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Translytical task flows take interactive Power BI reports to the next level where users can update records, add annotations, and trigger actions in external systems—all without leaving the report.

With translytical task flows, report consumers become active participants. Instead of viewing data and then switching to another application to take action, users can complete their entire workflow within the Power BI interface.

Translytical task flows connect Power BI reports to Fabric User Data Functions. When a user interacts with a report—selecting a record, entering a value, and clicking a button—the report passes the context of what they have selected to a function to execute the requested action.

Translytical_Task_Flows_Generally_Available

Figure 1: Example of a translytical report experience, where users can review project status and add notes directly in Power BI.

Capabilities

• Adding data: Insert new records into your database directly from the report. For example, a customer service representative can add a new customer record while reviewing existing accounts and see it immediately in the report.
• Updating data: Update existing records without leaving the report. A logistics coordinator can change an order status or add notes to a shipment record as they work through their queue.
• Deleting data: Remove records that are no longer needed. An inventory manager can delete discontinued products from active lists while reviewing stock levels.
• Calling external APIs: Trigger actions in other systems through API requests. A sales representative can request a discount approval that posts directly to Microsoft Teams, where a manager can review and respond. Not only can they trigger these but capture and pass on a message too.An_adaptive_card_requestion_an_update_in_a_Teams_channel
  Figure 2: An adaptive card in a Teams channel requesting an update to a status with link to the Power BI report to update it using translytical task flows.

These capabilities can work together too. For instance, a sales opportunity report can include a discount request form. When a user selects opportunities from a filtered table, enters a discount percentage, and adds a justification, clicking the submit button sends all that context to a function. The function then processes the discount and posts the request to Teams with the relevant details, creating a complete audit trail from insight to action. The updated data is also immediately visible in the same report!

Other scenarios for translytical task flows include:

Data annotation and quality management: Field teams often discover data issues while working in reports. With translytical task flows, they can correct a misspelled customer name, update an outdated address, or add contextual notes to records immediately. This approach improves data quality at the point of discovery rather than routing corrections through separate processes.

Workflow automation: Business processes frequently require approvals, notifications, or ticket creation. Translytical task flows can trigger these actions based on report context. A procurement analyst reviewing vendor performance can flag a supplier for review, automatically creating a task in the appropriate system with all the supporting data attached.

AI-assisted decision making: Use AI functions in your extract load and transform process with Fabric Notebooks to categorize or summarize your data. These can then be reviewed and updated in your Power BI report.

For data write-back scenarios, User Data Functions currently have native connection management for the following Fabric data sources:

• Fabric SQL databases
• Fabric warehouses
• Fabric lakehouses (for files)

Once you have your scenario in mind, building your first translytical task flow involves these main tasks:

• Storing your data in a Fabric data source.
• Developing a User Data Function to handle the action.
• Creating a Power BI semantic model to use this data.
• Building a Power BI report with interactive elements to capture the user’s input and call the function.

Copilot in Microsoft Fabric is built into many Fabric workflows and GitHub Copilot in Visual Studio Code with MCPs to access learn documentation and connect to Power BI semantic models can help you accelerate this process too.

Translytical task flows represent a shift in how organizations can use Power BI. Reports are no longer only for analysis—they enable you to take action. By connecting insight directly to execution, teams can reduce the time between identifying an opportunity and acting on it.

Next steps

• Learn more: Refer to the Translytical task flows documentation for detailed guidance on building your first task flow.
• Explore User Data Functions: Visit the User Data Functions in Fabric documentation to understand all the capabilities it supports.
• Submit ideas: Share your feedback and feature requests through the Power BI ideas forum.

Continue reading “Translytical Task Flows (Generally Available)”

We’re introducing three new skills for the Power Pages agentic code plugin for GitHub Copilot and Claude Code CLI that together unlock a missing capability in AI‑assisted site building: server‑side logic.

The post Build your server-side logic with AI: new Power Pages Agentic Code skills appeared first on Microsoft Power Platform Blog.

Updated on 20 April 2026: Thanks so much for all the engagement on this topic since the original publishing of this blog post on 2 April 2026. We’ve seen several recurring questions, and we’d like to clarify our position on a few key points. 

• Dataflow Gen1 remains supported, but it is in a legacy state and won’t receive future innovation. We understand many customers have built important, business‑critical solutions on Gen1, and those existing workloads can continue to run while future investments are planned around Dataflow Gen2. 

• Primary call to action for Premium customers: For Premium customers who already have access to Fabric, Dataflow Gen2 is the recommended path to take advantage of the latest improvements across functionality, productivity, performance, pricing, scale, reliability, and more. Many teams find it helpful to start by evaluating Gen2 for new or evolving workloads where these benefits can be realized quickly. 

• Guidance for Pro and Premium Per User (PPU) customers: Many customers rely on Dataflow Gen1 in Pro/PPU today, and it can continue to be the right choice depending on the scenario. If Gen1 best fits your current use case, it remains supported and existing workloads can continue to run as-is. As we introduce new Dataflow Gen2 paths for Pro/PPU scenarios, we’ll share clear guidance and recommended steps to help with a smooth transition. 

• GCC support for Premium customers: For customers using Dataflows Gen1 in GCC environments on Premium, Dataflow Gen2 support in GCC will be available before any future transition milestones, ensuring a supported upgrade path. 

More granular Fabric controls: Customers have asked for finer‑grained enablement than today’s all‑or‑nothing Fabric switch. Work is underway to provide more granular administrative controls, including the ability to enable only Dataflow Gen2, so capabilities can be rolled out incrementally with the right governance. 

Original post (2 April 2026):

For more than eight years, customers have relied on Power BI Dataflows (Gen1) as a core part of their analytics solutions. We’re grateful for the trust you placed in the Power Query experience to build reusable, low-code data preparation pipelines that power reports, semantic models, and downstream analytics.

Now, we’re sharing an update on the future of Dataflows.

Dataflows Gen2 builds on everything you know from Gen1—preserving the familiar Power Query authoring experience—while delivering major improvements in scale, flexibility, cost efficiency, and manageability. Going forward, all new Dataflow innovation will land only in Dataflows Gen2.

The future of Dataflows Gen1

Power BI Dataflows Gen1 has reached the end of active innovation and is moving into a Legacy state:

• Existing Gen1 dataflows will continue to work for the foreseeable future. However, specific retirement dates for Gen1 are being finalized, and we’ll share details as plans progress. For customers running Gen1 at Premium capacity, we will provide at least 12 months’ notice before Dataflow Gen1 is retired. (If you’re using Gen1 on Pro or Premium-Peruser, we still strongly recommend planning a move to Dataflows Gen2 to take advantage of the latest investments.)
• In the meantime, Gen1 artifacts will remain available and will be clearly marked as Legacy in product experiences, including the New Artifact menu.
• No new features are planned for Dataflows Gen1. Support will be limited to a narrow set of high impact issues, where changes can be delivered safely within the existing architecture. Many of the remaining Gen1 limitations would require significant architecture changes and are best addressed by moving to Dataflows Gen2, which was designed to solve these scenarios more comprehensively.

The previous statements apply to all Dataflows Gen1—including usage on Pro, PPU, and Premium licenses.

What’s new in Dataflows Gen2

Dataflows Gen2 retains the familiar Power Query experience while introducing substantial platform-level improvements to scale, performance, governance, and cost efficiency. The following updates provide additional details on each benefit area to help customers understand the full scope of Gen2 enhancements.

More flexible destinations

Dataflows Gen2 supports a significantly broader range of output destinations, enabling alignment with diverse data architectures across business, departmental, and enterprise scenarios.

• SharePoint and OneDrive are ideal for business users who need refreshed files (CSV, Excel) for downstream workflows, Office automation, or integrations with Power Automate and Teams.
• Azure Data Lake Storage is a great fit for customers building scalable ingestion pipelines for data science, machine learning, or lakehouse scenarios.
• Azure SQL Database / SQL MI enables operational reporting, standardized relational storage, and hybrid analytics scenarios.
• Microsoft Fabric Lakehouse / Warehouse / SQL analytics endpoints are the most seamless destination option for customers aligning with the Fabric vision. Gen2 integrates deeply with Fabric runtimes, unlocking better performance, more consistent semantics, and governance alignment.
• Snowflake and other cloud databases support multi‑cloud architectures and reduces friction for enterprise customers already standardized on multiple warehouse technologies.

This broader range of destinations allows teams to use dataflows as a general-purpose low-code data ingestion and transformation layer, enabling many more scenarios never supported before with Dataflow Gen1.

Improved performance and scale

Dataflows Gen2 is built on the Fabric runtime and a modernized execution engine that delivers a step‑change in performance, reliability, and scalability.

• Modern query evaluation leverages Fabric’s elastic compute layer to automatically manage scaling behavior, minimizing the need for manual optimization or workload tuning.
• Fast Copy technology supports high-throughput ingestion into Fabric destinations, enabling sustained data movement measured in gigabytes per minute.
• Parallelized execution enables multiple partitions and transformation steps to be processed concurrently, significantly reducing refresh durations compared to Gen1.
• Enhanced support for large datasets includes improvements in memory handling, high-cardinality data processing, and unbounded ingestion patterns.
• Predictable refresh behavior ensures more consistent performance under varying workload conditions.

Together, these improvements establish a more durable and high-performance engine for data preparation, especially in enterprise environments that rely on large or frequently refreshed dataflows.

Built-in AI assistance

Dataflows Gen2 introduces integrated AI capabilities designed to accelerate development, improve quality, and reduce the learning curve for users working with complex transformations.

• Copilot-assisted authoring converts natural-language instructions into Power Query logic, improving productivity and lowering the barrier to entry for users with limited M expertise.
• Code explanation capabilities translate complex or legacy M scripts into easy-to-understand natural language descriptions, improving maintainability and simplifying onboarding.
• Automated performance and foldability recommendations help users align transformations to foldable patterns, resulting in faster load times and lower compute consumption.
• AI-powered data quality insights assist with identifying semantic types, outliers, and join keys and detecting common data issues early in the pipeline.

These capabilities provide consistent guidance across the authoring lifecycle and support organizations with diverse skill levels.

Richer diagnostics

Gen2 introduces a more comprehensive set of diagnostics designed to improve traceability, troubleshooting, and operational reliability.

• Detailed refresh history now includes detailed timing information to clarify where time is spent during execution.
• Expanded logging and instrumentation provide visibility into foldability decisions, connector behaviors, authentication flows, and network operations.
• More consistent refresh semantics across all destinations ensures uniform behavior regardless of the target storage system.
• Greater operational transparency supports root-cause analysis and reduces the time required to identify and resolve failures.

These diagnostic improvements help teams manage dataflows more efficiently and maintain higher levels of operational readiness.

Tiered pricing and potential cost savings

The introduction of a tiered pricing model is a major advantage for customers transitioning from Gen1.

• Gen2 decouples Dataflows execution from Premium capacity consumption, aligning compute usage with Fabric’s capacity unit (CU)–based architecture.
• This model allows customers to pay only for the compute resources required rather than maintaining always-on Premium capacity for workloads that may be intermittent or variable.
• Elastic scaling ensures that high-volume or burst workloads can consume proportionally more compute during peak times, while lighter workloads incur lower costs.
• Organizations with unpredictable or seasonal refresh patterns may see material cost reductions compared to Gen1 running on Premium capacity.

This flexible cost model provides more predictable and efficient resource utilization, particularly for enterprises with diverse or rapidly evolving data refresh patterns.

Our recommendation: Start planning your upgrade

If you’re starting a new project, we strongly recommend using Dataflows Gen2. It offers better performance, richer diagnostics, built‑in AI, broader destination support, and a more flexible cost model.

For existing Gen1 dataflows, now is the right time to begin planning your upgrade:

• Use Save as Dataflow Gen2 for quick, low effort upgrades of individual dataflows.
• For larger migration scenarios, the Save As API enables bulk migration and automation, supporting CI/CD workflows.
• For programmatic or large‑scale upgrades, refer to the Migrate to Dataflow Gen2 (CI/CD) guidance.

Starting now allows you to modernize incrementally, beginning with a subset of your portfolio while keeping all your Power Query skills fully transferable.

Thank you

Thank you again for your long-standing investment in Dataflows and Power Query. We’re excited to support your transition to Dataflows Gen2 and to help you unlock new capabilities for the next generation of analytics solutions.

Continue reading “Dataflows: Thank you for eight years of Gen1—and why Gen2 is the future”

If governance is just a list of things people can’t do, that’s not governance—it’s a backlog of workarounds waiting to happen.

The post ​Building trustworthy AI: A practical framework for adaptive governance appeared first on Microsoft Power Platform Blog.