Databases via SQL Analytics Endpoint 

Click here to learn more about how to get started. 

Data Science 

As you may know, Copilot in Microsoft Fabric requires your tenant administrator to enable the feature from the admin portal. Starting May 20th, 2024, Copilot in Microsoft Fabric will be enabled by default for all tenants. This update is part of our continuous efforts to enhance user experience and productivity within Microsoft Fabric. This new default activation means that AI features like Copilot will be automatically enabled for tenants who have not yet enabled the setting.  

We are introducing a new capability to enable Copilot on Capacity level in Fabric. A new option is being introduced in the tenant admin portal, to delegate the enablement of AI and Copilot features to Capacity administrators.  This AI and Copilot setting will be automatically delegated to capacity administrators and tenant administrators won’t be able to turn off the delegation.   

We also have a cross-geo setting for customers who want to use Copilot and AI features while their capacity is in a different geographic region than the EU data boundary or the US. By default, the cross-geo setting will stay off and will not be delegated to capacity administrators automatically.  Tenant administrators can choose whether to delegate this to capacity administrators or not. 

Figure 1.  Copilot in Microsoft Fabric will be auto enabled and auto delegated to capacity administrators. 

Capacity administrators will see the “Copilot and Azure OpenAI Service (preview)” settings under Capacity settings/ Fabric Capacity / <Capacity name> / Delegated tenant settings. By default, the capacity setting will inherit tenant level settings. Capacity administrators can decide whether to override the tenant administrator’s selection. This means that even if Copilot is not enabled on a tenant level, a capacity administrator can choose to enable Copilot for their capacity. With this level of control, we make it easier to control which Fabric workspaces can utilize AI features like Copilot in Microsoft Fabric. 

To enhance privacy and trust, we’ve updated our approach to abuse monitoring: previously, we retained data from Copilot in Fabric, including prompt inputs and outputs, for up to 30 days to check for misuse. Following customer feedback, we’ve eliminated this 30-day retention. Now, we no longer store prompt related data, demonstrating our unwavering commitment to your privacy and security. We value your input and take your concerns seriously. 

Real-Time Intelligence 

This month includes the announcement of Real-Time Intelligence, the next evolution of Real-Time Analytics and Data Activator. With Real-Time Intelligence, Fabric extends to the world of streaming and high granularity data, enabling all users in your organization to collect, analyze and act on this data in a timeline manner making faster and more informed business decisions. Read the full announcement from Build 2024. 

Real-Time Intelligence includes a wide range of capabilities across ingestion, processing, analysis, transformation, visualization and taking action. All of this is supported by the Real-Time hub , the central place to discover and manage streaming data and start all related tasks.  

Read on for more information on each capability and stay tuned for a series of blogs describing the features in more detail. All features are in Public Preview unless otherwise specified. Feedback on any of the features can be submitted at https://aka.ms/rtiidea    

Ingest & Process  

• Introducing the Real-Time hub 
• Get Events with new sources of streaming and event data 
• Source from Real-Time Hub in Enhanced Eventstream  
• Use Real-Time hub to Get Data in KQL Database in Eventhouse 
• Get data from Real-Time Hub within Reflexes 
• Eventstream Edit and Live modes 
• Default and derived streams 
• Route data streams based on content 

Analyze & Transform  

• Eventhouse GA 
• Eventhouse OneLake availability GA 
• Create a database shortcut to another KQL Database 
• Support for AI Anomaly Detector  
• Copilot for Real-Time Intelligence 
• Tenant-level private endpoints for Eventhouse 

Visualize & Act  

• Visualize data with Real-Time Dashboards  
• New experience for data explorati on  
• Create triggers from Real-Time Hub 
• Set alert on Real-time Dashboards 
• Taking action through Fabric Items 

Ingest & Process 

Real-Time hub is the single place for all data-in-motion across your entire organization. Several key features are offered in Real-Time hub: 

• Single place for data-in-motion for the entire organization  

Real-Time hub enables users to easily discover, ingest, manage, and consume data-in-motion from a wide variety of sources. It lists all the streams and KQL tables that customers can directly act on. 

2. Real-Time hub is never empty  

All data streams in Fabric automatically show up in the hub. Also, users can subscribe to events in Fabric gaining insights into the health and performance of their data ecosystem, 

3. Numerous connectors to simplify data ingestion from anywhere to Real-Time hub  

Real-Time hub makes it easy for you to ingest data into Fabric from a wide variety of sources like AWS Kinesis, Kafka clusters, Microsoft streaming sources, sample data and Fabric events using the Get Events experience.  

There are 3 tabs in the Hub:  

• Data streams : This tab contains all streams that are actively running in Fabric that user has access to. This includes all streams from Eventstreams and all tables from KQL Databases. 
• Microsoft sources : This tab contains Microsoft sources (that user has access to) and can be connected to Fabric. 
• Fabric events : Fabric now has event-driven capabilities to support real-time notifications and data processing. Users can monitor and react to events including Fabric Workspace Item events and Azure Blob Storage events. These events can be used to trigger other actions or workflows, such as invoking a data pipeline or sending a notification via email. Users can also send these events to other destinations via Event Streams. 

Learn More  

You can now connect to data from both inside and outside of Fabric in a mere few steps.  Whether data is coming from new or existing sources, streams, or available events, the Get Events experience allows users to connect to a wide range of sources directly from Real-Time hub, Eventstreams, Eventhouse and Data Activator.  

This enhanced capability allows you to easily connect external data streams into Fabric with out-of-box experience, giving you more options and helping you to get real-time insights from various sources. This includes Camel Kafka connectors powered by Kafka connect to access popular data platforms, as well as the Debezium connectors for fetching the Change Data Capture (CDC) streams. 

Using Get Events, bring streaming data from Microsoft sources directly into Fabric with a first-class experience.  Connectivity to notification sources and discrete events is also included, this enables access to notification events from Azure and other clouds solutions including AWS and GCP.  The full set of sources which are currently supported are: 

• Microsoft sources : Azure Event Hubs, Azure IoT hub 
• External sources : Google Cloud Pub/Sub, Amazon Kinesis Data Streams, Confluent Cloud Kafka 
• Change data capture databases : Azure SQL DB (CDC), PostgreSQL DB (CDC), Azure Cosmos DB (CDC), MySQL DB (CDC)  
• Fabric events : Fabric Workspace Item events, Azure Blob Storage events  

Learn More   

With enhanced Eventstream, you can now stream data not only from Microsoft sources but also from other platforms like Google Cloud, Amazon Kinesis, Database change data capture streams, etc. using our new messaging connectors. The new Eventstream also lets you acquire and route real-time data not only from stream sources but also from discrete event sources, such as: Azure Blob Storage events, Fabric Workspace Item events. 

To use these new sources in Eventstream, simply create an eventstream with choosing “Enhanced Capabilities (preview)”. 

You will see the new Eventstream homepage that gives you some choices to begin with. By clicking on the “Add external source”, you will find these sources in the Get events wizard that helps you to set up the source in a few steps. After you add the source to your eventstream, you can publish it to stream the data into your eventstream.  

Using Eventstream with discrete sources to turn events into streams for more analysis. You can send the streams to different Fabric data destinations, like Lakehouse and KQL Database. After the events are converted, a default stream will appear in Real-Time Hub. To turn them, click Edit on ribbon, select “Stream events” on the source node, and publish your eventstream. 

To transform the stream data or route it to different Fabric destinations based on its content, you can click Edit in ribbon and enter the Edit mode. There you can add event processing operators and destinations. 

With Real-Time hub embedded in KQL Database experience, each user in the tenant can view and add streams which they have access to and directly ingest it to a KQL Database table in Eventhouse.  

This integration provides each user in the tenant with the ability to access and view data streams they are permitted to. They can now directly ingest these streams into a KQL Database table in Eventhouse. This simplifies the data discovery and ingestion process by allowing users to directly interact with the streams. Users can filter data based on the Owner, Parent and Location and provides additional information such as Endorsement and Sensitivity. 

You can access this by clicking on the Get Data button from the Database ribbon in Eventhouse. 

This will open the Get Data wizard with Real-Time hub embedded. 

You can use events from Real-Time hub directly in reflex items as well. From within the main reflex UI, click ‘Get data’ in the toolbar: 

This will open a wizard that allows you to connect to new event sources or browse Real-Time Hub to use existing streams or system events. 

Search new stream sources to connect to or select existing streams and tables to be ingested directly by Reflex. 

You then have access to the full reflex modeling experience to build properties and triggers over any events from Real-Time hub.  

Eventstream offers two distinct modes, Edit and Live, to provide flexibility and control over the development process of your eventstream. If you create a new Eventstream with Enhanced Capabilities enabled, you can modify it in an Edit mode. Here, you can design stream processing operations for your data streams using a no-code editor. Once you complete the editing, you can publish your Eventstream and visualize how it starts streaming and processing data in Live mode .   

In Edit mode, you can:   

• Make changes to an Eventstream without implementing them until you publish the Eventstream. This gives you full control over the development process.  
• Avoid test data being streamed to your Eventstream. This mode is designed to provide a secure environment for testing without affecting your actual data streams. 

For Live mode, you can :  

• Visualize how your Eventstream streams, transforms, and routes your data streams to various destinations after publishing the changes.  
• Pause the flow of data on selected sources and destinations, providing you with more control over your data streams being streamed into your Eventstream.  

When you create a new Eventstream with Enhanced Capabilities enabled, you can now create and manage multiple data streams within Eventstream, which can then be displayed in the Real-Time hub for others to consume and perform further analysis.  

There are two types of streams:   

• Default stream : Automatically generated when a streaming source is added to Eventstream. Default stream captures raw event data directly from the source, ready for transformation or analysis.  
• Derived stream : A specialized stream that users can create as a destination within Eventstream. Derived stream can be created after a series of operations such as filtering and aggregating, and then it’s ready for further consumption or analysis by other users in the organization through the Real-Time Hub.  

The following example shows that when creating a new Eventstream a default stream alex-es1-stream is automatically generated. Subsequently, a derived stream dstream1 is added after an Aggregate operation within the Eventstream. Both default and derived streams can be found in the Real-Time hub.  

Customers can now perform stream operations directly within Eventstream’s Edit mode, instead of embedding in a destination. This enhancement allows you to design stream processing logics and route data streams in the top-level canvas. Custom processing and routing can be applied to individual destinations using built-in operations, allowing for routing to distinct destinations within the Eventstream based on different stream content. 

These operations include:  

• Aggregate : Perform calculations such as SUM, AVG, MIN, and MAX on a column of values and return a single result. 
• Expand : Expand array values and create new rows for each element within the array.  
• Filter : Select or filter specific rows from the data stream based on a condition. 
• Group by : Aggregate event data within a certain time window, with the option to group one or more columns.  
• Manage Fields : Customize your data streams by adding, removing, or changing data type of a column.  
• Union : Merge two or more data streams with shared fields (same name and data type) into a unified data stream.  

Analyze & Transform 

Eventhouse, a cutting-edge database workspace meticulously crafted to manage and store event-based data, is now officially available for general use. Optimized for high granularity, velocity, and low latency streaming data, it incorporates indexing and partitioning for structured, semi-structured, and free text data. With Eventhouse, users can perform high-performance analysis of big data and real-time data querying, processing billions of events within seconds. The platform allows users to organize data into compartments (databases) within one logical item, facilitating efficient data management.  

Additionally, Eventhouse enables the sharing of compute and cache resources across databases, maximizing resource utilization. It also supports high-performance queries across databases and allows users to apply common policies seamlessly. Eventhouse offers content-based routing to multiple databases, full view lineage, and high granularity permission control, ensuring data security and compliance. Moreover, it provides a simple migration path from Azure Synapse Data Explorer and Azure Data Explorer, making adoption seamless for existing users. 

Engineered to handle data in motion, Eventhouse seamlessly integrates indexing and partitioning into its storing process, accommodating various data formats. This sophisticated design empowers high-performance analysis with minimal latency, facilitating lightning-fast ingestion and querying within seconds. Eventhouse is purpose-built to deliver exceptional performance and efficiency for managing event-based data across diverse applications and industries. Its intuitive features and seamless integration with existing Azure services make it an ideal choice for organizations looking to leverage real-time analytics for actionable insights. Whether it’s analyzing telemetry and log data, time series and IoT data, or financial records, Eventhouse provides the tools and capabilities needed to unlock the full potential of event-based data. 

We’re excited to announce that OneLake availability of Eventhouse in Delta Lake format is Generally Available. 

Delta Lake  is the unified data lake table format chosen to achieve seamless data access across all compute engines in Microsoft Fabric. 

The data streamed into Eventhouse is stored in an optimized columnar storage format with full text indexing and supports complex analytical queries at low latency on structured, semi-structured, and free text data. 

Enabling data availability of Eventhouse in OneLake means that customers can enjoy the best of both worlds: they can query the data with high performance and low latency in their  Eventhouse and query the same data in Delta Lake format via any other Fabric engines such as Power BI Direct Lake mode, Warehouse, Lakehouse, Notebooks, and more. 

To learn more, please visit https://learn.microsoft.com/en-gb/fabric/real-time-analytics/one-logical-copy 

A database shortcut in Eventhouse is an embedded reference to a source database. The source database can be one of the following: 

• (Now Available) A KQL Database in Real-Time Intelligence  
• An Azure Data Explorer database  

The behavior exhibited by the database shortcut is similar to that of a follower database  

The owner of the source database, the data provider, shares the database with the creator of the shortcut in Real-Time Intelligence, the data consumer. The owner and the creator can be the same person. The database shortcut is attached in read-only mode, making it possible to view and run queries on the data that was ingested into the source KQL Database without ingesting it.  

This helps with data sharing scenarios where you can share data in-place either within teams, or even with external customers.  

AI Anomaly Detector is an Azure service for high quality detection of multivariate and univariate anomalies in time series. While the standalone version is being retired October 2026, Microsoft open sourced the anomaly detection core algorithms and they are now supported in Microsoft Fabric. Users can leverage these capabilities in Data Science and Real-Time Intelligence workload. AI Anomaly Detector models can be trained in Spark Python notebooks in Data Science workload, while real time scoring can be done by KQL with inline Python in Real-Time Intelligence. 

We are excited to announce the Public Preview of Copilot for Real-Time Intelligence. This initial version includes a new capability that translates your natural language questions about your data to KQL queries that you can run and get insights.  

Your starting point is a KQL Queryset, that is connected to a KQL Database, or to a standalone Kusto database:  

Simply type the natural language question about what you want to accomplish, and Copilot will automatically translate it to a KQL query you can execute. This is extremely powerful for users who may be less familiar with writing KQL queries but still want to get the most from their time-series data stored in Eventhouse. 

Stay tuned for more capabilities from Copilot for Real-Time Intelligence!   

Customers can increase their network security by limiting access to Eventhouse at a tenant-level, from one or more virtual networks (VNets) via private links. This will prevent unauthorized access from public networks and only permit data plane operations from specific VNets.  

Visualize & Act 

Real-Time Dashboards have a user-friendly interface, allowing users to quickly explore and analyze their data without the need for extensive technical knowledge. They offer a high refresh frequency, support a range of customization options, and are designed to handle big data.  

The following visual types are supported, and can be customized with the dashboard’s user-friendly interface: 

You can also define conditional formatting rules to format the visual data points by their values using colors, tags, and icons. Conditional formatting can be applied to a specific set of cells in a predetermined column or to entire rows, and lets you easily identify interesting data points. 

Beyond the support visual, Real-Time Dashboards provide several capabilities to allow you to interact with your data by performing slice and dice operations for deeper analysis and gaining different viewpoints. 

• Parameters are used as building blocks for dashboard filters and can be added to queries to filter the data presented by visuals. Parameters can be used to slice and dice dashboard visuals either directly by selecting parameter values in the filter bar or by using cross-filters. 
• Cross filters allow you to select a value in one visual and filter all other visuals on that dashboard based on the selected data point. 
• Drillthrough capability allows you to select a value in a visual and use it to filter the visuals in a target page in the same dashboard. When the target page opens, the value is pushed to the relevant filters.    

Real-Time Dashboards can be shared broadly and allow multiple stakeholders to view dynamic, real time, fresh data while easily interacting with it to gain desired insights. 

Directly from a real-time dashboard, users can refine their exploration using a user-friendly, form-like interface. This intuitive and dynamic experience is tailored for insights explorers craving insights based on real-time data. Add filters, create aggregations, and switch visualization types without writing queries to easily uncover insights.  

With this new feature, insights explorers are no longer bound by the limitations of pre-defined dashboards. As independent explorers, they have the freedom for ad-hoc exploration, leveraging existing tiles to kickstart their journey. Moreover, they can selectively remove query segments, and expand their view of the data landscape.  

Dive deep, extract meaningful insights, and chart actionable paths forward, all with ease and efficiency, and without having to write complex KQL queries.  

Data Activator allows you to monitor streams of data for various conditions and set up actions to be taken in response. These triggers are available directly within the Real-Time hub and in other workloads in Fabric. When the condition is detected, an action will automatically be kicked off such as sending alerts via email or Teams or starting jobs in Fabric items.  

When you browse the Real-Time Hub, you’ll see options to set triggers in the detail pages for streams. 

Selecting this will open a side panel where you can configure the events you want to monitor, the conditions you want to look for in the events, and the action you want to take while in the Real-Time hub experience. 

Completing this pane creates a new reflex item with a trigger that monitors the selected events and condition for you. Reflexes need to be created in a workspace supported by a Fabric or Power BI Premium capacity – this can be a trial capacity so you can get started with it today! 

Data Activator has been able to monitor Power BI report data since it was launched, and we now support monitoring of Real-Time Dashboard visuals in the same way.

From real-time dashboard tiles you can click the ellipsis (…) button and select “Set alert”

This opens the embedded trigger pane, where you can specify what conditions, you are looking for. You can choose whether to send email or Teams messages as the alert when these conditions are met.

When creating a new reflex trigger, from Real-time Hub or within the reflex item itself, you’ll notice a new ‘Run a Fabric item’ option in the Action section. This will create a trigger that starts a new Fabric job whenever its condition is met, kicking off a pipeline or notebook computation in response to Fabric events. A common scenario would be monitoring Azure Blob storage events via Real-Time Hub, and running data pipeline jobs when Blog Created events are detected. 

This capability is extremely powerful and moves Fabric from a scheduled driven platform to an event driven platform.  

Pipelines, spark jobs, and notebooks are just the first Fabric items we’ll support here, and we’re keen to hear your feedback to help prioritize what else we support. Please leave ideas and votes on https://aka.ms/rtiidea and let us know! 

Real-Time Intelligence, along with the Real-Time hub, revolutionizes what’s possible with real-time streaming and event data within Microsoft Fabric.  

Learn more and try it today https://aka.ms/realtimeintelligence   

Data Factory 

Dataflow gen2 .

We are thrilled to announce that the Power Query SDK is now generally available in Visual Studio Code! This marks a significant milestone in our commitment to providing developers with powerful tools to enhance data connectivity and transformation. 

The Power Query SDK is a set of tools that allow you as the developer to create new connectors for Power Query experiences available in products such as Power BI Desktop, Semantic Models, Power BI Datamarts, Power BI Dataflows, Fabric Dataflow Gen2 and more. 

This new SDK has been in public preview since November of 2022, and we’ve been hard at work improving this experience which goes beyond what the previous Power Query SDK in Visual Studio had to offer.  

The latest of these biggest improvements was the introduction of the Test Framework in March of 2024 that solidifies the developer experience that you can have within Visual Studio Code and the Power Query SDK for creating a Power Query connector. 

The Power Query SDK extension for Visual Studio will be deprecated by June 30, 2024, so we encourage you to give this new Power Query SDK in Visual Studio Code today if you haven’t.  

To get started with the Power Query SDK in Visual Studio Code, simply install it from the Visual Studio Code Marketplace . Our comprehensive documentation and tutorials are available to help you harness the full potential of your data. 

Join our vibrant community of developers to share insights, ask questions, and collaborate on exciting projects. Our dedicated support team is always ready to assist you with any queries. 

We look forward to seeing the innovative solutions you’ll create with the Power Query SDK in Visual Studio Code. Happy coding! 

Introducing a convenient enhancement to the Dataflows Gen2 Refresh History experience! Now, alongside the familiar “X” button in the Refresh History screen, you’ll find a shiny new Refresh Button . This small but mighty addition empowers users to refresh the status of their dataflow refresh history status without the hassle of exiting the refresh history and reopening it. Simply click the Refresh Button , and voilà! Your dataflow’s refresh history status screen is updated, keeping you in the loop with minimal effort. Say goodbye to unnecessary clicks and hello to streamlined monitoring! 

• [New] OneStream : The OneStream Power Query Connector enables you to seamlessly connect Data Factory to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category. 

Data workflows  

We are excited to announce the preview of ‘Data workflows’, a new feature within the Data Factory that revolutionizes the way you build and manage your code-based data pipelines. Powered by Apache Airflow, Data workflows offer seamless authoring, scheduling, and monitoring experience for Python-based data processes defined as Directed Acyclic Graphs (DAGs). This feature brings a SaaS-like experience to running DAGs in a fully managed Apache Airflow environment, with support for autoscaling , auto-pause , and rapid cluster resumption to enhance cost-efficiency and performance.  

It also includes native cloud-based authoring capabilities and comprehensive support for Apache Airflow plugins and libraries. 

To begin using this feature: 

• Access the Microsoft Fabric Admin Portal. 
• Navigate to Tenant Settings. 

Under Microsoft Fabric options, locate and expand the ‘Users can create and use Data workflows (preview)’ section. Note: This action is necessary only during the preview phase of Data workflows. 

2. Create a new Data workflow within an existing or new workspace. 

3. Add a new Directed Acyclic Graph (DAG) file via the user interface. 

4.  Save your DAG(s). 

5. Use Apache Airflow monitoring tools to observe your DAG executions. In the ribbon, click on Monitor in Apache Airflow. 

For additional information, please consult the product documentation .   If you’re not already using Fabric capacity, consider signing up for the Microsoft Fabric free trial to evaluate this feature. 

Data Pipelines 

We are excited to announce a new feature in Fabric that enables you to create data pipelines to access your firewall-enabled Azure Data Lake Storage Gen2 (ADLS Gen2) accounts. This feature leverages the workspace identity to establish a secure and seamless connection between Fabric and your storage accounts. 

With trusted workspace access, you can create data pipelines to your storage accounts with just a few clicks. Then you can copy data into Fabric Lakehouse and start analyzing your data with Spark, SQL, and Power BI. Trusted workspace access is available for workspaces in Fabric capacities (F64 or higher). It supports organizational accounts or service principal authentication for storage accounts. 

How to use trusted workspace access in data pipelines  

Create a workspace identity for your Fabric workspace. You can follow the guidelines provided in Workspace identity in Fabric . 

Configure resource instance rules for the Storage account that you want to access from your Fabric workspace. Resource instance rules for Fabric workspaces can only be created through ARM templates. Follow the guidelines for configuring resource instance rules for Fabric workspaces here . 

Create a data pipeline to copy data from the firewall enabled ADLS gen2 account to a Fabric Lakehouse. 

To learn more about how to use trusted workspace access in data pipelines, please refer to Trusted workspace access in Fabric . 

We hope you enjoy this new feature for your data integration and analytics scenarios. Please share your feedback and suggestions with us by leaving a comment here. 

Introducing Blob Storage Event Triggers for Data Pipelines 

A very common use case among data pipeline users in a cloud analytics solution is to trigger your pipeline when a file arrives or is deleted. We have introduced Azure Blob storage event triggers as a public preview feature in Fabric Data Factory Data Pipelines. This utilizes the Fabric Reflex alerts capability that also leverages Event Streams in Fabric to create event subscriptions to your Azure storage accounts. 

Parent/Child pipeline pattern monitoring improvements

Today, in Fabric Data Factory Data Pipelines, when you call another pipeline using the Invoke Pipeline activity, the child pipeline is not visible in the monitoring view. We have made updates to the Invoke Pipeline activity so that you can view your child pipeline runs. This requires an upgrade to any pipelines that you have in Fabric that already use the current Invoke Pipeline activity. You will be prompted to upgrade when you edit your pipeline and then provide a connection to your workspace to authenticate. Another additional new feature that will light up with this invoke pipeline activity update is the ability to invoke pipeline across workspaces in Fabric. 

We are excited to announce the availability of the Fabric Spark job definition activity for data pipelines. With this new activity, you will be able to run a Fabric Spark Job definition directly in your pipeline. Detailed monitoring capabilities of your Spark Job definition will be coming soon!  

To learn more about this activity, read https://aka.ms/SparkJobDefinitionActivity  

We are excited to announce the availability of the Azure HDInsight activity for data pipelines. The Azure HDInsight activity allows you to execute Hive queries, invoke a MapReduce program, execute Pig queries, execute a Spark program, or a Hadoop Stream program. Invoking either of the 5 activities can be done in a singular Azure HDInsight activity, and you can invoke this activity using your own or on-demand HDInsight cluster. 

To learn more about this activity, read https://aka.ms/HDInsightsActivity  

We are thrilled to share the new Modern Get Data experience in Data Pipeline to empower users intuitively and efficiently discover the right data, right connection info and credentials.   

In the data destination, users can easily set destination by creating a new Fabric item or creating another destination or selecting existing Fabric item from OneLake data hub. 

In the source tab of Copy activity, users can conveniently choose recent used connections from drop down or create a new connection using “More” option to interact with Modern Get Data experience. 

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• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
• Python Web Dev
• DSA with Python
• Python OOPs
• Dictionaries

Python Operators

Precedence and associativity of operators in python.

• Python Arithmetic Operators
• Difference between / vs. // operator in Python
• Python - Star or Asterisk operator ( * )
• What does the Double Star operator mean in Python?
• Division Operators in Python
• Modulo operator (%) in Python
• Python Logical Operators
• Python OR Operator
• Difference between 'and' and '&' in Python
• not Operator in Python | Boolean Logic

Ternary Operator in Python

• Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

• Walrus Operator in Python 3.8
• Increment += and Decrement -= Assignment Operators in Python
• Merging and Updating Dictionary Operators in Python 3.9
• New '=' Operator in Python3.8 f-string

Python Relational Operators

• Comparison Operators in Python
• Python NOT EQUAL operator
• Difference between == and is operator in Python
• Chaining comparison operators in Python
• Python Membership and Identity Operators
• Difference between != and is not operator in Python

In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators. 

• OPERATORS: These are the special symbols. Eg- + , * , /, etc.
• OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators and Membership Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient. 

There are two types of division operators: 

Float division

• Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

• P – Parentheses
• E – Exponentiation
• M – Multiplication (Multiplication and division have the same precedence)
• D – Division
• A – Addition (Addition and subtraction have the same precedence)
• S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

• x % 10 -> yields the last digit
• x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

• Logical not
• logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

• Bitwise NOT
• Bitwise Shift
• Bitwise AND
• Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical. 

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5. 

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax :   [on_true] if [expression] else [on_false] 

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python . 

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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