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Return to Data Days Homepage Data Days Your Way Find all sessions on demand!. The Power BI Dataviz World Champs is happening now | start your journey to the finals in Barcelona. Start with what fits you: Fabric | Power BI | SQL | AI | Beginner / New You are looking for: Certification Prep | Data Engineering Deep Dives | Data Visualization | Community Browse by Language: Spanish | Português | French | Japanese / 日本語 | Hindi Fabric User DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak 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 Get Certified: (DP-700) Fabric Data Engineer Essentials (APAC) Date: June 16, 3pm Host: Mike Fortman, Martin Catherall Get Certified: (DP-700) Fabric Data Engineer Essentials (US/EMEA) Date: June 16, 3pm Host: Aleksi Partanen, Phillip Burton Get Certified: (DP-600) Fabric Analytics Engineer Essentials (APAC) Date: June 17, 8am Host: Heidi Hasting, Martin Catherall Get Certified: (DP-600) Fabric Analytics Engineer Essentials (US/EMEA) Date: June 17, 3pm Host: Ásgeir Gunnarsson, Rajendra Ongole DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak Orchestrating Fabric Spark and Best Practices for Production-Ready Workload Date: June 18, 8am Host: Santhosh Kumar Ravindran; Ashit Gosalia Security and Governance in Fabric Date: June 20, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Fabric Data Pipelines Full Course For Beginners (Data Days Edition 2026) Date: June 21, 05:30 AM Host: Ansh Lamba 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 Prepare for the Microsoft Data Days Date: June 22, 5 am Host: Aman Jindal DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 22, 6:30 PM Host: Amit Chandak Location Intelligence with Maps and GeoAnalytics in Microsoft Fabric Date: June 23, 07:30 AM Host: Philippa Burgess 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 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 Global Fabric Day 2026 Date: June 27 Host: Kim Manis Global Fabric Day 2026: Security, Location & Intelligence in Microsoft Fabric Date: June 27, 09:30 AM Host: Philippa Burgess Transforming and Modeling Data in Fabric Date: June 27, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Modeling Real LMS Data in Power BI: Star Schema from a Messy MySQL Source Date: June 28, 06:30 AM Host: Parul Rani Sagar Configuring Workspace Settings in Fabric Date: June 28, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Learn KQL in 10 minutes Date: June 29 Host: Phillip Burton Get to Know Esri: From Living Atlas to Spatial Analysis for Fabric Users Date: June 30, 07:30 AM Host: Philippa Burgess 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) Data and AI Security and Governance in Microsoft Fabric Date: July 7, 07:30 AM Host: Philippa Burgess Inside Fabric Runtime 2.0: Spark 4 and Delta 4 in Action Date: July 15, 4:00 PM Host: Arshad Ali and Miles Cole Fabric IQ for Data Professionals Date: July 21, 07:30 AM Host: Philippa Burgess Building Scalable Bronze Layer in Microsoft Fabric Date: July 23, 09:00 AM Host: Aleksi Partanen, Teemu Multanen Win in the Seams 🧵 Stitching Together Data and Security with Microsoft Fabric, KQL, and Data Logs Date: July 25, 09:30 AM Host: Philippa Burgess KQL for Data and Security Professionals Date: July 28, 07:30 AM Host: Philippa Burgess Fabric Analytics Engineer Certification Training (DP-600) Discover resources to prepare for this exam. Fabric Data Engineer Certification Training (DP-700) Discover resources to prepare for this exam. Get started with Microsoft Fabric Self-paced training DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak 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 4: Dataflow Gen2 End-to-End Date: June 17, 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 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 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 Power BI 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 Get Certified: (PL-300) Power BI Data Analyst (US/EMEA) Date: June 22, 8am Host: Ilgar Zarbaliyev, Doher Drizzle Pablo Get Certified: (PL-300) Power BI Data Analyst (APAC) Date: June 23, 3pm Host: Anupama Natarajan, Chris Hyde DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 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 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 Exploring Data with AI and Natural Language Features Date: July 13, 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 Power BI Essentials — Data Days with Data Analytic Group Date: July 18, 09:00 PM Host: Rajendra Ongole,Lanka, Shashi Performing Analytics in Power BI using DAX Date: July 20, 6:00 AM Host: Ilgar Zarbaliyev Get started with Microsoft data analytics Self-paced training Power BI Data Analyst Certification Training (PL-300) Discover resources to prepare for this exam. Power BI Dataviz World Championships Do you have what it takes? DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 6:30 PM Host: Amit Chandak Back to top SQL DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak Microsoft Cloud & AI Frontier Week: Modernize SQL for AI-Ready Databases Date: June 24, 03:00 AM Host: Diaa Radwan Microsoft Cloud & AI Frontier Week: Power Intelligent Apps and Agents with Azure Databases Date: June 24, 04:00 AM Host: Diaa Radwan Build with SQL + AI: From Prompt to Intelligent Apps Date: June 25, 01:00 PM Host: Matt Gordon, Alpa Buddhabhatti Modeling Real LMS Data in Power BI: Star Schema from a Messy MySQL Source Date: June 28, 06:30 AM Host: Parul Rani Sagar Starting with Data API Builder in 10 minutes Date: June 30, TBD Host: Phillip Burton Designing and Implementing Database Objects in Azure SQL Database Date: July 11, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Advanced Query Techniques in Azure SQL Date: July 12, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Implementing Programmability Objects in Azure SQL Database Date: July 18, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Securing Data Access in Azure SQL Database Date: July 19, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Optimizing Performance and integrity in Azure SQL Database Date: July 25, 9:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Win in the Seams 🧵 Stitching Together Data and Security with Microsoft Fabric, KQL, and Data Logs Date: July 25, 09:30 AM Host: Philippa Burgess Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan Query and modify data with Transact-SQL Self-paced training SQL AI Engineer Certification Training (DP-800) Discover resources to prepare for this exam. DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan Back to top Using AI Microsoft Cloud & AI Frontier Week: Transform Data Silos into AI Fuel – Build an End-to-End Data Foundation Date: June 22, 04:00 AM Host: Mark Torr, Chris Webb, Sarina Stevens Microsoft Cloud & AI Frontier Week: Unify Your Data with OneLake for Analytics, AI and Agents Date: June 24, 01:00 AM Host: Sevgi Guzzella Microsoft Cloud & AI Frontier Week: Turn Data into Intelligent Action with Microsoft Fabric Date: June 24, 02:00 AM Host: Simon Lidberg Microsoft Cloud & AI Frontier Week: Modernize SQL for AI-Ready Databases Date: June 24, 03:00 AM Host: Diaa Radwan Microsoft Cloud & AI Frontier Week: Power Intelligent Apps and Agents with Azure Databases Date: June 24, 04:00 AM Host: Diaa Radwan Microsoft Cloud & AI Frontier Week: Transform Fragmented Data into Trusted AI at Scale – A Roadmap for CDOs Date: June 24, 05:00 AM Host: Seda Teber & Marcel Franke The Future of AI in Microsoft Fabric: Data Agents and Beyond Date: June 24, 08:00 AM Host: Brian Bønk, Philippa Burgess DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak Build with SQL + AI: From Prompt to Intelligent Apps Date: June 25, 01:00 PM Host: Matt Gordon, Alpa Buddhabhatti Data and AI Security and Governance in Microsoft Fabric Date: July 7, 07:30 AM Host: Philippa Burgess Exploring Data with AI and Natural Language Features Date: July 13, 6:00 AM Host: Ilgar Zarbaliyev Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan Back to top New Fabric / Power BI / SQL Users DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak Fabric Data Pipelines Full Course For Beginners (Data Days Edition 2026) Date: June 21, 05:30 AM Host: Ansh Lamba Get Certified: Which Data Exam Fits You Best? Date: June 23, 12pm Host: Dean Jurecic, Taylor Amy Learn KQL in 10 minutes Date: June 29 Host: Phillip Burton Starting with Data API Builder in 10 minutes Date: June 30, TBD Host: Phillip Burton From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Get started with Microsoft data analytics Self-paced training Get started with Microsoft Fabric Self-paced training Introduction to Microsoft Azure Data core data concepts Self-paced training Query and modify data with Transact-SQL Self-paced training DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Back to top Certification Prep Certification Resources Which Data Exam Fits You Best? Date: June 23, 12pm Host: Dean Jurecic, Taylor Amy What to Expect and How to Pass Date: June 25, 8am Host: Heini Ilmarinen, Teemu Multanen Get Certified: (Exam Day) What to Expect and How to Pass (US/EMEA) Date: August 6, 8:00 AM Host: Brian Bønk; Charley Hanania Find a Study Group DP-600, DP-700, DP-800, and PL-300 Free Certification Exam Voucher DP-600, DP-700 or DP-800 Get Certified: (Exam Day) What to Expect and How to Pass (US/EMEA) Date: August 6, 8:00 AM Host: Brian Bønk; Charley Hanania DP-600 - Fabric Analytics Engineer Get Certified: (DP-600) Fabric Analytics Engineer Essentials (APAC) Date: June 17, 3pm Host: Heidi Hasting, Martin Catherall Certifícate: (DP-600) Fabric Analytics Engineer Conceptos Clave Date: June 17, 4pm Host: Renzo Roca, Javier Villegas Get Certified: (DP-600) Fabric Analytics Engineer Essentials (US/EMEA) Date: June 18, 8am Host: Ásgeir Gunnarsson, Rajendra Ongole Certifique-se: (DP-600) Fundamentos de Analytics no Fabric Date: June 18, 12pm Host: Ladislau Andre, Roberto Fonseca DP-600 to Real Project: What the Certification Taught Me (and What It Didn't) Date: July 11, 09:30 PM Host: Parul Rani Sagar Microsoft Certified: Fabric Analytics Engineer Associate(DP 600) Date: July 17, 08:30 PM Host: Inturi Suparna Babu, Ajay Babu Inturi, Upputuri Gopikrishna DP-600 Exam Prep — Fabric Analytics Engineer with Data Analytic Group Date: July 25, 09:00 PM Host: Rajendra Ongole,Lanka, Shashi Prepare for Exam DP-600 Prep resources DP-600 In Depth On-demand recorded sessions Free Certification Exam Voucher DP-600, DP-700 or DP-800 Find a Study Group DP-600, DP-700, DP-800, and PL-300 DP-700 - Fabric Data Engineer DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak Certifique-se: (DP-700) Fundamentos de Dados no Fabric Date: June 15, 12pm Host: Luiz Santana, Percy Machado Certifícate: (DP-700) Fabric Data Engineer Conceptos Clave Date: June 15, 4pm Host: Gonzalo bissio, Keyla Dolores Mendez DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak Get Certified: (DP-700) Fabric Data Engineer Essentials (APAC) Date: June 16, 3:00 PM Host: Mike Fortman, Martin Catherall DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak Get Certified: (DP-700) Fabric Data Engineer Essentials (US/EMEA) Date: June 17, 8:00 AM Host: Aleksi Partanen, Phillip Burton DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 19, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 22, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 22, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 23, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 24, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak Como passar na Certificação DP-700: Guia Definitivo! Date: June 25, 3:00 PM Host: Sidney Cirqueira DP 700- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: June 25, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 25, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: June 26, 6:30 PM Host: Amit Chandak Getting Started with PySpark for DP-700 Date: July 16, 9:00 AM Host: Teemu Multanen DP-700 In Depth On-demand recorded sessions Find a Study Group DP-600, DP-700, DP-800, and PL-300 Free Certification Exam Voucher DP-600, DP-700 or DP-800 Prepare for Exam DP-700 Prep resources DP 700- Microsoft Fabric Training | Episode 1: Fabric Overview, Domains, Workspaces & OneLake Date: June 14, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 2: Lakehouse, Warehouse & T-SQL Date: June 15, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 7: Real-Time Analytics, Eventstream, Eventhouse & KQL Date: June 22, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 8: Direct Lake Semantic Models & Power BI Performance Date: June 23, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 9: Mirroring, Databases, Composite Models & Data Agents Date: June 24, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 10: End-to-End Fabric Project & DP-700 Exam Preparation Date: June 25, 6:30 PM Host: Amit Chandak DP-800 - SQL AI Engineer Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan DP-800 In Depth On-demand recorded sessions Find a Study Group DP-600, DP-700, DP-800, and PL-300 Free Certification Exam Voucher DP-600, DP-700 or DP-800 Prepare for Exam DP-800 Prep resources Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (EMEA / US) Date: July 15, 8:00 AM Host: Javier Villegas; Hamish Watson Get Certified SQL+AI (DP-800): Design and Develop SQL Solutions Like a Pro (APAC) Date: July 16, 3:00 PM Host: Martin Catherall; Greg Low Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (APAC) Date: July 20, 4:00 PM Host: Mike Fortman; Mayte Castillo Get Certified DP-800: Secure, Optimize, & Ship SQL+AI Solutions (EMEA/US) Date: July 21, 8:00 AM Host: Jeff Taylor; Matt Gordon Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (EMEA / US) Date: July 23, 8:00 AM Host: Gaston Cruz; Armando Lacerda Get Certified SQL+AI (DP-800): Bring AI to SQL with Embeddings, Search, and RAG (APAC) Date: July 29, 3:00 PM Host: Greg Low; Anupama Natarajan PL-300 - Power BI Data Analyst Using Slicers and What-If Parameters in Power BI Date: June 15, 6:00 AM Host: Ilgar Zarbaliyev Dynamic Currency Conversion in Power BI Date: June 22, 6:00 AM Host: Ilgar Zarbaliyev Get Certified: (PL-300) Power BI Data Analyst (US/EMEA) Date: June 22, 8am Host: Ilgar Zarbaliyev, Doher Drizzle Pablo Certifique-se: (PL-300) Fundamentos de Análise de Dados com Power BI Date: June 22, 12pm Host: Brendell Silva Gomes, Miguel Felix Get Certified: (PL-300) Power BI Data Analyst (APAC) Date: June 23, 3pm Host: Anupama Natarajan, Chris Hyde Certifícate: (PL-300) Power BI Data Analyst Conceptos Clave Date: June 23, 4pm Host: Adrian Fernandez Zenteno, Ricardo Rincón Implementing Row-Level Security (RLS) Date: June 29, 6:00 AM Host: Ilgar Zarbaliyev Building Interactive Dashboards and Data Alerts Date: July 6, 6:00 AM Host: Ilgar Zarbaliyev Microsoft Certified: Power BI Data Analyst Associate Date: July 18, 5:30 AM Host: Inturi Suparna Babu, Ajay Babu Inturi, Upputuri Gopikrishna Performing Analytics in Power BI using DAX Date: July 20, 6:00 AM Host: Ilgar Zarbaliyev Prepare for Exam PL-300 Prep resources PL-300 In Depth On-demand recorded sessions Free Certification Exam Voucher DP-600, DP-700 or DP-800 Find a Study Group DP-600, DP-700, DP-800, and PL-300 Back to top Data Engineering Deep Dives Managing secure access, trusted discovery & data sharing with OneLake (3 sessions) Date: June 16, 9:00 AM Host: Josh Ndemenge Tenant management with Sempy Date: June 16, 11:00 AM Host: Taylor Amy, Teemu Multanen DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak Orchestrating Fabric Spark and Best Practices for Production-Ready Workload Date: June 19, 8am Host: Santhosh Kumar Ravindran; Ashit Gosalia Data Ingestion and Discovery in Fabric Date: June 21, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak Modeling Real LMS Data in Power BI: Star Schema from a Messy MySQL Source Date: June 28, 06:30 AM Host: Parul Rani Sagar Orchestrating Pipelines, and Notebooks in Fabric Date: July 4, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Designing Data Load Strategies in Fabric Date: July 5, 09:30 AM Host: Amit Kumar Mahato (Cloud Guru Amit) Inside Fabric Runtime 2.0: Spark 4 and Delta 4 in Action Date: July 15, 4:00 PM Host: Arshad Ali and Miles Cole Building Scalable Bronze Layer in Microsoft Fabric Date: July 23, 09:00 AM Host: Aleksi Partanen, Teemu Multanen Managing secure access, trusted discovery & data sharing with OneLake (3 sessions) Date: June 16, 9:00 AM Host: Josh Ndemenge DP 700- Microsoft Fabric Training | Episode 3: Lakehouse with Spark SQL Date: June 16, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 4: Dataflow Gen2 End-to-End Date: June 17, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 5: PySpark Notebooks for Data Engineering Date: June 18, 6:30 PM Host: Amit Chandak DP 700- Microsoft Fabric Training | Episode 6: Data Pipelines, Scheduling & OneLake Shortcuts Date: June 21, 6:30 PM Host: Amit Chandak Inside Fabric Runtime 2.0: Spark 4 and Delta 4 in Action Date: July 15, 4:00 PM Host: Arshad Ali and Miles Cole Back to top Dataviz Using Slicers and What-If Parameters in Power BI Date: June 15, 6:00 AM Host: Ilgar Zarbaliyev Power BI Dataviz World Championships: Start Your Journey to Barcelona Date: June 16, 8am Host: Valerie Junk, Lakshmi Ponnurasan Power BI Dataviz World Championships: Comece sua jornada para Barcelona Date: June 16, 2pm Host: Samyr Moises, Dirceu Moraes Resende Power BI Dataviz World Championships: Comienza tu camino a Barcelona Date: June 16, 4pm Host: Walter Calcagno, Lucrecia Krause Dynamic Currency Conversion in Power BI Date: June 22, 6:00 AM Host: Ilgar Zarbaliyev Building Interactive Dashboards and Data Alerts Date: July 6, 6:00 AM Host: Ilgar Zarbaliyev Inside the Mind of a Dataviz World Champion Date: July 14, 8:00 AM Host: Valerie Junk; Santhana Lakshmi Ponnurasan Performing Analytics in Power BI using DAX Date: July 20, 6:00 AM Host: Ilgar Zarbaliyev From “I’m Just Getting Started” to “I Made This” Date: July 28, 8:00 AM Host: Philippa Burgess; Taylor Amy Power BI Dataviz World Championships Do you have what it takes? 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A machine learning model can be remarkably accurate and still leave people completely unconvinced. That is one of the strange realities of working in data science. You can spend weeks cleaning data, testing algorithms, tuning parameters, and validating performance, only to reach the moment when somebody looks at a prediction and asks a very reasonable question: “Why?” Suddenly, accuracy alone does not feel like enough. A probability score might tell you what the model believes, but it does not necessarily help the person receiving that prediction understand how the model arrived there. That gap between prediction and understanding is exactly where model explainability becomes important. What you will learn: In this edition, we’re exploring model explainability in production and how SHAP, FastAPI, and Plotly can work together to make predictions easier to understand. By the time you’re done with this, you’ll have a clear view of how SHAP can explain individual model decisions, how those explanations can be served dynamically through an API, and how interactive visualizations can turn technical attribution values into something far more approachable. Source: Sahir Maharaj (https://sahirmaharaj.com) It is tempting to think of explainability as a chart you generate after training a model, but production explainability is really an ongoing capability. Every new observation can produce a different prediction, and every prediction may have a different explanation. A customer predicted to leave a service might receive a high risk score because of declining engagement, while another customer receives the same score because of repeated service issues. The prediction may look identical at the surface, but the reasoning behind it can be completely different. That difference matters when somebody needs to decide what to do next. This is where SHAP becomes particularly valuable because it gives you a structured way to describe feature contributions. Imagine a model predicting whether a machine is likely to fail. The prediction itself might tell you that failure risk is 82 percent. Useful, certainly, but incomplete. SHAP can help reveal that rising operating temperature, vibration intensity, and unusually long operating hours are pushing the prediction upward, while another factor such as recent maintenance is reducing the predicted risk. Suddenly the prediction becomes much easier to reason about. You still have a probabilistic model, but you also have something resembling an explanation of its behavior. As a data scientist, I find that this distinction between global and local understanding is especially important. During model development, I often want to understand the model globally. Which variables matter most overall? Is the model relying heavily on variables I expected it to use? Are there surprising relationships hiding in the data? In production, however, people often care about something much more specific. They want to know why this customer, this transaction, this machine, or this observation received the prediction it did. That is a local explanation, and SHAP is particularly useful for helping bridge that gap. Source: Sahir Maharaj (https://sahirmaharaj.com) You can think about the difference through a credit-risk example. Globally, a model might rely heavily on payment history, debt ratios, and income stability. That tells you something useful about the overall model. But suppose one applicant receives a higher risk prediction than expected. A general feature importance chart does not really answer the person's question. You need to understand what happened for that particular prediction. Perhaps the debt ratio increased the risk score significantly, while long-term employment reduced it. That level of explanation provides much more context than simply showing which variables matter across thousands of predictions. Explainability can also become an important debugging tool for you as the person building the model. Suppose a model performs well according to the metrics you are monitoring, but its explanations repeatedly show that one unexpected variable dominates many predictions. That should make you curious. import numpy as np import pandas as pd import shap import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split np.random.seed(42) n = 800 X = pd.DataFrame({ "usage_drop": np.random.normal(20, 10, n), "support_calls": np.random.poisson(3, n), "login_days": np.random.randint(1, 30, n), "tenure_months": np.random.randint(1, 72, n), "monthly_cost": np.random.normal(80, 20, n) }) score = ( 0.08 * X["usage_drop"] + 0.45 * X["support_calls"] - 0.05 * X["login_days"] - 0.02 * X["tenure_months"] + 0.015 * X["monthly_cost"] ) y = (score + np.random.normal(0, 1, n) > np.median(score)).astype(int) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=42 ) model = RandomForestClassifier(n_estimators=150, random_state=42) model.fit(X_train, y_train) explainer = shap.TreeExplainer(model) values = explainer(X_test) class_values = values[:, :, 1] if values.values.ndim == 3 else values shap.plots.beeswarm(class_values, max_display=5) plt.show() shap.plots.waterfall(class_values[0], max_display=5) plt.show() Imagine that somebody submits new information to a prediction service. The model receives those features, processes them, and generates a result. If explainability is part of the design, the same interaction can also produce SHAP information describing the important factors behind that specific prediction. Instead of storing explanations for every imaginable case beforehand, you generate them when they are needed. FastAPI fits naturally into this kind of workflow because it gives the prediction and explanation process a consistent interface. Rather than another application needing to understand your model directly, it can send the required information to an API. The API can coordinate the prediction process, calculate the explanation, and return the relevant output. The application consuming the result does not need to know the mathematical details of SHAP or how the model itself works internally. It simply receives information in an agreed structure. That separation can make a production system much easier to manage. From what I observe when building data science workflows, this separation becomes valuable surprisingly quickly. During early experimentation, everything often exists together. Data preparation, prediction, explanation, and visualization may all happen inside one environment. That is perfectly reasonable when you are exploring an idea. Production introduces more responsibilities. The application requesting a prediction should not need access to the entire modeling environment, and your model should not depend on somebody manually generating explanations. Creating a clear service boundary helps each part of the system do its job without becoming unnecessarily tangled with everything else. Dynamic explanations also give you flexibility over how much information you return. A technical monitoring application may want detailed SHAP values for every available feature. A decision-support interface may only need the five strongest contributors. Another application may need a short summary explaining the strongest positive and negative influences. The explanation engine can be the same while the presentation changes according to the audience. That matters because explainability is not only a modeling problem. It is also a communication problem. Source: Sahir Maharaj (https://sahirmaharaj.com) Performance deserves careful attention here as well. Calculating an explanation adds work beyond producing the prediction itself, and depending on the model, the amount of additional work may not be trivial. That does not mean explanations should be avoided. It means you should think about when they are required. Some systems may need an explanation for every single prediction. Others may generate explanations only when a user requests additional detail. You might also decide that certain high-impact decisions always receive explanations while low-impact automated predictions do not. Production design is often about finding that balance between completeness and responsiveness. There is also an important consistency issue. An explanation is only useful if it corresponds to the exact model and transformation process that generated the prediction. Imagine updating your model but accidentally continuing to generate explanations using an older version. The results could look perfectly reasonable while describing behavior that no longer matches the prediction being shown. import numpy as np import pandas as pd import shap import plotly.graph_objects as go from sklearn.ensemble import GradientBoostingClassifier np.random.seed(7) n = 700 X = pd.DataFrame({ "temperature": np.random.normal(70, 12, n), "vibration": np.random.normal(4, 1.5, n), "operating_hours": np.random.normal(900, 250, n), "maintenance_days": np.random.randint(1, 120, n) }) risk = ( 0.06 * X["temperature"] + 0.7 * X["vibration"] + 0.003 * X["operating_hours"] - 0.025 * X["maintenance_days"] ) y = (risk + np.random.normal(0, 1, n) > np.median(risk)).astype(int) model = GradientBoostingClassifier(random_state=7) model.fit(X, y) explainer = shap.Explainer(model, X) row = pd.DataFrame([{ "temperature": 92, "vibration": 6.8, "operating_hours": 1250, "maintenance_days": 75 }]) prediction = model.predict_proba(row)[0, 1] explanation = explainer(row) names = row.columns.tolist() impacts = explanation.values[0] waterfall = go.Figure(go.Waterfall( orientation="h", y=names, x=impacts, measure=["relative"] * len(names), text=[f"{x:+.2f}" for x in impacts], textposition="outside" )) waterfall.update_layout( title="SHAP Explanation: What Drove This Prediction?", xaxis_title="SHAP Impact on Model Output", yaxis_title="Feature", height=450 ) waterfall.show() gauge = go.Figure(go.Indicator( mode="gauge+number+delta", value=prediction * 100, number={"suffix": "%"}, delta={"reference": 50}, title={"text": "Predicted Machine Failure Risk"}, gauge={ "axis": {"range": [0, 100]}, "steps": [ {"range": [0, 40]}, {"range": [40, 70]}, {"range": [70, 100]} ], "threshold": { "line": {"width": 4}, "thickness": 0.8, "value": prediction * 100 } } )) gauge.update_layout(height=400) gauge.show() pd.DataFrame({ "Feature": names, "Value": row.iloc[0].values, "SHAP Impact": impacts }).sort_values("SHAP Impact", key=abs, ascending=False) The third objective is turning those explanation values into something people can interpret without needing to understand every detail of SHAP. This is where visualization becomes more than decoration. Raw values can tell you exactly how features influenced a prediction, but they require effort to interpret. A Plotly visualization can organise those contributions so the strongest influences immediately stand out. Features pushing a prediction in one direction can be separated visually from those pushing it in the opposite direction, allowing somebody to understand the basic story of the prediction within a few moments. Imagine a customer churn model that predicts an unusually high probability of cancellation. A dynamic explanation might show that a recent drop in product usage is the strongest contributor, followed by repeated support contacts and a reduction in login frequency. At the same time, a long customer relationship might slightly reduce the churn prediction. That is much more meaningful than presenting a table containing feature names and decimal values. The visualization helps the reader understand not only which features mattered but also how they worked together. Source: Sahir Maharaj (https://sahirmaharaj.com) When I look at explanations intended for other people, I try not to assume that more information automatically creates more transparency. It is very easy to generate a detailed visualization containing dozens of variables simply because the values are available. The result may technically be comprehensive while being practically overwhelming. If somebody has to study a chart for several minutes before understanding the main message, the explanation probably needs refinement. In many situations, highlighting the strongest contributors and allowing additional detail to be explored when needed creates a much better experience. Interactivity can make this even more useful. Plotly allows explanations to become something people can investigate instead of merely observe. A user might hover over a feature to inspect the contribution more closely or compare several predictions to understand why similar observations received different results. Imagine comparing two loan applications that received different risk scores. An interactive explanation can make it easier to see that one prediction was strongly influenced by debt levels while another was affected primarily by inconsistent payment history. That comparison helps reveal how the model responds to different combinations of information. At the same time, visual polish should never become a substitute for clarity. A sophisticated interactive chart can create an impression of authority, and that means you have a responsibility to provide enough context around what the visualization represents. The user should understand that the chart explains the model's behavior, not necessarily the underlying truth of the real-world situation. If a feature makes a large contribution, that means the model relied heavily on that feature for the prediction. It does not automatically prove that the feature caused the outcome. Keeping that distinction clear is a major part of responsible model communication. Source: Sahir Maharaj (https://sahirmaharaj.com) So give it a try. Do not wait until you have a huge production system or a complicated machine learning platform before thinking about transparency. Start with one prediction, one explanation, and one clear visualization. Build from there as your understanding grows. The more comfortable you become explaining what your model is doing, the easier it becomes to recognise when its behaviour deserves a closer look. And that ability is valuable whether you are just entering data science or have been building models for years. If you ever need anything while exploring it, just reach out to me. I’ll be more than happy to help. Thanks for taking the time to read my post! I’d love to hear what you think and connect with you! 🙂 LinkedIn Kaggle Topmate (Free Power BI / Data Science Sessions and Resources) Website The Tech Journal (Blog) About the author Sahir Maharaj is a Lead Data Scientist who leads the design and deployment of end-to-end AI solutions that drive strategic decisions at scale. As a Microsoft MVP, he has been featured internationally, including in The Indian Express and on New York's Times Square billboards, and is a prolific content creator on LinkedIn.42Views0likes0CommentsMastering Advanced Regression for Data Science in Microsoft Fabric
In this edition, we’re exploring two regression techniques that every data professional eventually bumps into when the simple models stop telling the full story. You’ll get a clear sense of what quantile regression actually solves, especially when your data behaves in unpredictable or uneven ways. By the time you’re done, you’ll feel more confident choosing the regression approach that truly fits the question you’re trying to answer, instead of defaulting to whatever is familiar.364Views2likes5CommentsMicrosoft Fabric, RAG, and the Conversations That Follow You Home
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🎨 Community Sticker Challenge Barcelona 2026 Winners Congratulations to the winners of the Community Sticker Challenge Barcelona 2026! We are excited to announce the winning designs across four challenge categories. Thank you to everyone who shared their creativity, voted for their favorite designs, and helped make this challenge a success. ••• 🏆 Winning Designs 😂 Inside Joke Username: jennratten Title: Famous Last Words Before Production View Sticker 🏅 Certified & Proud Username: kf2k3 Title: Building My Future View Sticker 🤖 AI-Powered Username: jayasurya_prud Title: Better Together: Human + AI View Sticker 🌍 Community Enthusiasm: Barcelona Edition Username: Juless Title: Data Is Art: Barcelona 2026 View Sticker 🎨 Winning Sticker Designs Take a look at the four winning stickers selected by the Fabric Community. 💙 Thank You, Fabric Community Thank you to everyone who participated in the challenge and voted for their favorite stickers. We were impressed by the creativity, humor, talent, and community spirit shown throughout the competition. We look forward to celebrating the winning designs at FabCon & SQLCon in Barcelona and sharing them with the community. Congratulations to Our Winners! The Fabric Community Sticker Challenge Barcelona 2026 was a huge success because of your creativity and participation.2.9KViews19likes6Comments