Direct Lake Calculated Columns (Preview)
Calculated columns are now available in preview for Direct Lake on OneLake semantic models. This has been one of the most requested capabilities since Direct Lake launched and closes a long-standing gap with Import and DirectQuery storage modes. You can define calculated columns directly in your semantic model using DAX in web modeling and Power BI Desktop, without changing the table storage mode or modifying data upstream. The problem this solves Direct Lake combines Import-like performance with near real-time data freshness by querying Delta tables in OneLake and loading data into memory as needed. But until now, if your model needed a derived column, you had only two options: push the logic upstream into the data source or move the table to a different storage mode. Calculated columns close that gap – you can now define columns directly in the semantic model using DAX without leaving Direct Lake. What you can build Common use cases include: Derived values, such as an age calculated from a birth date. Concatenating fields, such as city and region into a single field for slicers. Formatted dates like Month-Year for grouping in visuals. Multilingual reports using data translations with USERCULTURE(). Personalized content based on functions like USERPRINCIPALNAME(). Examples 1. Age = DATEDIFF( DimCustomer[BirthDate], TODAY(), YEAR ) 2. Customer Location = DimCustomer[CityName] & ", " & DimCustomer[ContinentName] 3. Month Name = FORMAT( DimDate[FirstOfMonth], "mmmm", USERCULTURE() ) The first two return the same value for every user unless security context differs. The third adapts to the language of the person viewing the report – see Month Name. Calculated columns now have a new Expression Context property. Direct Lake calculated columns support User Context expression context only. With User Context: The expression is evaluated when a user queries the model. Evaluation respects row-level security (RLS) and object-level security (OLS). Expressions that do not reference user-aware DAX expressions or secured columns typically return the same value for every user. Considerations: Cannot be used in relationships. Direct Lake calculated columns are evaluated at query time and do not materialize. Because of this, they cannot be used as relationship keys. Build relationship keys in the Delta table when needed. Do not observe filter context. Calculated columns return a fixed value for each row and do not respond to report filters or slicers. Use a measure when the result must change based on report interactions. Unlike standard calculated columns, Direct Lake calculated columns are evaluated in the current user's security context. This means calculations automatically respect RLS and OLS. Security behavior across expression contexts The security difference is easiest to see with a column that references a protected value. Consider a model where OLS hides DimCustomer[Education] from a "NoEducationOLS" role, and a calculated column derived from it: Is Graduate = IF( DimCustomer[Education] = "Graduate Degree", "Yes", "No" ) The result depends on the expression context: Import - Standard Import - User Context Direct Lake - User Context Observes OLS No Yes Yes Result for a restricted user "Yes" or "No", derived from the hidden value Column not available Column not available With Standard, a restricted user still sees the derived answer and the protection is effectively bypassed. With User Context in both Import and Direct Lake, the reference to the secured column is evaluated within the user's security context, so the restricted user does not see the derived column. The sensitive value is never exposed. Import – Standard Expression Context Import – User Context Expression Context Getting Started Calculated columns for Direct Lake are available in preview. Open a Direct Lake on OneLake semantic model in the web or Desktop, create a calculated column, and begin modeling directly in DAX. To learn more, see the Create calculated columns in Power BI Desktop documentation.Modern Power BI architecture choices for reporting on Azure Databricks: A performance benchmark for Power BI storage modes
Many enterprise Power BI semantic models use Azure Databricks as a data source. When building these models, developers and architects face an early and consequential decision: which storage mode to use. Cost, security, and ease of development and tuning all factor in — but report performance is probably the most important of them, because reports that are slow to load are one of the most common causes of end-user dissatisfaction. In practice, that decision is often made on intuition rather than evidence. To help change that, we've published a new white paper, Modern Power BI Architecture Choices for Reporting on Azure Databricks, benchmarking four ways of serving the same Delta tables to a Power BI report: Direct Lake on OneLake — over Delta tables in a Fabric lakehouse or warehouse Direct Lake on mirrored Unity Catalog tables — shortcuts, no copy DirectQuery — on a Databricks SQL warehouse Composite Model on Databricks — DirectQuery combined with Import-mode aggregations Figure: The four Power BI storage modes benchmarked to evaluate their impact on report performance and scalability. What the results suggest: there's no universal winner — but there are clear patterns. Direct Lake on OneLake performed well across the widest range of situations in this benchmark. It's highly competitive at smaller and mid-size volumes, and for the typical Power BI workload — where reports are used repeatedly throughout the day — it delivers interactive performance without extra modeling effort. At the top end of the volume curve, the picture shifts. With billions of rows, a Composite Model with aggregations was the fastest and most consistent pattern, staying sub-100ms on queries the aggregation tables can resolve. The caveat is equally clear: that advantage doesn't extend to queries that fall through to the underlying DirectQuery source, so the payoff depends on how well your aggregations match real user behavior. These are just the headline findings — the detailed results vary considerably by data volume, cache state, filter scenario, and query type. We'd encourage you to read the white paper for the full picture before deciding on a pattern. One thing worth noting up front: this is a point-in-time comparison as of June/July 2026, and both platforms are moving quickly. It also measures the end-user query experience within Power BI, rather than raw database execution speed. Treat it as a guide for running your own testing, on your own data. Explore the white paper for a full walkthrough of through each pattern in detail — the test setup, what was measured, and how results break down by data volume, cache state, and query type. Visit the Power BI download center to access the paper and other related resources.Bringing Power BI Insights to Every Copilot User
The future of Power BI and Copilot starts by meeting users where they already work. We’re bringing trusted Power BI answers into Microsoft 365 Copilot first, so people can ask questions of governed business data in the flow of work — then carrying that richer, more conversational experience back into the Power BI service itself.
Workspace outbound access protection for Power BI reports (Preview)
Workspace outbound access protection (OAP) is a workspace-level control in Microsoft Fabric that lets you constrain where the data inside a workspace can flow. With this preview, OAP support now extends to Power BI reports. The good news for report authors and admins: there's nothing new to configure on the report itself. Protection comes from a single, well-defined rule that the workspace enforces automatically as soon as you turn OAP on.Migrating from embedded ODBC drivers to ADBC in Power BI and Fabric
If you use connectors like Databricks, Snowflake, or BigQuery in Power BI or Fabric, there’s an important change coming. Microsoft is moving from embedded ODBC drivers to ADBC, which affects how connections are established and managed. This post explains what’s changing, who’s impacted, and the steps you can take now to prepare.Building in the Agentic Era with Power BI and Fabric
Microsoft Build 2026 marks a major shift in how developers build data experiences with AI agents. Today we're announcing two capabilities that bring agentic analytics to the forefront: Agent Skills for Power BI, which let developers prompt an AI agent to build and refine semantic models and reports, and Fabric Apps for Semantic Models, which enable AI agents to build and deploy Fabric-native web apps on semantic models. Both capabilities will accelerate the time it takes to go from raw data to a polished analytics solution with just natural language prompts.Outbound Access Protection for semantic models (Preview)
Author: Kay Unkroth, Principl Program Manager - Outbound Access Protection (OAP) is a workspace-level network security and governance feature that blocks outbound traffic from a workspace by default and lets you allow only the destinations you explicitly trust. With this preview, you can now extend OAP to semantic models. Power BI reports aren't part of this preview yet; report support is coming in a separate announcement soon.
Semantic Layers: The foundation of enterprise AI
If you haven’t already, check out Arun Ulag’s hero blog “FabCon and SQLCon 2026: Unifying databases and Fabric on a single, complete platform” for a complete look at all of our FabCon and SQLCon announcements across both Fabric and our database offerings. Power BI is the decision layer for millions of users because it doesn’t just visualize data—it standardizes meaning. Semantic models capture the definitions that businesses run on the measures people trust, the relationships that provide context, and the governance that keeps answers consistent. Microsoft Fabric IQ builds that same semantic layer, extending trusted Power BI definitions into a broader, connected context that can support analytics and AI with fewer gaps and less guessing. Here’s how customers are already putting that vision into action: “Power BI Copilot coupled with trusted data products have become the common language of insight across the enterprise — connecting teams, data, and decisions through a single, trusted analytics platform.” Paul Wellman, Vice President, Enterprise Data & Analytics Platforms, TD Bank Group We’re announcing new capabilities that make Power BI more open, more powerful, and more deeply integrated into the way you work while strengthening the semantic foundation that AI relies on to deliver consistent, trustworthy answers What’s new in Power BI Translytical Task Flows (Generally Available) Power BI has evolved from a reporting tool to an operational surface. With translytical task flows, users can act directly from reports: update records, trigger workflows, or resolve data issues in real time. There’s no need to move into another system or submit a request and wait. Insight and execution now happen in the same place, at the same time. To learn more, visit our translytical task flows documentation. Figure: Translytical task flows in Power BI—take action directly from a report to update records and move work forward without leaving the analytics experience. Modern visual defaults (Preview) With Modern visual defaults, new reports start out looking polished and consistent—so teams spend less time tweaking formatting and more time surfacing insights. The updated base theme aligns with Fluent 2 design, creating a professional visual style across charts, slicers, buttons, and tables right out of the box. Charts, slicers, and buttons also have style presets available, for quick style changes in a couple of clicks, ensuring everyone can present data with clarity and impact from day one. Figure: Modern visual defaults (Preview) in Power BI Desktop—updated Fluent 2 styling and a refreshed base theme give new reports a polished look by default. Report Copilot for Mobile (Preview) Whether you’re in a meeting or on the move, you can now ask questions using voice or text in the Power BI mobile app and receive instant answers or visuals from Copilot. Figure: Copilot in the Power BI mobile app answers a question and generates a visual. TMDL View in the Web (Preview) Tabular Model Definition Language (TMDL) View on the Web is launching in preview soon, bringing a code‑first semantic modeling experience directly to the browser. Developers will be able to view, edit, and apply changes to all semantic model metadata using TMDL, enabling greater transparency, efficiency, automation, and more consistent model development. Figure: Editing semantic model metadata in a code-first TMDL experience in the browser. Direct Lake on OneLake (Generally Available) Reduce refresh overhead and keep data in open formats. Power BI is standardizing open-data formats by adopting Delta Lake and Parquet to help you avoid vendor lock-in and reduce data duplication. Direct Lake storage mode accelerates time to data-driven decisions by unlocking incredible performance directly from OneLake. Figure: Creating a semantic model with Direct Lake on OneLake. Direct Lake on OneLake, now generally available, provides compatibility with OneLake security, more modeling features, and faster query performance. Refer to Direct Lake on OneLake documentation to learn more. Table Visual Updates (Generally Available) We’ve added polish where it matters most. You now have greater control over totals in table visuals, along with cleaner, modern default styles that improve readability. These enhancements may seem small—but across thousands of reports, they save time and elevate the user experience at scale. Direct Lake calculated columns (Preview) You asked, and we listened. Calculated columns for Direct Lake tables will soon be available in preview. When adding columns upstream isn’t feasible (such as when data preparation in OneLake is owned by another team) you can extend Direct Lake tables by creating calculated columns. We are also introducing the ability to make calculated columns user-context aware by dynamically responding to DAX functions including UserCulture(), UserPrincipalName(), CustomData(). This enables new scenarios like data translations, and we’re excited to see the creative ways the community will use this! Figure: Creating a calculated column for a Direct Lake table in the semantic model We’re just getting started—join us! As you explore these updates, continue to follow this space for more deep dive blogs on these feature releases over the next couple of weeks. We’d love to keep the conversation going with you. If you’re joining FabCon Atlanta 2026, build your agenda and attend the Power BI and Fabric sessions that map to your role. Come find us in the Community Lounge or at Ask the Experts to share what’s working and what else you want to see in the product. If you’re not onsite, you can still participate. Post questions and feedback in the Power BI Community forums and influence the product roadmap by sharing and upvoting ideas. Let’s shape the future of data and AI together.
