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.DAX Reference
Overview Learning a new language can be over-whelming. Within the Data Analysis eXpression (DAX) language there are many different commands. However it is quite possible to create a professional Power BI solution using a small sub set of the commands available. Where this small sub set of DAX commands together have a much more manageable learning experience. There are two types of best practice to keep the usage of DAX as simple as possible … • Aim for your data model to be a star schema. • Perform as much transformation within the data load process as possible, either in Power Query (for Power BI) or SSIS for Analysis Services Tabular Model. If you follow the above best practices in full you will be very pleased as to how many insightful and attractive data visualisations you can easily create within a very short period of time. The examples are included within the sample AdventureWorks PowerPivot and AdventureWorks PowerBI solutions so you can gain a greater context of the example code against each function description. Please click on the following link to examine these data models. There is also a DAX query script where some example code is executed within the context of a DAX query as instead of a column or measure within a model. Please download DAX Studio https://daxstudio.org/ to view / analyse and run the DAX query scripts. Training Details of training on Power BI is available from https://www.dataplatformservices.com/ eyJrIjoiYzQ0ZjJmYzItNTNlZi00YmFlLWFlMDAtOTA3MjYwMjlkYzYzIiwidCI6ImY1NmM4YzIyLTQ5ZDMtNDQ5OC1iZThiLWU3MWE0YjVlZGRjNiIsImMiOjh9&pageName=ReportSectionPower BI Best Practice Recommendation
Power BI Best Practice Recommendation This Adventure Works Power BI solution shows example best practice recommendation. Please click on the (i)information buttons on each page for further reading. Training Details of training on Power BI is available from https://www.dataplatformservices.com/ About the Author Kieran Wood is a UK based consultant and trainer focusing on Power BI and SQL Server Analysis Services related technologies such as DAX and MDX. Website: https://www.dataplatformservices.com/ Twitter: https://twitter.com/KieranIBI LinkedIn: http://www.linkedin.com/in/kieranpatrickwood Blog: http://kieranwood.wordpress.com/ eyJrIjoiYmJkNWU3MmQtMGQwNi00M2VjLThlYTAtMWQyZDdhYzI1ZjUxIiwidCI6ImY1NmM4YzIyLTQ5ZDMtNDQ5OC1iZThiLWU3MWE0YjVlZGRjNiIsImMiOjh9Data Days | Data Days Your Way
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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 topIs 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.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.Stop Hosting Report Images Publicly Just to Show Them in Power BI
The problem- showing an image meant making it public Say you're building a product catalog report, and you want each row in a table to show a small product photo next to its name and price. Power BI has always supported this through an image URL field you give it a web address, and it displays the image. Simple enough, until you think about where that image actually has to live. For Power BI to load an image from a URL, that URL generally needs to be publicly reachable on the internet. Which means product photos, employee headshots, or internal logos things that often shouldn't be sitting on a public website at all - ended up hosted somewhere publicly accessible anyway, purely so a report could display them. For a genuinely public product catalog, that might be fine. But for an internal HR dashboard showing employee photos, or an internal parts catalog with proprietary product images, publishing those images to a public URL just to satisfy a formatting requirement was never a comfortable trade-off. The alternative some people used embedding images as base64 text directly in the data - avoided the public hosting problem but came with its own cost =base64-encoded images are large blocks of text, and packing many of them into a semantic model bloats its size and slows things down, especially at any real scale. What changed In the August 2026 update, Power BI added support for using image files stored in OneLake directly as an image source - anywhere Power BI already accepts an image URL. This includes the image visual itself, table and matrix cells, the card visual, and button and list slicers. OneLake is Fabric's underlying storage layer think of it as your organization's own secure file storage that lives inside your Fabric tenant, not on the public internet. The important shift here is that your images now stay inside that same secure boundary as the rest of your data. Nothing has to be exposed publicly, and nothing has to be crammed into your model as bloated encoded text. A real example Picture that same product catalog report, but this time for an internal parts catalog your company doesn't want publicly visible - only your own staff should see these product images. 1. Upload your product photos to a Lakehouse in your Fabric workspace, the same way you might upload any other file to OneLake storage. 2. In your semantic model, add a column that holds the OneLake path to each product's image, matched to the right row (similar to how you'd normally store a web image URL). 3. In your table or matrix visual, set that column's data category to Image URL, the same step you'd already take for a normal web-hosted image. 4. Power BI now loads each product's photo directly from OneLake, displayed right in the table - with no image ever needing to be publicly hosted, and no bloated encoded text sitting in your model. The report looks and behaves exactly the way it would with any other image URL - the only thing that's changed is where the image actually lives, and who can reach it. Where this helps the most - Internal catalogs and HR-style reports, where showing a photo matter for usability, but the image itself shouldn't be public - Organizations with strict data governance requirements, where "the image is on a public URL" was never going to pass an internal security review - Reports with a lot of images, where base64 embedding was making the model uncomfortably large What to keep in mind This requires your images to actually be organized in a Lakehouse within your Fabric workspace first, so it's a bit more setup than just pasting a public URL - worth it specifically when keeping images private and secure actually matters for your use case. For a genuinely public-facing report where the images are meant to be seen by anyone anyway, a normal public image URL still works fine and doesn't need this extra step. If you've been quietly uncomfortable about hosting internal images publicly just so a report could display them, this is worth switching to the next time you build a report that needs pictures. Thanks for reading! Connect with me on: LinkedIn | Data With Pankaj - YouTubeGlobal Aircraft✈️ Live Tracking with Microsoft Fabric Real-Time Intelligence
In this blog, I’ll walk you through, build a real-time global flight tracking system. We will ingest live flight data from the public OpenSky Network API using a Python polling script, stream it through Microsoft Fabric Eventstream into an Eventhouse (KQL Database), transform the dense raw arrays using KQL update policies and visualize the results on a Real-Time Dashboard complete with maps, KPIs and analytical charts. Prerequisites: Valid Fabric Capacity / Trail License Knowledge on Python Knowledge on KQL Step1: Setup Workspace & Eventhouse (KQL Database) Created a workspace “FlightTracking-[WS]” Created an eventhouse “FLightTracking-EH” Create a raw ingestion table “RawFlightbatc” in KQL Databse Step 2: Setup a Fabric Eventstream Created a Eventstream “GlobalFlightStream” and select ‘Use custom endpoint’ Click ‘Add’ Click on ‘Publish’ Copy the ‘Event hub name’ and ‘connection string-primary key’ into notepad Step 3: Notebook Creation and Setup Python script Created a notebook. Make sure select Python Install azure eventhub package Let’s go back to eventstream and add destination by selecting ‘Eventhouse’ Configure all details and click on save Comeback to Notebook and insert the Python script which is having all the connection strings / passwords etc. Run the notebook Now notebook started running and sending the data to eventstream Data is loaded into eventstream and Click on Publish Now eventstream is ‘Live’ Data is loading into KQL Database Step 4: Regularizing Data with KQL & Update Policies Create a cleaned table “FlightStates” Create the parsing function and update policy Alter table with updated policy Step 5: Building Real-Time Dashboard Click on Realtime dashboard and give a name “FlightOperationsDashboard” Now click on edit Run the below code to get total active flights count Change the chart to Stat and rename, click on Apply KPI added and click on Add visual and take new Stat visual Insert the code and run to get India Origin Flights and Format it and click Apply In the same way, I built other KPIs Select Map Chart Run the below code and Fill all details and Click on Apply. Here we’re calculating Flights trend We can see chart added to Dashboard Select a Bar chart Run the below code, fill all details and click on apply to add Bar chart to Dashboard. Here we’re getting the top 10 countries by aircrafts Run the below code, fill all details and click on apply to add Column chart to Dashboard. Here we’re categorizing the baro-altitude which is critical for airport delay predection Run the below code, fill all details and click on apply to add Pie chart to Dashboard. It splits the aircraft parked versus those actively flying Together, these visuals transform raw flight telemetry into an operational monitoring experience. Key takeaways: Fabric Eventstream provides a streamlined way to ingest external real-time feeds. Eventhouse/KQL Database provides a real-time analytical environment for flight telemetry. KQL mv-expand simplifies the processing of nested flight-state arrays. Update Policies automate transformation from raw streaming data into structured analytical data. KQL geospatial functions enable location-based flight analysis. Real-Time Dashboards transform streaming telemetry into actionable operational insights. Conclusion: This project demonstrates how Microsoft Fabric Real-Time Intelligence can be used to build an end-to-end real-time aircraft tracking solution. we can transform continuously arriving aircraft telemetry into meaningful real-time insights. Do you want to replicate? Get Code file from my GitHub link You can find all the KQL queries, update policy functions, and the complete Python polling script in the official GitHub repository below: [Download from here] Acknowledgements I would like to express my sincere gratitude to @SuryaTejaJosyul , @minniwalia and @rajendraongole1 for their continuous guidance and support throughout this Real-Time Intelligence (RTI) implementation. Their insights and encouragement played a key role in helping me complete this solution successfull Happy learning! — Inturi Suparna Babu [LinkedIn]
