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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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.From 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.How to Use Verified Answers in Power BI Visuals
Step-by-Step Walkthrough Start by opening Power BI Desktop. If you prefer to work in the Cloud, you can enable the Large Semantic Model setting to proceed in a similar way. In the Desktop interface, navigate to the section labeled "3.3" (a UI navigation point within the verified answers setup) and select "Verified Answers." This opens a set of optional choices where you can select which visuals should serve as verified answers for specific questions. If your goal is to generate a specific visual as a complete answer for a particular question, add that visual as a verified answer along with an example question. Once configured, click the Apply button to save your selection. From there, move on to additional visuals and repeat the same process. For example, you can ask a question related to revenues and configure a trend graph to appear as the answer whenever that question is prompted. You can also use your own custom input when setting up verified answers, giving you flexibility beyond the default options. In a full demo scenario, three questions were configured — each mapped to a specific visual. When those questions were entered as prompts in Fabric Corporate, the corresponding visuals appeared as output exactly as configured. What to Take Away Targeted Responses: Verified answers ensure Power BI returns only the visuals you have specifically approved, making your reports more focused and relevant. Flexible Setup: You can configure this feature in both Power BI Desktop and the Cloud, with the option to enable the Large Semantic Model on the Cloud side. Language Support: The feature was tested in both English and Turkish, confirming that verified answers work across languages. Tools and Concepts Used Power BI Desktop Power BI Cloud (with optional Large Semantic Model) Verified Answers Feature Prompting and Question-Based Visuals Language Support (English and Turkish) Frequently Asked Questions Q: Can I use verified answers in Power BI Cloud as well as Desktop? A: Yes, verified answers work in both environments. If you are working on the Cloud side, you have the option to enable the Large Semantic Model setting to proceed in a similar way to Desktop. Q: Does verified answers support languages other than English? A: Yes. The feature was tested with Turkish and produced the expected results, confirming multi-language support. Conclusion Verified answers in Power BI offer a practical way to ensure that only the visuals you want are shown in response to specific prompts. Whether you are working in Power BI Desktop or through Microsoft Fabric on the Cloud, this feature helps streamline your reports and improves relevance for your audience. Try configuring verified answers in your next Power BI project to see the benefits firsthand. If you have questions or comments, feel free to share them below. See you in the next post!Lakehouse vs. Warehouse in Microsoft Fabric: Which One Should You Actually Use?
If you're new to Microsoft Fabric, you've probably hit this wall already: you go to create an item, and Fabric hands you two very similar-sounding options - Lakehouse and Warehouse. Both store tabular data in Delta format in OneLake and provide SQL access, but they offer different development and transactional experiences. Both let you query with SQL.Fabric presents both as analytical data-store options, and their capabilities overlap enough that the choice is not always obvious. So which one do you pick? Short answer: it depends on who's writing the queries and what shape your data is in. Long answer: keep reading — and try the quick self-check below before you scroll to the recommendation. 60-Second Self-Check Answer these three questions honestly before you create your next Fabric item: 1. Who will write the transformation logic? - A) Python/Spark notebooks, data engineers comfortable with PySpark - B) SQL analysts and BI developers who live in T-SQL 2. What does your source data look like? - A) Mixed — JSON, Parquet, CSV, streaming events, semi-structured - B) Mostly clean, structured, relational-shaped data 3. What's the end consumption pattern? - A) A mix of ML, notebooks, ad-hoc exploration, and reporting - B) Primarily Power BI reports and governed semantic models Mostly A's → Lean Lakehouse. Mostly B's → Lean Warehouse. Mixed bag? → You're not alone — see the "Can I use both?" section below. Lakehouse: The Flexible One A Lakehouse stores data as files (Delta Parquet) in OneLake, and gives you two ways to work with it: - Notebooks (PySpark, Spark SQL) for engineers who want full control - A SQL analytics endpoint that auto-generates so SQL folks can still query the same tables Pick Lakehouse when: - Your data arrives messy, semi-structured, or in large volumes that benefit from Spark's distributed processing - Your team already thinks in notebooks and data science workflows - You want schema flexibility - Delta tables support schema enforcement and controlled schema evolution, offering flexibility while preserving data reliability. - You're building a medallion architecture (bronze → silver → gold) and need engineering muscle at the bronze/silver layers Watch out for: - The SQL endpoint is read-only - The SQL analytics endpoint is read-only for Lakehouse table data, so it does not support INSERT, UPDATE, or DELETE against those Delta tables. Modify or load the data through Spark or another supported ingestion and transformation experience. You can still create supported SQL objects such as views, functions, and stored procedures in the endpoint. - Fabric provides automatic Delta table optimizations, but advanced workloads may still benefit from deliberate file sizing, data layout, partitioning, or optimization strategies. (OPTIMIZE, V-Order, partitioning) python #Typical Lakehouse bronze-to-silver pattern in a notebook df = spark.read.format("json").load("Files/raw/events/") df_clean = df.dropDuplicates().withColumn("load_date", current_date()) df_clean.write.format("delta").mode("overwrite").saveAsTable("silver_events") Python from pyspark.sql.functions import current_date() df = spark.read.format("json").load("Files/raw/events/") df_clean = df.dropDuplicates().withColumn("load_date", current_date()) df_clean.write.format("delta").mode("overwrite") saveAsTable("silver_events") Warehouse: The Familiar One Fabric Warehouse provides a rich T-SQL-first data warehousing experience. — think of it as a cloud data warehouse that happens to store data in OneLake under the hood. Pick Warehouse when: - Your team's primary skill is T-SQL, not Spark/Python - You need full DML (`INSERT`, `UPDATE`, `DELETE`, `MERGE`) with transactional guarantees - You're modeling a governed, relational structure — star schemas, stored procedures, views - You want a more traditional data-warehouse development experience (cross-database queries, You want a familiar relational warehouse development experience with T-SQL, views, stored procedures, and support for compatible SQL tools.) Watch out for: - Less flexible with wildly semi-structured or streaming-first data — you'll typically land that in a Lakehouse first, then move it in Warehouse is designed primarily for T-SQL rather than Spark-native development. If Spark is central to your transformation logic, Lakehouse is usually the more natural starting point. sql -- Typical Warehouse transformation pattern MERGE INTO dbo.DimCustomer AS target USING staging.CustomerUpdates AS source ON target.CustomerID = source.CustomerID WHEN MATCHED THEN UPDATE SET target.Email = source.Email WHEN NOT MATCHED THEN INSERT (CustomerID, Email) VALUES (source.CustomerID, source.Email); Can I Use Both? Yes - and honestly, A supported and commonly discussed architecture is to use a Lakehouse for engineering-oriented layers and a Warehouse for a curated relational serving layer. do exactly this: - Lakehouse for bronze/silver ingestion and heavy transformation (Spark does the messy work) - Warehouse for the polished gold layer that analysts and Power BI consume with familiar T-SQL Both use OneLake, and Fabric supports patterns such as shortcuts and cross-database queries that can reduce or avoid unnecessary data duplication. If you physically load curated data into separate Warehouse tables, however, that creates another stored representation. Try It Yourself Before your next project kickoff, run this checklist with your team: [ ] Who owns the transformation code - engineers or SQL analysts? [ ] Does the source data need Spark-level flexibility, or is it already relational? [ ] Do you need multi-table transactions and T-SQL DML, or can table changes be implemented through Spark and Delta operations? [ ] Could a hybrid (Lakehouse → Warehouse) actually be the real answer? No data to test with yet? A quick way to practice both patterns above is to grab a free sample dataset (or a ready-made dashboard layout to reverse-engineer) from a site like Docynx. Over to you I'd love to hear how your team decided: Did you go Lakehouse, Warehouse, or both? What tipped the decision — team skill set, data shape, or something else entirely? Drop your setup in the comments — especially if you've got a "we picked wrong and had to migrate" story, those are always the most useful ones.
