Understanding Attention Mechanisms and Transformers for Data Science in Microsoft Fabric
In this edition, we’re exploring attention mechanisms and why they became such a big deal in modern deep learning. By the time you reach the end, you will have a clear sense of what problem attention was meant to solve, why older sequence models struggled, and how attention fits naturally inside transformer models using a torch-style way of thinking.Fabric IQ: from booked hours to next year's forecast
You might remember that the first time you asked an AI agent about next year's project hours, it told you it could not predict the future. Fair enough. It had historical data, not a forecast. In this build, we gave it one. Fabric Plan generated monthly forecasts from booked hours. We wrote those values back to a database, connected them to the business entities in an ontology, and let a data agent answer questions about them. That distinction matters. Predict produced the numbers. The data agent retrieved and explained them. The interesting part was getting those pieces to agree on what "forecast" meant. We used synthetic planning, procurement and maintenance data. Forecasting connected the planning model, ontology and agents. Live telemetry and graph exploration showed where the other pieces fit, and where they did not. Disclaimer: These are notes from a hands-on lab built on synthetic data, and the findings are our own. Fabric IQ features and limits change quickly, so treat the Microsoft Learn and official documentations in the references as the authoritative source. Put a forecast behind the question The starting point was a planning semantic model with actual booked hours by project, discipline and month. In Fabric Plan, closed periods took those actuals. Open periods held editable forecast values. We used Predict [1] to populate the open periods from historical data. For monthly forecasting, the current guidance calls for at least 24 months of history; some algorithms need more. Our sample history went back to September 2023. The forecast was then written to a Fabric SQL database and exposed through a OneLake shortcut in the gold lakehouse. We added a ProjectForecast entity to the ontology, linked to Project and Discipline. The data agents could now reach the forecast as data rather than being asked to invent it. The question became: Which projects have the most forecast hours in 2027? That has a queryable answer: select the forecast scenario, filter the year, group the detail rows by project and rank the totals. Writing back the forecast did not automatically add it to the original semantic model. We had to expose that new table deliberately. This demonstrated the workflow, not forecast accuracy. The data was synthetic, and we did not backtest the forecast against held-out actuals. A correct answer about stored predictions is a different test from whether those predictions will be useful. Two forecasts, two different questions The first problem was not statistical. It was vocabulary. Our planning semantic model already had a measure called Forecast Hours At Completion. It estimated total hours at completion from baseline hours and earned-value progress. It was not a month-by-month prediction. Asked for the 2027 forecast, the agent initially chose that measure, applied a 2027 filter and found nothing. The measure name sounded right. The meaning was wrong. We made the distinction explicit in the agent instructions: Monthly, yearly and future-period forecasts use the Plan forecast table. Estimates at completion and overruns use the earned-value measures. An unqualified "forecast" means the Plan forecast in this application, and the answer must say so. After that change, the next-year question returned the expected project ranking. The useful check was not whether the answer sounded confident. It was whether the selected source, filters and totals matched the query we expected. The at-completion estimate also used the current activity snapshot. A date filter did not turn it into a historical estimate. That was another definition worth stating rather than leaving the agent to infer. The writeback table needs rules Our writeback contained more than a tidy list of future values. It included actuals, forecast values, subtotal rows and blank cells. That creates several ways to get a plausible but wrong total. Summing detail and All subtotal rows double-counts hours. Selecting the wrong value column mixes up actuals and forecasts. Treating every blank as zero changes the meaning of missing or unpopulated values. For this table, the query needed to: Select the intended scenario and Hours Forecast value column. Exclude All subtotal rows from project, discipline, year and month. Filter the requested period and aggregate at the required grain. Preserve the distinction between a blank and a recorded zero. Those rules can live in source instructions, but a curated view is easier to reuse when several consumers need the same result. Either way, the agent needs to know what a row means before it starts adding rows together. Ontology adds meaning, not missing data The ontology integration with data agents [2] is in preview. It supplies entities, relationships, definitions and source bindings. The data agent uses that context to choose a source and generate SQL, KQL or DAX; the underlying source executes the query. That separation explained one of our early failures. The agent could see only part of the lakehouse, and the forecast binding pointed elsewhere. Adding business context could not compensate for tables it could not reach. We had to check the actual source configuration and table selection. There was a less obvious problem too. An AI-assisted ontology edit added the forecast entity but repointed a shared connection to the plan database. Other entities still depended on that connection reaching the gold lakehouse. The ontology opened normally; the saved definition exposed the mistake. We corrected the connection and bound ProjectForecast to the shortcut in gold. The lesson was practical: after changing an ontology, inspect the saved bindings and rerun representative questions. A successful edit message is not enough. Shared business meaning belongs in the ontology. Source-specific query guidance belongs with the source. Keeping those responsibilities clear is more useful than repeating the same join rules in every agent prompt. Test the answers, including the expected ones We used the data agent evaluation SDK [3], also in preview, to run question sets and save the results. SQL queries computed expected answers from the sample data, and a grader compared them with the agent responses. That made instruction changes testable. It also exposed a mistake in our test: a planning question used a definition of baseline hours that did not match the semantic model. The apparent agent failure was a disagreement about the expected answer. Query-derived expectations still need review. They stay tied to the data, but a wrong join or business definition will produce the wrong benchmark very consistently. For an ontology comparison, keep the sources, permissions, questions, runtime and expected answers constant. Comparing an agent with access to the full lakehouse against one that can see only a few tables tells you little about the value of the ontology. We also kept answer checking separate from presentation. Inspect the query, result and any generated code, not just the final paragraph. Save the configuration with the run so a later score is comparable. More guidance when needed, analysis after the query Two preview features helped beyond basic question answering. Topics [4] let the agent retrieve relevant sections of longer guidance rather than send all of it with every question. They currently apply to SQL sources on the preview runtime. Rules that always apply still belong in source instructions; detailed definitions can be grouped into topics. The run steps show which topic was opened. The code interpreter [5] works on retrieved data in a sandboxed Python environment. In the procurement demo, it produced a chart of late deliveries by supplier quality rating. That was easier to read than a long answer listing percentages. It can also combine results from multiple sources for analysis. That is different from a database query joining those sources, and it still requires compatible keys and grain. We did not treat a chart of synthetic data as evidence that supplier ratings caused delivery performance. The generated Python and its inputs are part of the answer worth checking. Use the graph for relationships We also materialized part of the ontology as a graph: Supplier, PurchaseOrderLine, WorkOrder, Project and Installation. This made it possible to follow a supplier's order lines to projects and its contractor work orders to installations. It was a useful way to explore connections without writing each join. A connection alone did not prove that a supplier had delayed a project; that still required dates and status filters. The graph projection [6] is in preview and has its own eligibility rules. Our Equipment entity, which also carried Eventhouse telemetry, was not eligible. We kept live readings in the Real-Time Dashboard and operations agent rather than forcing them into the graph. Making the graph work also uncovered a compatibility problem. We changed relationship mappings and some entity keys. Afterwards, that ontology would no longer attach to the data agent, while a copy from before the changes still worked. We used the graph-ready copy for exploration and the earlier copy for the domain agent. We did not isolate which change caused the failure. This was a workaround for our build, not evidence that every solution needs two ontologies. From a sensor reading to a Teams recommendation The operations agent [7] covered a different job: watching a signal rather than waiting for a question. Our preview setup monitored simulated pump telemetry in an Eventhouse. Every five minutes it evaluated peak vibration and alerted when the value crossed above 5 mm/s. We observed the Teams recommendation and used Investigate further to explore the alert. This was a simulated bearing-wear scenario, not a validated fault diagnosis. The threshold identified a condition to investigate. The transition rule mattered. "Crosses above" is not the same as "is above." For a repeatable test, we first let the agent observe a below-threshold window, then introduced the fault. Restarting the stream or leaving an empty window did not guarantee a new crossing. We configured a Power Automate action with human approval, but did not complete end-to-end verification of the approved action's result. Its parameters were fixed for the demo pump, not dynamically mapped for every asset. The confirmed result was the alert and investigation, not an automatically created maintenance request. The client still matters Fabric IQ appearing in several clients does not mean each client can reach every Fabric item in the same way. Microsoft 365 Copilot Chat [8] can use Power BI reports and semantic models. The Copilot Cowork plugin [9] currently reaches semantic models through their reports; it does not directly ground answers in lakehouses, Eventhouses, ontologies or data agents. Existing Power BI permissions and row-level security apply. Publishing a Fabric data agent as a Microsoft 365 agent is a separate integration path [10]. It should not be confused with the Power BI connection. We used Prep data for AI [11], which is in preview, for model-level AI instructions and schema selection. We also covered verified answers for recurring questions. Preparing the model does not make the separate forecast table available to a client that cannot query it. Before choosing a client, check which source it can reach and how the answer is produced. The Fabric IQ name alone does not settle that. What changed We started with an agent that could answer questions about recorded work. We ended with one that could also answer questions about a stored forecast. The change was not a more ambitious prompt. It was a forecast with a defined grain, an accessible table, business context and explicit routing between two different meanings of the same word. That is the useful part of this Fabric IQ build: planning produces values that can be queried alongside operational data. The forecast still needs validation, and the answer still needs checking. But "next year" no longer has to mean "there is no data." Try it yourself: sample data, semantic models and data agent setup on GitHub References (Microsoft Learn, accessed 16 October 2026) [1] Predict forecasts: https://learn.microsoft.com/fabric/iq/plan/planning-forecasting/planning-how-to-generate-statistical-forecasts-using-predict-feature [2] Ontology in a data agent: https://learn.microsoft.com/fabric/data-science/data-agent-ontology-sources [3] Evaluate a data agent: https://learn.microsoft.com/fabric/data-science/evaluate-data-agent [4] Data agent topics: https://learn.microsoft.com/fabric/data-science/data-agent-topics [5] Code interpreter: https://learn.microsoft.com/fabric/data-science/data-agent-code-interpreter [6] Ontology graph: https://learn.microsoft.com/fabric/iq/ontology/how-to-use-ontology-graph [7] Operations agent: https://learn.microsoft.com/fabric/real-time-intelligence/operations-agent [8] Fabric IQ in Copilot Chat: https://learn.microsoft.com/fabric/iq/connectors/microsoft-365-copilot-overview [9] Fabric IQ in Copilot Cowork: https://learn.microsoft.com/fabric/iq/connectors/cowork-overview [10] Data agent in Microsoft 365 Copilot: https://learn.microsoft.com/fabric/data-science/data-agent-microsoft-365-copilot [11] Prep data for AI: https://learn.microsoft.com/power-bi/create-reports/copilot-prepare-data-ai
Production-grade Fabric data agents: moving from Demo to Dependable
Getting a Fabric data agent to work in a demo is one thing. Supporting it once people depend on it is another. Using the Lakeshore Retail demo, this guide covers the practical work behind that transition: reliable data, ontology context, secure access, performance testing, guardrails, and day-two operations. Tutorial data - Lakeshore Retail. The inventory is a CSV reference, not an agent answer; benchmarks are proposed, not measured. 1. Define the service before tuning the prompt Start with verifiable questions: inventory below safety stock, inbound shipments, governed sales measures, and refrigeration readings over a defined window. Agree on definitions, units, time zone, snapshot selection, freshness, and an answer owner. Low stock is not a shortage forecast. Two high-temperature readings do not prove a continuous breach: cadence, gaps, quality, and boundaries matter. Store-level relationships do not identify which product occupied a refrigeration unit. Put durable calculations in governed measures, SQL, or KQL. Ordering and other write actions need separate authorization and approval. Figure 1. Reference design; guardrails do not replace authorization. Use the standard runtime for production. Ontology, preview runtime, Foundry integrations/agent guardrails, evaluation SDK, and data-agent source control have separate preview considerations. The core's GA status does not extend to every dependency. 2. Use ontology for meaning, not as a security boundary LakeshoreOntology is now attached to the LakeshoreDataAgent draft, with Standard selected. It supplies entities, relationships, and mappings. SQL, KQL, or DAX executes against underlying sources under user permissions. Figure 2. Tutorial ontology, not proof of query correctness. The integration is preview. Attachment here does not confirm a service-wide fix to the documented new-experience issue. Keep shared definitions in the ontology. Explicitly mount eligible underlying sources before adding source-specific instructions, descriptions, or examples. These settings do not write back to the ontology. 3. Establish one reference answer For the fixed 2026-08-28T18:50:00Z snapshot, select positions where: LocationType = STORE StorageClass = FROZEN OnHandQuantity < SafetyStockQuantity The 48-position inventory fixture has exactly three matches, all Vanilla Bean Ice Cream (P-FRZ-001): Figure 3. A calculation from tutorial files, not a live agent result. For test INV-001, assert exact Store/Product keys, count, quantities, filters, and timestamp, not just three city names. The fixture's InventoryStatus = 1 marks low stock; sample instructions call it healthy. Resolve this first; the reference uses quantities. Do not silently alter data or treat August as current. 4. Prepare and wire the sources Curate objects without losing keys, joins, dates, or measures. Smaller is not automatically better. Use governed Power BI measures, Prep for AI, and Verified Answers. DAX generation uses model metadata and Prep for AI, not agent instructions. SQL/KQL source instructions/examples are separate. AI scoping and hidden fields are not authorization. Column flags do not narrow a selected source table; use supported model configuration or curated objects, not destructive edits to shared models. In development: Check prerequisites: supported paid F2+/P1+ capacity, tenant settings, and same-region agent/source capacities. Use Sources > Add sources > Add an ontology to attach LakeshoreOntology. Review its entities and Ontology data sources. Mount eligible sources for SQL/KQL configuration; prepare the semantic model separately. Set routing, clarification, and response rules, including units, identifiers, and honest failures. Inspect the fixed inventory question's source, generated query, and result. Publish to a restricted test audience; repeat as real consumers. Figure 4. The supplied attached-source draft. It does not establish query, security, or performance success. Validate the published version; republish runtime changes. Standard does not freeze service-managed LLM upgrades. Model Read permits semantic-model queries; authoring needs broader rights. 5. Benchmark the whole request Schema width and preview runtime are experiments, not guaranteed gains. Measure authentication, planning, query generation, source execution, response generation, and wrapper/guardrail work. Fast engine execution does not establish that capacity will fix conversation latency. Figure 5. Proposed experiments, not benchmark results. Vary one factor before combinations: Import, Direct Lake, and DirectQuery have no universal speed ranking. Filter telemetry early. Advanced DAX value indexing currently needs Q&A enabled. Fix questions, data, identities, and versions. Separate new chats/follow-ups and cold/warm runs; interleave baseline/candidates. Never clear shared production caches for a test. Use approved synthetic load with representative volume, cardinality, skew, joins, and freshness; tutorial data cannot prove sizing. Record sample size, p50/p95, timeouts, failures, throttles, correctness, and resources. Count all attempted eligible requests when reporting correct completion within budget, including timeouts. Leave caching off until permission-aware keys, freshness, expiration, and revocation invalidation are proven. 6. Add Foundry without moving the authorization boundary A prompt/hosted agent can use Fabric for orchestration, safety, and escalation, not repair measures, grant access, or replace Fabric's query-generation model. Use defense in depth: Application: authenticate, allowlist tools/arguments, bound calls/concurrency/deadlines, and validate available structured results. Foundry: supported safety controls and orchestration. Fabric and sources: enforce the identity's permissions. Data/semantic layers: business meaning. Use delegated end-user identity for interactive access; test two differently entitled consumers through the whole route. Never silently use the owner's identity. The Fabric tool and Fabric IQ integration differ; the former requires the same tenant. Deployment-principal support does not establish query-principal support. The public published-agent MCP pattern is: https://api.fabric.microsoft.com/v1/mcp/workspaces/{workspaceId}/dataagents/{dataAgentId}/agent Verify OAuth, consent, and networking; private-link routes may need workspace-specific hosts. Keep tokens out of code/logs. Moderation is route-specific, not automatic for arbitrary MCP bridges or nested calls. Test input, tool calls, retrieved content, and output on your route. Safety approval does not prove accuracy or authorization. For hosted guardrails, prove filtering with negative tests. invocations needs invocations_moderation; invocations_ws has no content-safety moderation. Agent-level groundedness/spotlighting are unsupported. Implement and test any missing required control, or block release. Review Code Interpreter/action tools for isolation, retention, and approval. Exclude preview dependencies if policy requires it. 7. Test behavior, not convincing prose Use approved references and holdouts separate from tuning. Assert numbers, dates, filters, and authorized scope deterministically. Test ambiguity, no data, outages, expired identities, follow-up filters, threshold boundaries, gaps, injection, denied fields, and cross-user isolation. Check totals/errors for disclosures too. Test consumers: workspace Admin, Member, and Contributor bypass RLS. Admin success is not an RLS pass. The conversational cap is 25 rows by 25 columns. Compute totals in the source, not from truncated output; offer an authorized report/export route for complete datasets. The preview evaluation SDK uses question/expected_answer columns and a published target. Review detailed steps alongside assertions. Judge scores cannot override failed access checks. Example targets: all critical cases pass; broader correctness >=95%; no disclosures observed in the negative suite; 95% of attempted eligible requests correct within 30 seconds at five concurrent users. These are proposals, not measurements or guarantees. 8. Release a service someone can operate Figure 6. Proposed release gates, not an existing pipeline. Separate dev/test/prod. Version configurations, ontology/models, AI preparation, examples, policies, code, and evaluations. Data-agent source control is preview. Verify target bindings, permissions, region, and freshness. Publish and smoke-test consumers before expanding access. Practice rollback to a known-good release; it does not necessarily restore data, permissions, tools, or the service-managed model. Regress after service changes. Assign business, data, application, security, and operations owners. Monitor outcomes, p95, throttling, freshness, quality, and cost with correlation IDs/versions. Restrict trace access and retention; never log tokens or unrestricted results. Bound transient retries, honor Retry-After, and never retry denials as outages. Add deadlines, circuit breakers, and escalation. Distinguish no matches, no access, and failed queries. Review compliance/geographic boundaries for Foundry, external services, logs, and exports. Fabric's up-to-28-day history retention does not define external retention. Respect Purview/source policies. AI consumption includes instructions, examples, and history; source queries consume separately. Monitor AI Query and engines, budget Foundry/application costs, and compare cost per correct, authorized answer within target. Require evidence for scope, data quality, published bindings, consumer access, correctness, guardrails, load, rollback, ownership, and honest failures. Missing evidence blocks release. Dependable means useful within scope and supportable when things change.Data Days | Data Days Your Way
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Power BI Dataviz World Championships Do you have what it takes? DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 6:30 PM Host: Amit Chandak Back to top SQL DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak Microsoft Cloud & AI Frontier Week: Modernize SQL for AI-Ready Databases Date: June 24, 03:00 AM Host: Diaa Radwan Microsoft Cloud & AI Frontier Week: Power Intelligent Apps and Agents with Azure Databases Date: June 24, 04:00 AM Host: Diaa Radwan Build with SQL + AI: From Prompt to Intelligent Apps Date: June 25, 01:00 PM Host: Matt Gordon, Alpa Buddhabhatti Modeling Real LMS Data in Power BI: Star Schema from a Messy MySQL Source Date: June 28, 06:30 AM Host: Parul Rani Sagar Starting with Data API Builder in 10 minutes Date: June 30, TBD Host: Phillip Burton Designing and Implementing Database Objects in Azure SQL Database Date: July 11, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Advanced Query Techniques in Azure SQL Date: July 12, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Implementing Programmability Objects in Azure SQL Database Date: July 18, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Securing Data Access in Azure SQL Database Date: July 19, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Optimizing Performance and integrity in Azure SQL Database Date: July 25, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Win in the Seams 🧵 Stitching Together Data and Security with Microsoft Fabric, KQL, and Data Logs Date: July 25, 09:30 AM Host: Philippa Burgess Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan Query and modify data with Transact-SQL Self-paced training SQL AI Engineer Certification Training (DP-800) Discover resources to prepare for this exam. DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan Back to top Using AI Microsoft Cloud & AI Frontier Week: Transform Data Silos into AI Fuel – Build an End-to-End Data Foundation Date: June 22, 04:00 AM Host: Mark Torr, Chris Webb, Sarina Stevens Microsoft Cloud & AI Frontier Week: Unify Your Data with OneLake for Analytics, AI and Agents Date: June 24, 01:00 AM Host: Sevgi Guzzella Microsoft Cloud & AI Frontier Week: Turn Data into Intelligent Action with Microsoft Fabric Date: June 24, 02:00 AM Host: Simon Lidberg Microsoft Cloud & AI Frontier Week: Modernize SQL for AI-Ready Databases Date: June 24, 03:00 AM Host: Diaa Radwan Microsoft Cloud & AI Frontier Week: Power Intelligent Apps and Agents with Azure Databases Date: June 24, 04:00 AM Host: Diaa Radwan Microsoft Cloud & AI Frontier Week: Transform Fragmented Data into Trusted AI at Scale – A Roadmap for CDOs Date: June 24, 05:00 AM Host: Seda Teber & Marcel Franke The Future of AI in Microsoft Fabric: Data Agents and Beyond Date: June 24, 08:00 AM Host: Brian Bønk, Philippa Burgess DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak Build with SQL + AI: From Prompt to Intelligent Apps Date: June 25, 01:00 PM Host: Matt Gordon, Alpa Buddhabhatti Data and AI Security and Governance in Microsoft Fabric Date: July 7, 07:30 AM Host: Philippa Burgess Exploring Data with AI and Natural Language Features Date: July 13, 6:00 AM Host: Ilgar Zarbaliyev Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan Back to top New Fabric / Power BI / SQL Users DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak Fabric Data Pipelines Full Course For Beginners (Data Days Edition 2026) Date: June 21, 05:30 AM Host: Ansh Lamba Get Certified: Which Data Exam Fits You Best? Date: June 23, 12pm Host: Dean Jurecic, Taylor Amy Learn KQL in 10 minutes Date: June 29 Host: Phillip Burton Starting with Data API Builder in 10 minutes Date: June 30, TBD Host: Phillip Burton From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Get started with Microsoft data analytics Self-paced training Get started with Microsoft Fabric Self-paced training Introduction to Microsoft Azure Data core data concepts Self-paced training Query and modify data with Transact-SQL Self-paced training DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Back to top Certification Prep Certification Resources Which Data Exam Fits You Best? Date: June 23, 12pm Host: Dean Jurecic, Taylor Amy What to Expect and How to Pass Date: June 25, 8am Host: Heini Ilmarinen, Teemu Multanen Get Certified: (Exam Day) What to Expect and How to Pass (US/EMEA) Date: August 6, 8:00 AM Host: Brian Bønk; Charley Hanania Find a Study Group DP-600, DP-700, DP-800, and PL-300 Free Certification Exam Voucher DP-600, DP-700 or DP-800 Get Certified: (Exam Day) What to Expect and How to Pass (US/EMEA) Date: August 6, 8:00 AM Host: Brian Bønk; Charley Hanania DP-600 - Fabric Analytics Engineer Get Certified: (DP-600) Fabric Analytics Engineer Essentials (APAC) Date: June 17, 3pm Host: Heidi Hasting, Martin Catherall Certifícate: (DP-600) Fabric Analytics Engineer Conceptos Clave Date: June 17, 4pm Host: Renzo Roca, Javier Villegas Get Certified: (DP-600) Fabric Analytics Engineer Essentials (US/EMEA) Date: June 18, 8am Host: Ásgeir Gunnarsson, Rajendra Ongole Certifique-se: (DP-600) Fundamentos de Analytics no Fabric Date: June 18, 12pm Host: Ladislau Andre, Roberto Fonseca DP-600 to Real Project: What the Certification Taught Me (and What It Didn't) Date: July 11, 09:30 PM Host: Parul Rani Sagar Microsoft Certified: Fabric Analytics Engineer Associate(DP 600) Date: July 17, 08:30 PM Host: Inturi Suparna Babu, Ajay Babu Inturi, Upputuri Gopikrishna DP-600 Exam Prep — Fabric Analytics Engineer with Data Analytic Group Date: July 25, 09:00 PM Host: Rajendra Ongole,Lanka, Shashi Prepare for Exam DP-600 Prep resources DP-600 In Depth On-demand recorded sessions Free Certification Exam Voucher DP-600, DP-700 or DP-800 Find a Study Group DP-600, DP-700, DP-800, and PL-300 DP-700 - Fabric Data Engineer DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak Certifique-se: (DP-700) Fundamentos de Dados no Fabric Date: June 15, 12pm Host: Luiz Santana, Percy Machado Certifícate: (DP-700) Fabric Data Engineer Conceptos Clave Date: June 15, 4pm Host: Gonzalo bissio, Keyla Dolores Mendez DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak Get Certified: (DP-700) Fabric Data Engineer Essentials (APAC) Date: June 16, 3:00 PM Host: Mike Fortman, Martin Catherall DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak Get Certified: (DP-700) Fabric Data Engineer Essentials (US/EMEA) Date: June 17, 8:00 AM Host: Aleksi Partanen, Phillip Burton DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 19, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 22, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 22, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 23, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 24, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak Como passar na Certificação DP-700: Guia Definitivo! Date: June 25, 3:00 PM Host: Sidney Cirqueira DP 700- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: June 25, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 25, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: June 26, 6:30 PM Host: Amit Chandak Getting Started with PySpark for DP-700 Date: July 16, 9:00 AM Host: Teemu Multanen DP-700 In Depth On-demand recorded sessions Find a Study Group DP-600, DP-700, DP-800, and PL-300 Free Certification Exam Voucher DP-600, DP-700 or DP-800 Prepare for Exam DP-700 Prep resources DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 22, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: June 25, 6:30 PM Host: Amit Chandak DP-800 - SQL AI Engineer Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan DP-800 In Depth On-demand recorded sessions Find a Study Group DP-600, DP-700, DP-800, and PL-300 Free Certification Exam Voucher DP-600, DP-700 or DP-800 Prepare for Exam DP-800 Prep resources Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan PL-300 - Power BI Data Analyst Using Slicers and What-If Parameters in Power BI Date: June 15, 6:00 AM Host: Ilgar Zarbaliyev Dynamic Currency Conversion in Power BI Date: June 22, 6:00 AM Host: Ilgar Zarbaliyev Get Certified: (PL-300) Power BI Data Analyst (US/EMEA) Date: June 22, 8am Host: Ilgar Zarbaliyev, Doher Drizzle Pablo Certifique-se: (PL-300) Fundamentos de Análise de Dados com Power BI Date: June 22, 12pm Host: Brendell Silva Gomes, Miguel Felix Get Certified: (PL-300) Power BI Data Analyst (APAC) Date: June 23, 3pm Host: Anupama Natarajan, Chris Hyde Certifícate: (PL-300) Power BI Data Analyst Conceptos Clave Date: June 23, 4pm Host: Adrian Fernandez Zenteno, Ricardo Rincón Implementing Row-Level Security (RLS) Date: June 29, 6:00 AM Host: Ilgar Zarbaliyev Building Interactive Dashboards and Data Alerts Date: July 6, 6:00 AM Host: Ilgar Zarbaliyev Microsoft Certified: Power BI Data Analyst Associate Date: July 18, 5:30 AM Host: Inturi Suparna Babu, Ajay Babu Inturi, Upputuri Gopikrishna Performing Analytics in Power BI using DAX Date: July 20, 6:00 AM Host: Ilgar Zarbaliyev Prepare for Exam PL-300 Prep resources PL-300 In Depth On-demand recorded sessions Free Certification Exam Voucher DP-600, DP-700 or DP-800 Find a Study Group DP-600, DP-700, DP-800, and PL-300 Back to top Data Engineering Deep Dives Managing secure access, trusted discovery & data sharing with OneLake (3 sessions) Date: June 16, 9:00 AM Host: Josh Ndemenge Tenant management with Sempy Date: June 16, 11:00 AM Host: Taylor Amy, Teemu Multanen DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak Orchestrating Fabric Spark and Best Practices for Production-Ready Workload Date: June 19, 8am Host: Santhosh Kumar Ravindran; Ashit Gosalia Data Ingestion and Discovery in Fabric Date: June 21, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak Modeling Real LMS Data in Power BI: Star Schema from a Messy MySQL Source Date: June 28, 06:30 AM Host: Parul Rani Sagar Orchestrating Pipelines, and Notebooks in Fabric Date: July 4, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Designing Data Load Strategies in Fabric Date: July 5, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Inside Fabric Runtime 2.0: Spark 4 and Delta 4 in Action Date: July 15, 4:00 PM Host: Arshad Ali and Miles Cole Building Scalable Bronze Layer in Microsoft Fabric Date: July 23, 09:00 AM Host: Aleksi Partanen, Teemu Multanen Managing secure access, trusted discovery & data sharing with OneLake (3 sessions) Date: June 16, 9:00 AM Host: Josh Ndemenge DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak Inside Fabric Runtime 2.0: Spark 4 and Delta 4 in Action Date: July 15, 4:00 PM Host: Arshad Ali and Miles Cole Back to top Dataviz Using Slicers and What-If Parameters in Power BI Date: June 15, 6:00 AM Host: Ilgar Zarbaliyev Power BI Dataviz World Championships: Start Your Journey to Barcelona Date: June 16, 8am Host: Valerie Junk, Lakshmi Ponnurasan Power BI Dataviz World Championships: Comece sua jornada para Barcelona Date: June 16, 2pm Host: Samyr Moises, Dirceu Moraes Resende Power BI Dataviz World Championships: Comienza tu camino a Barcelona Date: June 16, 4pm Host: Walter Calcagno, Lucrecia Krause Dynamic Currency Conversion in Power BI Date: June 22, 6:00 AM Host: Ilgar Zarbaliyev Building Interactive Dashboards and Data Alerts Date: July 6, 6:00 AM Host: Ilgar Zarbaliyev Inside the Mind of a Dataviz World Champion Date: July 14, 8:00 AM Host: Valerie Junk; Santhana Lakshmi Ponnurasan Performing Analytics in Power BI using DAX Date: July 20, 6:00 AM Host: Ilgar Zarbaliyev From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Power BI Dataviz World Championships Do you have what it takes? Inside the Mind of a Dataviz World Champion Date: July 14, 8:00 AM Host: Valerie Junk; Santhana Lakshmi Ponnurasan From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Back to top Community connections Global Fabric Day 2026 Date: June 27 Host: Kim Manis Data & IA sous contrôle - Meetup MTG:Bordeaux Juillet 2026 - Data Days Edition Date: July 2, 9:30 AM Host: Youva Gharout, Iuliia Mazur, Pierre Chaumont, Xavier Noya, Christian Bonnaud, Alexandre Nédélec Build Connections. Grow Your Career. Start Here for Your Community in India. Date: July 13, 11:00 PM Host: Amit Chandak; Vinodh Kumar Stop Lurking, Start Connecting: You Belong In the Microsoft Data & AI Communities Date: July 16, 9:00 AM Host: Teemu Multanen; Santhana Lakshmi Ponnurasan; Mike Fortman Data Days Hotline: No Slides, Just Answers Date: July 22, 8:00 AM Host: Johan Ludvig Brattås; Markus Ehrenmueller-Jensen Data Days Lightning Talks: Short Talks. Big Ideas. Zero Fluff. Date: July 30, 8:00 AM Host: Jennifer Ratten; Stephanie Bruno Data Days Universidad ICESI - Cali, Colombia Date: August 5, 12:00 PM Host: Álvaro Rodríguez, Cristhian Cabra, Angely Andrade, Andrés Gallego Connect on Reddit Fabric Fina a local Azure Group On the Meetup Find a local User Group On Meetup Find a Study Group DP-600, DP-700, DP-800, and PL-300 More Ways to Connect Find your community Start a New User Group Learn more about User Groups Build Connections. Grow Your Career. Start Here for Your Community in India. Date: July 13, 11:00 PM Host: Amit Chandak; Vinodh Kumar Stop Lurking, Start Connecting: You Belong In the Microsoft Data & AI Communities Date: July 16, 9:00 AM Host: Teemu Multanen; Santhana Lakshmi Ponnurasan; Mike Fortman Data Days Hotline: No Slides, Just Answers Date: July 22, 8:00 AM Host: Johan Ludvig Brattås; Markus Ehrenmueller-Jensen Data Days Lightning Talks: Short Talks. Big Ideas. Zero Fluff. Date: July 30, 8:00 AM Host: Jennifer Ratten; Stephanie Bruno Back to top Spanish Certifícate: (DP-700) Fabric Data Engineer Conceptos Clave Date: June 15, 4pm Host: Gonzalo bissio, Keyla Dolores Mendez Power BI Dataviz World Championships: Comienza tu camino a Barcelona Date: June 16, 4pm Host: Walter Calcagno, Lucrecia Krause Certifícate: (DP-600) Fabric Analytics Engineer Conceptos Clave Date: June 17, 4pm Host: Renzo Roca, Javier Villegas Certifícate: (PL-300) Power BI Data Analyst Conceptos Clave Date: June 23, 4pm Host: Adrian Fernandez Zenteno, Ricardo Rincón Study Group: From Power BI to Microsoft Fabric: The DP-600 Analytics Engineer Vision Date: June 25, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Certifícate: Preparación para el examen – Qué esperar y cómo aprobar Date: June 25, 4pm Host: Gaston Cruz, Keyla Dolores Mendez Study Group: Operational Architecture in Fabric: Workspaces, Capacity, Governance, and Permissions Date: July 1, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Cierre de la temporada 3 - Data Days Edition Date: July 1, 10:00 AM Host: Ana María Bisbé, Diana Aguilera Reyna, Nelson López Centeno Study Group: Martes 7 de julio - Diseño e implementación de soluciones analíticas en Microsoft Fabric Date: July 7, 2026 Host: AP Data & IA Track: DP-700 | Location: Virtual Study Group: OneLake and Lakehouse: Data Strategy, Ingestion, and Unified Access Date: July 8, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Study Group: Jueves 9 de julio - Ingesta, transformación y procesamiento de datos Date: July 9, 2026 Host: AP Data & IA Track: DP-700 | Location: Virtual Data Days Edition by BIExpert - Fabric Data Engineering Date: July 10, 4:00 PM Host: Nicolas Nakasone, Natali Lujan Power BI + MCP: la nueva forma de conectar la IA con tus datos (6 sesiones, en español) — 13 de julio – 17 de agosto de 2026 Date: July 13, 07:00 AM Host: Vicente Antonio Juan Magallanes Study Group: Martes 14 de julio - Monitoreo, rendimiento y optimización de cargas de trabajo Date: July 14, 2026 Host: AP Data & IA Track: DP-700 | Location: Virtual Study Group: Data Warehouse in Fabric: Dimensional Modeling and Analytical Design Date: July 15, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Study Group: Jueves 16 de julio - Preparación para el examen DP-700 y sesión abierta de preguntas y respuestas Date: July 16, 2026 Host: AP Data & IA Track: DP-700 | Location: Virtual Study Group: Data Days - Grupo de Estudio DP-600 / DP-800 Date: July 18, 2026 Host: Cloud Experts Community Track: DP-600 | Location: In-Person | University Norbert Wiener Study Group: Data Preparation in Fabric: Quality, Transformation, SQL, and KQL Date: July 22, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Study Group: Sesión 1: Diseño y desarrollo de soluciones SQL Date: July 28, 2026 Host: AP Data & IA Track: DP-800 | Location: Virtual Study Group: Enterprise Semantic Models: DAX, Direct Lake, and Power BI Performance Date: July 29, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Study Group: Sesión 2: Seguridad, optimización y administración de soluciones SQL con confianza Date: July 30, 2026 Host: AP Data & IA Track: DP-800 | Location: Virtual Agentes inteligentes con Microsoft Fabric: MCP, LLM y datos empresariales (6 sesiones, en español) — 31 de julio – 4 de septiembre de 2026 Date: July 31, 07:00 AM Host: Vicente Antonio Juan Magallanes Study Group: Sesión 3: Incorpo Date: August 4, 2026 Host: AP Data & IA Track: DP-800 | Location: Virtual Study Group: End-to-End Fabric Solution: Security, Governance, Lifecycle, and DP-600 Preparation Date: August 5, 2026 Host: Comunidad Power BI, Fabric & AI en Español Track: DP-600 | Location: Virtual Data Days Universidad ICESI - Cali, Colombia Date: August 5, 12:00 PM Host: Álvaro Rodríguez, Cristhian Cabra, Angely Andrade, Andrés Gallego Data Days Edition by BIExpert - Fabric Data Engineering Date: August 7, 04:00 PM Host: Nicolas Nakasone, Natali Lujan Data Days Edition by BIExpert - Fabric Data Engineering Date: July 10, 4:00 PM Host: Nicolas Nakasone, Natali Lujan Back to top Português Certifique-se: (DP-700) Fundamentos de Dados no Fabric Date: June 15, 12pm Host: Luiz Santana, Percy Machado Power BI Dataviz World Championships: Comece sua jornada para Barcelona Date: June 16, 2pm Host: Samyr Moises, Dirceu Moraes Resende Certifique-se: (DP-600) Fundamentos de Analytics no Fabric Date: June 18, 12pm Host: Ladislau Andre, Roberto Fonseca Study Group: Serie 1 - Preparação para o Exame PL-300 (Power BI Data Analyst) Date: June 21, 2026 Host: Fabric Lusofono Track: PL-300 | Location: Virtual Building a Medallion Architecture in Microsoft Fabric Date: June 22 Host: Ladislau Andre Certifique-se: (PL-300) Fundamentos de Análise de Dados com Power BI Date: June 22, 12pm Host: Brendell Silva Gomes, Miguel Felix Designing Modern Data Architectures with Microsoft Fabric Date: June 23, 11:00 AM Host: To be announced Certifique-se: Dia do Exame — O que esperar e como passar Date: June 25, 12pm Host: Luiz Santana, Miguel Felix Como passar na Certificação DP-700: Guia Definitivo! Date: June 25, 3:00 PM Host: Sidney Cirqueira Medallion Architecture + Data Mesh Architecture Date: June 26, TBD Host: Ladislau Andre Introdução à Análise de Dados da Microsoft Date: June 27, 08:00 PM Host: Shalom André, Filomena Adão Semantic Models in Power BI and Fabric Date: June 28, TBD Host: Ladislau Andre Study Group: Serie 2 - Preparação para o Exame DP-600 (Fabric Analytics Engineer) Date: June 28, 2026 Host: Fabric Lusofono Track: DP-600 | Location: Virtual Semantic Models Schedule Refresh with Pipelines Date: June 30, TBD Host: Ladislau Andre Semantic Models on Fabric notebooks Date: July 2, TBD Host: Ladislau Andre Semantic Models In Enterprise BI Date: July 4, 11:00 AM Host: Ladislau Andre Preparar Dados para Análise com o Power BI Date: July 4, 08:00 PM Host: Shalom André, Evaristo Quiosa Study Group: Serie 3 - Preparação para o Exame DP-700 (Fabric Data Engineer) Date: July 5, 2026 Host: Fabric Lusofono Track: DP-700 | Location: Virtual Modelar Dados com o Power BI Date: July 11, 08:00 PM Host: Shalom André Study Group: Serie 4 - Preparação para o Exame DP-800 (SQL Developer & AI Solutions) Date: July 12, 2026 Host: Fabric Lusofono Track: DP-800 | Location: Virtual Criar Relatórios Eficazes no Power BI Date: July 18, 08:00 PM Host: Shalom André Pare de scrolar e comece a se conectar: você pertence às comunidades de Dados e IA da Microsoft. Date: July 21, 12:00 PM Host: Alison Pezzott; Sidney Oliveira Cirqueira Dentro do Cérebro de campeões mundiais de Dataviz Date: July 21, 2:00 PM Host: Percy Machado; Samyr Moises; Paulo Grijó Get Certified SQL+AI (DP-800): Projete e Desenvolva Soluções SQL como um Profissional Date: July 23, 12:00 PM Host: Armando Lacerda; Ladislau André Gerenciar e Proteger o Power BI Date: July 25, 08:00 PM Host: Shalom André, Milton Dunda Preparação para o Exame e Sessão Q&A Date: July 26, 08:00 PM Host: Shalom André De “tô só começando” para “caramba, eu fiz isso” Date: July 28, 12:00 PM Host: Hugo Venturini; Dirceu Moraes Resende Get Certified SQL+AI (DP-800): Proteja, Otimize e Entregue Soluções SQL com Confiança Date: July 30, 12:00 PM Host: Brendell Silva Gomes; Eda Oliviera Get Certified SQL+AI (DP-800): Leve IA para o SQL com Embeddings, Busca e RAG Date: August 4, 12:00 PM Host: Luis Gustavo Nascimento Serra; Thiago Zavaschi Pare de scrolar e comece a se conectar: você pertence às comunidades de Dados e IA da Microsoft. Date: July 21, 12:00 PM Host: Alison Pezzott; Sidney Oliveira Cirqueira Dentro do Cérebro de campeões mundiais de Dataviz Date: July 21, 2:00 PM Host: Percy Machado; Samyr Moises; Paulo Grijó Get Certified SQL+AI (DP-800): Projete e Desenvolva Soluções SQL como um Profissional Date: July 23, 12:00 PM Host: Armando Lacerda; Ladislau André De “tô só começando” para “caramba, eu fiz isso” Date: July 28, 12:00 PM Host: Hugo Venturini; Dirceu Moraes Resende Get Certified SQL+AI (DP-800): Proteja, Otimize e Entregue Soluções SQL com Confiança Date: July 30, 12:00 PM Host: Brendell Silva Gomes; Eda Oliviera Get Certified SQL+AI (DP-800): Leve IA para o SQL com Embeddings, Busca e RAG Date: August 4, 12:00 PM Host: Luis Gustavo Nascimento Serra; Thiago Zavaschi Back to top French Study Group: Building the Microsoft Fabric Analytics Foundation (French | Virtual) Date: July 18, 2026 Host: Data & AI France Study Group Track: DP-600 | Location: Virtual Study Group: Prepare data with Power BI desktop and Power Query (French | Virtual) Date: July 25, 2026 Host: Data & AI France Study Group Track: PL-300 | Location: Virtual Study Group: Designing and Optimizing Semantic Models in Microsoft Fabric (French | Virtual) Date: July 25, 2026 Host: Data & AI France Study Group Track: DP-600 | Location: Virtual Study Group: Designing Modern SQL Database Solutions (French | Virtual) Date: July 30, 2026 Host: Data & AI France Study Group Track: DP-800 | Location: Virtual Study Group: Model data with Power BI Desktop (French | Virtual) Date: August 1, 2026 Host: Data & AI France Study Group Track: PL-300 | Location: Virtual Study Group: Building Enterprise Reports, Governance and End-to-End Analytics (French | Virtual) Date: August 1, 2026 Host: Data & AI France Study Group Track: DP-600 | Location: Virtual Study Group: Ingesting and Managing Data with Microsoft Fabric (French | Virtual) Date: August 1, 2026 Host: Data & AI France Study Group Track: DP-700 | Location: Virtual Study Group: Visualize, secure and deploy data on the Power BI service (French | Virtual) Date: August 2, 2026 Host: Data & AI France Study Group Track: PL-300 | Location: Virtual Study Group: Transforming and Engineering Data at Scale (French | Virtual) Date: August 8, 2026 Host: Data & AI France Study Group Track: DP-700 | Location: Virtual Study Group: Developing and Querying SQL Databases (French | Virtual) Date: August 8, 2026 Host: Data & AI France Study Group Track: DP-800 | Location: Virtual Data & IA sous contrôle - Meetup MTG:Bordeaux Juillet 2026 - Data Days Edition Date: July 2, 9:30 AM Host: Youva Gharout, Iuliia Mazur, Pierre Chaumont, Xavier Noya, Christian Bonnaud, Alexandre Nédélec From rows to reasoning: Designing databases for AI apps and agents - Morocco Data Days Edition, Date: July 30, 11:00 AM Host: ANAS BELABBES Back to top Japanese / 日本語 Study Group: DP 600 Session 1 Date: July 8, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-600 | Location: Virtual Study Group: DP 700 Session 1 Date: July 9, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-700 | Location: Virtual Study Group: DP 700 Session 2 Date: July 15, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-700 | Location: Virtual Study Group: DP 600 Session 2 Date: July 16, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-600 | Location: Virtual Study Group: DP 700 Session 3 Date: July 18, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-700 | Location: Virtual Study Group: DP 700 Session 4 Date: July 20, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-700 | Location: Virtual Study Group: DP 600 Session 3 Date: July 22, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-600 | Location: Virtual Study Group: DP 600 Session 4 Date: July 29, 2026 Host: Japan Microsoft Data Platform User Group Track: DP-600 | Location: Virtual Back to top Hindi Road to Microsoft Data Days Date: June 15, 05:30 AM Host: Aman Jindal Prepare for the Microsoft Data Days Date: June 22, 5 am Host: Aman Jindal DP 700 Hindi- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: July 19, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: July 20, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: July 21, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: July 22, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: July 23, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: July 26, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: July 27, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: July 28, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: July 29, 06:30 PM Host: Amit Chandak DP 700 Hindi- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: July 30, 06:30 PM Host: Amit Chandak Back to topSkills for Fabric in practice: tune them, write your own, close the synonyms gap (Part 2 of 2)
How to use Microsoft’s Skills for Fabric day to day, tune them to your team’s standards without forking them, and write your own. Then a full walkthrough of a new skill that adds Q&A and data-agent synonyms to a Power BI semantic model, the one AI-readiness setting that neither the Power BI MCP server nor the official skills will touch. Tested end to end in Power BI Desktop.Forecasting With Confidence using Prophet for Data Science in Microsoft Fabric
In this edition, we’re exploring how Prophet actually thinks about time. By the end, you should have a much clearer sense of how trends, seasonality, and real-world disruptions fit together, and why that framing often matters more than complex math. We will also explore interpretation, because getting numbers is easy, but knowing what they really mean is where the real value shows up.Power BI MCP, Fabric MCP and Skills for Fabric in VS Code: setup and limits (Part 1 of 2)
A practical setup guide for Power BI and Fabric MCP servers and Microsoft’s Skills for Fabric in VS Code, covering the extension route, the remote servers, all four ways to install the skills (including the ZIP download and the trap that comes with it), and an honest list of what these tools can and can’t do. Checked against Skills for Fabric v0.3.18.Microsoft Fabric Data Agent Security: What Actually Protects Your Data?
Most Microsoft Fabric Data Agent demos begin the same way: connect a semantic model, Lakehouse, or Warehouse, add some instructions, ask a natural-language question, and get an answer. That's the exciting part. But the moment we move from a demo to a real enterprise implementation, the questions change: Whose identity is actually querying the data? Does sharing the Data Agent also share the underlying data? Can the agent bypass RLS or CLS? Does Object-Level Security still protect metadata? What happens if the same data is reachable through multiple query paths? These are the questions we set out to answer during our Fabric Data Agent POC. The most important principle that came out of it was surprisingly simple: The Fabric Data Agent should not be your security layer. Your data platform should be. That one principle makes the rest of the architecture much easier to reason about. A Data Agent Does Not Get Unlimited Access to Fabric When someone uses a Fabric Data Agent directly in Fabric, the interaction normally runs in the context of the authenticated Microsoft Entra ID user. Fabric manages the underlying Azure OpenAI service, so users don't need to supply an OpenAI API key, and the Data Agent doesn't suddenly receive unrestricted access to every table in the workspace. When the agent needs data, it generates the appropriate SQL, DAX, or KQL and queries the underlying source using the permissions available to the calling identity. Fig. Security Path The important part is the middle: the agent generates the query, but the data platform decides whether that query can return the requested data. That is exactly where enterprise security should live. Sharing a Data Agent Does Not Mean Sharing the Data There are effectively two authorization checks: Can the user access the Data Agent itself? Can the user access the underlying data source? Fig. Two Authorization Checks Fabric doesn't elevate a user's access just because a Data Agent has been shared with them. Users still need the applicable permissions on the underlying source. Queries that touch data they can't access can fail authorization or return no data, depending on the source and its security model. This separation is critical for enterprise deployments: you can distribute one common analytical assistant while still preserving different data entitlements for Finance, Sales, Operations, or regional teams. Semantic Models Have an Interesting Permission Model Power BI semantic models are especially interesting here. Microsoft documents that a consumer can query a semantic model through a Data Agent with Read permission. They don't need: Workspace Member Workspace Contributor Build permission Write permission simply to ask questions through the Data Agent. (Build or Write permissions remain relevant when someone needs to modify the model or use capabilities that change its AI configuration, such as Prep for AI.) From a least-privilege perspective, this is a very useful improvement. Instead of granting broad workspace or semantic-model permissions just because somebody needs conversational analytics, you can grant only the narrower access required for consumption. What Happens to RLS and CLS? This is probably the security question that comes up most often around conversational analytics: "If the AI generates the query, can it somehow bypass RLS?" For supported Data Agent interactions, Microsoft explicitly documents that existing user permissions continue to apply, including Row-Level Security (RLS) and Column-Level Security (CLS). Fig. RLS — One Agent Three Users All three users can use the same published Data Agent, yet the semantic model can return different populations because the security context belongs to the user. The agent doesn't need instructions like: If User = John, only show Europe. In fact, that would be the wrong way to implement security. The model should enforce the rule. The Data Agent should operate inside it. What About OLS? We tested Object-Level Security (OLS) rather than assuming how the Data Agent would behave. In our model, we applied OLS to Customer[Company Name] in the Power BI semantic model and tested the Data Agent using the restricted identity. We then asked: "Show Total Sales by Company Name." Fig. Data Agent does not expose the OLS-protected Company Name field. The Data Agent responded that the model does not contain a Company Name field and suggested Customer Name instead. Fig. Run Steps show the semantic model was analyzed, but the protected field was unavailable to the agent. That was the key result. Company Name still physically exists in the semantic model, but for the OLS-restricted user, the Data Agent behaved as though the field didn't exist. In our tested path, OLS protected not only the data but also the object's metadata. Our test confirmed OLS enforcement for the tested Fabric Data Agent → Power BI semantic model path, including metadata hiding. There's an important qualification: this remains query-path specific. If the same underlying data is also exposed through a Lakehouse, Warehouse, SQL endpoint, or another source, that route needs its own security controls. Fig. OLS security Security Is Specific to the Query Path This is one of the broader lessons from our POC. Imagine the same business data can be reached through: Power BI Semantic Model │ └── RLS / CLS / OLS Lakehouse │ └── OneLake / item security Warehouse │ └── SQL / OneLake permissions Securing one route doesn't automatically prove that all the others are equally secure. The semantic model could correctly restrict a user's data through RLS or OLS while the same user has broader access to the underlying Lakehouse. If both are available to the Data Agent, the overall architecture may still be over-permissioned. So the enterprise question isn't simply "Does my semantic model have RLS?" It's: Is every query path available to this Data Agent secured for this identity? Fig. Security Is Specific to the Query Path AI Instructions Are Not Security Policies Consider an instruction like: Never display employee salary information. That may be useful behavioral guidance, but it is not an authorization boundary. A user could phrase the question differently. The orchestration could change. A future runtime could interpret an instruction differently. The instruction could be accidentally edited or removed. The secure implementation puts the control in the data platform (RLS/CLS/OLS and source permissions), with instructions layered on top for behavior. Fig. AI Instructions Are Not Security Policies The distinction is simple: Instructions determine how the agent should behave. Permissions determine what the agent is allowed to see. Never reverse those responsibilities. Reduce the Agent's Data Surface Security isn't only about identity — it's also about how much data you expose to the agent in the first place. When configuring a Data Agent, creators select relevant sources, tables, and schema context. For an enterprise Finance Data Agent, exposing: Dim Date Dim Account Dim Cost Center Fact Actuals Fact Forecast is usually better than exposing hundreds of unrelated HR, customer, and operational tables. It helps in two ways: it reduces unnecessary exposure, and it improves query generation because the agent has fewer irrelevant paths to reason through. But schema selection still isn't authorization. Fig. Three Boundaries Data Agent Querying Is Read-Oriented — With an Important Nuance The normal Data Agent source-query path is designed for analytical retrieval. Its SQL, DAX, and KQL tools retrieve and analyze data rather than modify the underlying source. So a prompt like: Delete all customers with zero revenue. doesn't turn the Data Agent into a transactional database administrator. That lowers risk significantly compared with giving an autonomous agent unrestricted write-capable database tools. But there's a nuance. Code Interpreter Changes the Execution Surface Fabric Data Agent can optionally use Code Interpreter (currently in Preview), which generates and executes Python in a Microsoft-managed sandbox for tasks such as statistics, advanced calculations, data transformation, visualization, and analytical processing. That does not make the underlying source write-enabled. A better mental model: Underlying Source │ │ Governed read ▼ Fabric Data Agent │ ▼ Sandboxed Python │ ▼ Calculated / visualized result Fig. Code Interpreter Surface Source querying remains governed and read-oriented, while Code Interpreter adds an additional analytical execution environment. That extra execution surface should be reviewed deliberately, especially when considering Preview capabilities for sensitive production workloads. Discussed in Part 2 with detailed analysis. Check Out. What We Took Away From the Security POC The cleanest security model for us became: IDENTITY Microsoft Entra ID ↓ AUTHORIZATION Fabric item + source permissions ↓ DATA SECURITY RLS / CLS / OLS / OneLake / SQL ↓ QUERY-PATH SECURITY Validate each available source ↓ AI SCOPE Selected schema ↓ FABRIC DATA AGENT The Data Agent sits on top of the security architecture. It should not replace it. That's the difference between building an impressive AI demo and building an enterprise analytical agent. Fig. Security Architecture Security Is Only the First Layer Once the basic security model is in place, a new set of questions appears: Who can modify the agent? Which runtime should production use? How do we test changes? What happens with Code Interpreter? How should service principals be handled? Where is conversation history stored? What happens when the agent is used from Copilot Studio, Foundry, MCP, or a custom application? Those are no longer just security questions — they're governance questions. Next: Part 2 — Governance, From POC to Production Part 2 focuses on how a production Data Agent should be changed, tested, released, consumed, and governed — including identity models, lifecycle management, evaluation, privacy, data residency, networking, and external consumption: Microsoft Fabric Data Agent Governance: From POC to Production. The central idea remains the same: Do not make the AI responsible for protecting the data. Make the data platform responsible for protecting the data, and make the AI operate inside those boundaries. Click Here to go to Part 2 Related Reading Building a Better Microsoft Fabric Data Agent: Instructions, Visual Policy, and Cost-Aware Design Building and Enriching a Microsoft Fabric Data Agent on a Power BI Semantic ModelFrom Chaos to Clarity: How the Manufacturing Dashboard Helps Everyone Understand the Business
Let's walk through what each page actually shows, and why it matters. Page 1: Customer Insights — "Who are we selling to, and are they happy?" Every business lives or dies by its customers, but it's surprisingly hard to keep track of hundreds of relationships in your head. This page acts like a customer relationship "scoreboard." It shows things like: * How many active customers the business currently has * Which customers are considered healthy (buying regularly, paying on time) versus at risk (going quiet, showing warning signs) * How revenue is spread across different customers and regions — are we relying too heavily on just a few big accounts? Think of it like a doctor's checkup, but for relationships instead of health. If ten customers suddenly go from "healthy" to "at risk," that's a signal to act before they walk away for good — not after the revenue has already dropped and everyone's asking why. Page 2: Production & Supply — "Are we making enough, and can we deliver it?" This is the page for anyone who cares about what's actually happening on the factory floor and getting product out the door. It's less about money and more about operations. Key questions it answers: * How much did we produce this month, and is that on target? * Are shipments going out on time, or are we falling behind on delivery promises? * Is one particular plant underperforming compared to the others? If you're a plant manager, this is likely the first thing you check every morning — before coffee, even. It tells you at a glance whether today is a "business as usual" day or a "we need to fix something now" day. Page 3: Overview — "How's the whole business doing, in 30 seconds?" This is the page built for someone with almost no time to spare — a CEO, an investor, or anyone who just needs the headline numbers without digging through details. At the top sit eight simple cards, each showing one important number and whether it went up or down compared to last month: * Total Revenue — how much money came in * Total Production — how much product was made * Capacity Utilization — how much of the factories' potential is actually being used * Gross Profit — how much money is left after production costs * Supply Fulfillment — how reliably orders are being delivered * Inventory Value — how much stock is sitting in the warehouse * Active Customers — how many customers are currently buying * Overall Operational Efficiency — a single score summarizing how smoothly everything is running Below that, charts break revenue and production down by month, by plant, and by region — so if a number drops, you don't just see that it dropped, you can immediately see where. Was it one plant having a bad month, or a slowdown across an entire region? There's also a simple "Alerts & Insights" section that puts the numbers into plain words — things like "Supply on track: fulfillment is 94% and improving" — so nobody has to guess what a chart is trying to tell them. Page 4: Inventory & Working Capital — "Do we have too much stock, or too little?" This page tackles a balancing act every manufacturing business faces. Keep too much inventory sitting around, and you're tying up cash and warehouse space that could be used elsewhere. Keep too little, and you risk running out of product right when a customer needs it — losing sales and trust. This page shows: * Total Inventory Value — how much money is currently tied up in stock * Inventory Turnover — how quickly that stock is being sold and replaced (a higher number generally means things are moving efficiently) * Days Inventory Outstanding — roughly how many days' worth of stock is sitting around unused * Raw Material vs. Finished Goods Stock — how much is still waiting to be turned into product versus ready to ship * Slow-Moving Inventory — stock that isn't selling and may need attention * Stockout Risk — a warning flag for items at risk of running out * Inventory Accuracy — how well the recorded stock counts match what's physically in the warehouse There's even a detailed table listing specific materials by name, plant, and status (like "Slow-Moving" or "Healthy"), so instead of a vague warning, someone gets a precise to-do list of exactly what needs a closer look. Why This Approach Works for Everyone The real magic of this dashboard isn't any single chart — it's the consistency. Every page follows the same basic structure: 1. Filters at the top (date range, region, plant, product category) so anyone can narrow the view to exactly what matters to them 2. A handful of key numbers, shown as simple cards with an up or down arrow — no complicated formulas to interpret 3. A plain-language "Alerts & Insights" section that explains, in a sentence or two, what changed and why it matters That means a machine operator, a plant manager, a customer success rep, and a CEO can all open the same dashboard and immediately find what's relevant to them — no translation needed, no waiting for someone else to "run the numbers." In a business with as many moving parts as manufacturing, that kind of shared clarity isn't a luxury. It's what keeps everyone — from the shop floor to the boardroom — pointed in the same direction.Is Your Semantic Model Ready for Copilot? A Fabric IQ Readiness Guide
For years, the Power BI semantic model had one main job: feed reports and dashboards. That job has changed. With Fabric IQ, your semantic model is becoming the place Microsoft 365 Copilot and your AI agents get their business answers from. If the model is messy, inconsistent or badly documented, Copilot's answers will be too. This guide explains what Fabric IQ is, why it matters, what it costs, and how to get your models ready in about 30 days. What is Fabric IQ? Fabric IQ is the semantic foundation over your structured business data. It is one of the four pillars of Microsoft IQ, was announced at Build 2026, and is now generally available. It brings three things together: your data in OneLake, the business meaning in your Power BI semantic models, and operational intelligence expressed as ontologies. Microsoft IQ is the umbrella. At Build, Microsoft presented it as the context layer that grounds AI agents in both general world knowledge and the organization's own knowledge. Work IQ covers people, emails, meetings and documents. Fabric IQ covers the numbers: revenue, margin, churn, inventory, and every KPI your business runs on. Medium Why this changes how people use data The biggest change is where people get their data answers. Instead of logging into Power BI to check a metric, business users can ask Copilot in Teams or Outlook a question in plain language and get an answer. Importantly, those answers come from your Power BI semantic models with existing row-level security and sensitivity labels applied. The reach keeps growing. Fabric IQ is integrated with Microsoft Agent 365 as a first-party MCP tool (in preview), and it is also extending into Microsoft 365 Copilot, including Cowork and Copilot Chat. Developers get it too: through Agent Skills for Fabric, Fabric IQ tools are available in GitHub Copilot CLI, so teams can query Power BI reports and semantic models from the command line. Microsoft Azure Put simply, one semantic model now answers questions in reports, in Teams, in Outlook, in custom agents and in the terminal. That is powerful when the model is good and risky when it isn't. This is Microsoft's stated direction, not a side feature. In a July 21, 2026 post on the Fabric Updates Blog about the future of conversational analytics, Microsoft described trusted business context in Fabric IQ that can be used consistently across Microsoft 365, Fabric, developer tools and future AI applications. The catch: reports have been hiding your model's gaps This is the point most teams miss. Fabric IQ skips the visualization layer and queries the model directly, so the model has to work on its own, outside the filter context of any specific report page. Think about how many of your reports quietly fix problems in the model: A page-level filter excludes test customers or cancelled orders A slicer defaults to "current fiscal year," so nobody notices the measure has no time logic A visual title explains what "Net Revenue" actually means Hidden columns and awkward field names only work because report authors know which ones to use When Copilot queries the model directly, none of those fixes apply. A user asks, "What was net revenue last quarter?" and gets a number that includes test data, the wrong calendar, or a measure nobody has used since 2023. Licensing: what you actually need You pay for this in two places: Microsoft 365 side: you need Microsoft 365 Copilot licenses to use Fabric IQ through Teams and Outlook. Fabric side: Fabric IQ grounding doesn't consume extra Fabric capacity from the Microsoft 365 side, but everything Fabric IQ grounds on still needs F-SKU capacity. For budgeting, this means heavier Copilot use doesn't directly raise your Fabric capacity bill for the grounding itself. You still need your models hosted on proper Fabric capacity, sized for the query load that conversational use brings. The 10-point readiness checklist Use this to review each semantic model before you expose it to Copilot. Clear business names. Rename tables, columns and measures in business language. fct_sls_amt_net should become Net Sales Amount. Copilot and your users both work better with plain names. Descriptions on everything that matters. Add descriptions to key tables, columns and measures. Say what each one means, what it includes and excludes, and the unit. This is the model's documentation for AI. One definition per metric. If you have Revenue, Revenue v2, Revenue (Finance) and Rev_Final, Copilot has to guess. Merge them into one certified measure and hide or remove the rest. Move report-level logic into the model. Any filter that must always apply, such as excluding test accounts or internal transactions, belongs in the model (in measures, RLS or the data itself), not on a report page. A proper date table. Mark it as a date table, include fiscal periods if your business uses them, and make sure time-intelligence measures work without a slicer. Hide the plumbing. Hide keys, technical columns, staging fields and helper measures. What stays visible is what Copilot considers fair game. Validate row-level security. Answers respect RLS, which is good. So test RLS properly with real user roles. A gap in RLS is now a gap in every Copilot conversation. Apply sensitivity labels. Label models that contain confidential or personal data so Microsoft's information-protection controls carry through to AI answers. Write AI instructions and version them. "Prep for AI" instructions are written on the semantic model, version-controlled with the model, and now shape Copilot's behaviour everywhere it grounds on that model. Use them to explain business rules, preferred measures and common terms (for example, "'customers' means active customers unless stated otherwise"). Test with real questions. Collect 20 to 30 questions your business users actually ask. Run them through Copilot and compare the answers with your certified reports. Fix any mismatch in the model, not in the report. A 30-day readiness plan Week 1: Inventory and prioritize. List your semantic models and choose two or three high-value, widely used ones, such as sales, finance or operations. Find each model's owner, check its current certification status, and identify which users will get Copilot access first. Week 2: Clean up the model. Work through checklist items 1 to 6: naming, descriptions, one version of each metric, moving report logic into the model, the date table, and hiding technical fields. This week delivers the biggest improvement in answer quality. Week 3: Security and AI instructions. Validate RLS against real roles, apply sensitivity labels, and write your first set of AI instructions. Put the model in source control (Fabric Git integration) if it isn't already, so instructions and changes are tracked. Week 4: Test, pilot and set up governance. Run your question set, fix the mismatches, then pilot with a small group of business users. Agree who owns each model, how changes get approved, and how users report a wrong answer. Governance: treat semantic models like products Once Copilot answers from your models, they are shared business infrastructure, no longer just a BI team's side project. A few habits help: Named owners for every model exposed to Copilot Certification so only reviewed models become the preferred sources Change control through Git and deployment pipelines, because a renamed measure can change hundreds of Copilot answers overnight Feedback loops so a wrong answer becomes a model fix within days, not months The bottom line Fabric IQ changes what a good semantic model is. It used to be a model that powered good reports. Now it has to answer business questions correctly on its own, in any tool, for anyone with permission to ask. Teams that clean up names, fix metric definitions, move logic into the model and write AI instructions now will have Copilot answers people trust. Teams that don't will spend 2027 explaining why Copilot and the dashboard disagree. Start with one model this month. The checklist above is enough to begin.
