Forecasting with Autoregression, ARIMA & AIC / BIC for Data Science in Microsoft Fabric
In this edition, we’re exploring forecasting through Autoregression, ARIMA, and the model selection tools AIC and BIC. By the time you’re done reading, you’ll understand how data can actually learn from its own patterns, how ARIMA helps bring structure to unpredictable trends, and how AIC and BIC keep your models grounded by balancing accuracy with simplicity.Power BI Smart Table Visual: Excel style Column Filtering, Dynamic Column Headers, Grouping Columns
For a smoother reading experience with clearer screenshots and easy code copying, I suggest [reading this article on my website](https://www.techietips.co.in/articles/powerbi-managed-6500measures-smartly). It’s free, ad-free, distraction-free, and dedicated solely to sharing knowledge. The Table is the most commonly used visual in Power BI. Anyone who has spent years in Excel expects to click a header and filter that column. They expect related columns to sit under a common heading. The native table visual does none of this. There are grid visuals in the Microsoft visual app source that do. Most developers never get to use them, for two reasons that have nothing to do with the features. The good ones are licensed per developers or user, and that cost has to be justified to someone. The free ones are usually not Microsoft certified, which means an uncertified third party script is running inside your report, and that is a fair thing for a security team to say no to. So I built one. It is called Smart Table, and I built it with Claude. This post walks through what it does. 1. Excel style column filtering Every column header carries a funnel button in its own divided cell. Click it and you get what you would expect from Excel: sort, a set of conditions appropriate to the column’s type, and a searchable checkbox list of that column’s values. The menu names the column it belongs to, counts what you have ticked, and stays open while you sort. The conditions follow the column type rather than offering one generic list: Sync: does the filter stay in the grid or reach the report Sync is on by default. Filters applied in the header are pushed to the model, so every other visual on the page responds, exactly as if the user had used a slicer. Turn Sync off: Filtering then narrows this grid only. Global search The toolbar has a search box with its own operator dropdown: contains, is exactly, starts with, ends with, does not contain. It searches across every column at once. Power BI: Smart Table Excel Style Column Filtering 2. Dynamic column headers, driven by a measure A column header name is normally a static label. Here it can be a DAX measure. Arrival Month is set to April, and the Key Metrics headers read Lead Time (Avg: 30.00), Room Nights (Avg: 1.00), ADR (Avg: 160.50). Change the month and they recompute. The header honors the filter context, including the filters applied inside the grid itself. Filter Property down to Resort Hotel with the funnel and the averages in the header follow. Power BI Smart Table: Measure Driven Column Headers (Dynamic Column names) - YouTube 3. Column groups, without any extra tables Four groups across eleven columns: Reservation, Channel, Room Type, Key Metrics. Each one is a spanning header above its columns with its own colour. The thing to notice is what is not involved. No disconnected table. No field parameters. No two table visuals stacked on top of each other. No shapes placed in the background: which means your column groups move to the right along with your cursor. Power BI Smart Table: Grouping columns in a table without using a static table and a matrix visual - YouTube Known limits All of these come down to one idea: some filtering reaches the model and some is local to the grid. 30,000 rows That is the Power BI cap for a table mapping with this data reduction algorithm. Header filters push a real model filter, so filtering down to a workable set works fine. Do not point it at an unfiltered fact table. Value lists cap at 2,000 distinct values per column**, built from the loaded rows. On a high cardinality column like a guest name, use the search box in the menu. Ends with, Does not end with, and blank selections filter the grid only.** Power BI’s advanced filter operators have `StartsWith` and `Contains` but no `EndsWith`, and a blank has no equivalent in a model side `In` list. The menu tells you when you pick one of these. Bookmarks restore the data but not the ticked checkboxes.** The model filter is persisted by Power BI, so the rows come back correctly. The header checkboxes just will not show as ticked. I wrote this to make one point: the gaps in the built-in visuals are not permanent. Build your own, and with vibe coding that is no longer a months-long project. I am not sharing the .pbiviz file. It is an experiment, not tested for production grade reports. Feel free to checkout my [website](https://www.techietips.co.in) and reach out to me if you are interested to do these experiments along with me, I would like to continue developing this visual and will write detailed blogs in features explaining its features and implementations. I hope you learned something new. Feel free to share your thoughts in the comments section. Happy Learning!!!Data Days | Data Days Your Way
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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. 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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 top
My button & slicer disappeared in Power BI Service. Here's why.
Imagine you have created a beautiful table in Power BI with a slicer and a button placed on top of it, overlapping the table area. The slicer is used to search for specific content. The button becomes clickable when exactly one row is selected and opens a detail page for that row. In Power BI Desktop, everything works perfectly. However, as soon as you publish your report to the Power BI service and start interacting with it, strange things may happen. If you select one row, both the slicer and the button disappear. If you leave the table area, both reappear. However, as soon as you hover over them, they disappear again. Even more strangely, if you move in and out of the table a few times, it may happen that the button is available again. As it turns out, this is documented behaviour, not a bug. In reading view, Power BI brings the selected visual to the front layer, above anything that overlaps it. Selecting a table row selects the table, so the button and the slicer, which actually sit in front of the table, end up behind it and disappear. What I can't explain is the button becoming available again after moving in and out a few times. I haven't found anything documented on that. The good news: this default behaviour can be changed. To change it, there is an option. It's called 'Maintain layer order' and can be found in a visuals format pane under Properties > Advanced options. Switch it on for the visual that jumps to the front, in my case the table. Have you run into this one? And on which visual did you end up switching it on?Why Refresh the Whole Model When Only One Table Changed? Power BI Finally Lets You Choose
The problem: refreshing everything when you only needed a little Picture a semantic model with a few tables: a large Sales fact table that updates every day, a Products table that changes maybe once a month, and a Currency Exchange Rate table that barely changes at all. For as long as most of us have used Power BI, hitting "Refresh" meant refreshing the entire model - every table, every time, whether it actually needed it or not. Worse, a normal refresh always did two things at once: it checked whether anything about your data source's structure had changed (a schema sync), and then it reloaded all the actual data. Most of the time you only care about one of those. You just want fresh numbers in your Sales table - you don't need Power BI to re-check whether your Currency table's column structure changed, because it never does. But there was no way to say "just get me new data, skip the schema check," or "refresh only this one table." You got the whole thing, every time. This wasn't just an annoyance - for larger models, refreshing everything when you only needed a fraction of it meant longer waits, more load on your source systems, and refresh operations eating into your daily refresh limits for no good reason. The old way to get around this If you needed genuinely granular control before this update, your only real option was going through the XMLA endpoint or the REST API - writing code to say "refresh just this table" or "refresh just this partition." This worked, but it required Premium or Fabric capacity, technical setup, and honestly, a level of comfort with scripting that most report builders don't have or need for day-to-day work. For anyone just using the standard Power BI Service interface, there was no such option - it was full refresh or nothing. What changed In the August 2026 update, Power BI brought this kind of control directly into the Service UI, no scripting required. You now get two separate improvements working together: First, refresh type options. Instead of one generic "Refresh" button that always does a schema sync plus a data refresh, you can now choose exactly what you want: - Refresh schema and data - the original all-in-one behavior, still available when you genuinely need both - Sync schema only - checks and applies any structural changes from your data source, like new columns, without reloading all the data - Refresh data only - reloads fresh data without checking for schema changes at all Second, table-level refresh. Instead of applying that choice to your entire model, you can now apply it to just one specific table. A real example Back to our Sales, Products, and Currency Exchange Rate model. Say it's the middle of a regular business day, and you just want today's sales numbers updated - nothing else has changed. Open the semantic model in Model view within the Power BI Service. Instead of hitting the generic top-level Refresh button, find the Sales table specifically. Choose Refresh data only for just that table. Power BI reloads fresh data into Sales, and leaves Products and Currency Exchange Rate completely untouched - no unnecessary schema check, no reloading tables that didn't need it. What used to be a full-model operation is now a targeted, few-second action. Where this saves the most time - Large models with tables that update on very different schedules - a fast-changing fact table alongside slow-changing dimension tables that rarely need touching - Direct Lake models specifically, where a Lakehouse table might have gained new columns, you're not ready to bring into your model yet - you can refresh the data without pulling in that schema change - Anyone managing refresh limits or capacity usage, where cutting unnecessary schema syncs and full-model reloads adds up to real savings over dozens of refreshes a day What to keep in mind This is a Power BI Service feature, so it's something you'll do from the web interface managing a published semantic model, not from Power BI Desktop while you're building. It's also worth remembering that "Sync schema only" and "Refresh data only" are meant to be used thoughtfully - if you genuinely don't know whether a data source's structure has changed recently, doing a full "Refresh schema and data" occasionally is still the safer choice, especially before a report goes out to a client or stakeholder. If you've been refreshing an entire model out of habit when only one table actually needed it, this is worth checking out the next time you're in the Power BI Service. Thanks for reading! Connect with me on: LinkedIn | Data With Pankaj - YouTubeLost Track of Which Feature Workspace Belongs to What? Fabric's New Relations API Fixes That
The problem: branch workspaces multiply, and nothing tracks them If your team uses Fabric's Git integration with branching, you've probably experienced this pattern: someone creates a new feature branch in source control, and Fabric spins up a matching "feature workspace" so they can build and test in isolation without touching the shared Dev or Test environment. This is genuinely useful - it means multiple people can work on different features at the same time without stepping on each other's changes. The trouble shows up a few weeks later. You've now got a dozen feature workspaces sitting in your tenant, and there's no built-in way to look at one and know which project it belongs to, who created it, or whether it's safe to delete. Some of those branches got merged and closed out weeks ago, but the workspace never got cleaned up. Others are still actively being used. From the outside, they all just look like... workspaces. Sorting that out has generally meant manually checking naming conventions, asking around, or just leaving stale workspaces sitting there because nobody wants to risk deleting the wrong one. What Fabric's Workspace Relations API actually does In the August 2026 Fabric update, Microsoft introduced a set of API endpoints that let you create, query, update, and delete relationships between workspaces - specifically, the link between a "parent" workspace (your main Dev or Test environment) and the "branch" or "feature" workspaces created from it. In plain terms: instead of a feature workspace being just another disconnected item in your tenant, it now carries an explicit, query able record saying "I belong to this parent workspace." Any tool, script, or automation you build can ask Fabric directly, "show me every branch workspace linked to this parent," and get a real answer - not a guess based on naming patterns. A real example Picture a typical setup: your team's Git-integrated Fabric project has one main Dev workspace, and every time someone starts a new feature branch, an automated pipeline (using Azure DevOps or GitHub Actions) spins up a matching feature workspace, configures its Git connection, and sets the right permissions.Here's where the Relations API closes the loop: after that feature workspace is provisioned, the pipeline calls the Relations API to explicitly link it back to the parent Dev workspace. From that point forward: Anyone (or any script) can query "which branch workspaces belong to this parent?" and get a complete, accurate list When a feature branch gets merged and its workspace is no longer needed, a cleanup script can safely identify orphaned branch workspaces tied to a specific parent, instead of relying on someone remembering to delete it manually If your organization runs a periodic governance check ("do we have workspaces nobody's touched in 60 days?"), that check can now also confirm which parent each one belongs to, making it much easier to decide what's safe to remove None of this requires manually maintaining a spreadsheet or a naming convention discipline that inevitably breaks down over time. The relationship is a real, first-class piece of information Fabric tracks for you. Where this saves the most time Teams using Git branching heavily, where feature workspaces are created and torn down frequently as part of normal development Governance and cleanup routines, where knowing "does this workspace still have a live purpose" used to require manual investigation Building your own internal tooling or dashboards that need to show a clear picture of your Fabric tenant's workspace structure, rather than a flat, unorganized list What to keep in mind This is an API-level capability, so using it directly means writing a bit of automation - typically something your DevOps or platform team would set up as part of your existing branch-workspace pipeline, rather than something you'd click through in the Fabric UI Day to day. If that's not your role, the practical takeaway is simpler: ask whoever manages your team's Git integration and CI/CD setup whether this is wired into your branch workspace pipeline yet. It turns "we have no idea which workspaces are still needed" into a question your tooling can actually answer. If your tenant has been quietly accumulating feature workspaces with no clear ownership trail, this is worth bringing up with your Fabric admin or platform team as a governance improvement. Thanks for reading! Connect with me on: LinkedIn | Data With Pankaj - YouTubeModel Explainability That Goes Beyond the Notebook for Data Science in Microsoft Fabric
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.From Chaos to Clarity: A Supply Chain Response App with Fabric's SQL Database
Supply chains break in ways that rarely announce themselves in advance. A supplier goes dark, a shipment stalls at a border, a single component runs low in a warehouse halfway across the world — and suddenly a business is scrambling to figure out what's actually happening and what to do about it. The businesses that recover fastest aren't the ones with the fewest disruptions; they're the ones who can see the problem the moment it appears. That's the premise behind a hands-on project I've been working through: building a Supply Chain Disruption Response App entirely inside Microsoft Fabric, using its native SQL database capability. No separate database server to provision, no disconnected BI layer bolted on afterward — just one unified platform taking raw supplier and warehouse data and turning it into something a team can actually act on. It's a big enough build that I'm splitting it into a short series. This first post covers the full data foundation — standing up the workspace and database, loading and shaping the data, layering analytical views on the SQL analytics endpoint, and turning it all into a live Power BI report. Later posts will pick up from here and cover notebooks, DevOps, a GraphQL API, and finally a real web application. Here's how the foundation came together, and what stood out along the way. Starting with a Workspace, Not a Server The first thing that struck me about Fabric is how little "infrastructure thinking" it requires. There's no spinning up a VM, no configuring a database engine from scratch. Everything begins with a workspace — essentially a shared home for every item in the project: databases, pipelines, notebooks, and reports all live side by side. Setting one up is a matter of naming it, attaching it to a Fabric capacity, and deciding on a semantic model storage format. Ten minutes in, I had a dedicated space — Supply Chain Analytics Workspace — ready to hold the entire project without a single line of provisioning script. Spinning Up the Database From there, creating the actual SQL database is almost anticlimactic in its simplicity: a name, a click, and Fabric provisions a fully managed SQL database behind the scenes. I called mine supply_chain_analytics_database, and within moments it was ready to accept data. Fabric even ships with sample retail data (the familiar SalesLT schema — customers, products, orders) that loads in with one click. It's a small touch, but it meant I could start writing real queries immediately instead of wrestling with seed data. Teaching the Database to Think About Supply Chains Sample sales data is useful, but it doesn't know anything about supply chains. So the next step was to give the database a vocabulary for the problem at hand: a dedicated SupplyChain schema, and inside it, a Warehouse table modeling the essentials — which product ties to which component, which supplier provides it, where that supplier is located, and how much stock is currently on hand. Rather than hand-typing rows, I populated the table directly from the existing product catalog, using T-SQL to generate plausible component, supplier, and location IDs on the fly. In a few lines of script, the database went from "generic retail demo" to "a working model of a multi-tier supply network" — the exact structure you'd need to trace a disruption back to its source. One small but genuinely useful discovery here: Fabric's query editor has a Copilot-style assist built in. Typing a plain-English comment like "show the total number of customers" and pressing Tab generates the T-SQL for you — and there's an "Explain query" option that annotates existing code step by step. For anyone who's ever inherited a gnarly SQL script with zero comments, this alone is worth the price of admission. Bringing in the Outside World Real supply chains don't live in one database — they're distributed across vendors, partners, and legacy systems that were never designed to talk to each other. To simulate that, I used a Fabric Data Pipeline with a Dataflow Gen2 to pull supplier data from an external OData feed (in this case, the Northwind Traders sample service), representing a partner organization sharing the same supplier network. https://services.odata.org/v4/northwind/northwind.svc/ What made this step interesting wasn't the mechanics — point, click, choose a table, run — but what it represents: Fabric's pipelines are built to treat "connect to an external partner's data" as a first-class, repeatable operation rather than a one-off script someone writes and forgets. Once the dataflow published and the database refreshed, a new Suppliers table appeared right alongside the internally generated data, ready to be joined. Turning Rows into Answers With warehouse and supplier data now living in the same database, the real payoff arrives: queries that answer questions a disruption-response team would actually ask. A simple ranking query — which products are moving the most — is useful on its own. But the more important artifact is a view: a saved query, vProductsbySuppliers, that joins warehouse inventory against supplier records and groups the results by supplier location. That view becomes a reusable lens on the data — the kind of object that can sit quietly behind a dashboard, or be exposed through Fabric's SQL analytics endpoint and GraphQL API for a front-end app to query directly. It's the difference between running a report once and building infrastructure a team can lean on every day. The Parts You Don't Have to Build Perhaps the most understated part of the whole exercise was everything I didn't have to configure. Fabric's SQL database comes with a built-in Performance Dashboard tracking CPU consumption, connection counts, and allocated storage, plus automatic indexing that tunes itself based on query patterns over time. Backups happen on a schedule without anyone setting up a maintenance window. For a demo project this is a nice-to-have; for a production disruption-response system running around the clock, it's the difference between a database that needs a dedicated administrator and one that mostly looks after itself. Querying Through the SQL Analytics Endpoint Every SQL database in Fabric quietly maintains a second identity: the SQL analytics endpoint. It's a read-only, automatically synchronized mirror of the same data, purpose-built for analytical querying rather than transactional writes. Practically speaking, that means I could point ordinary T-SQL at it — no different syntax, no separate connection ceremony — and start layering real analytical logic on top of the operational tables without ever putting extra query load on the transactional database itself. That separation turned out to be the right place to build out the analytical model properly. Rather than one flat query, I created three views, each with a distinct job: vProductsBySupplier rolls up order quantities by product and supplier; vSalesByDate breaks sales volume down by year and month; and vTotalProductsByVendorLocation sits on top of the first view and groups everything by supplier location — the exact cut a disruption-response team needs when a single region goes offline. Building views on top of other views felt like the right instinct here rather than a shortcut. vTotalProductsByVendorLocation didn't need to know anything about sales order headers or line-item details — it just needed the already-summarized product-and-supplier numbers, joined once more against the warehouse table to bring location into the picture. Each layer stays simple, and each one is independently reusable. CREATE VIEW SupplyChain.vProductsBySupplier AS -- View for total products each supplier SELECT sod.ProductID , sup.CompanyName , SUM(sod.OrderQty) AS TotalOrderQty FROM SalesLT.SalesOrderHeader AS soh INNER JOIN SalesLT.SalesOrderDetail AS sod ON soh.SalesOrderID = sod.SalesOrderID INNER JOIN SupplyChain.Warehouse AS sc ON sod.ProductID = sc.ProductID INNER JOIN dbo.Suppliers AS sup ON sc.SupplierID = sup.SupplierID GROUP BY sup.CompanyName, sod.ProductID; GO CREATE VIEW SupplyChain.vSalesByDate AS -- Product Sales by date and month SELECT YEAR(OrderDate) AS SalesYear , MONTH(OrderDate) AS SalesMonth , ProductID , SUM(OrderQty) AS TotalQuantity FROM SalesLT.SalesOrderDetail AS SOD INNER JOIN SalesLT.SalesOrderHeader AS SOH ON SOD.SalesOrderID = SOH.SalesOrderID GROUP BY YEAR(OrderDate), MONTH(OrderDate), ProductID; GO CREATE VIEW SupplyChain.vTotalProductsByVendorLocation AS -- View for total products by each supplier by location SELECT wh.SupplierLocationID AS 'Location' , vpbs.CompanyName AS 'Supplier' , SUM(vpbs.TotalOrderQty) AS 'TotalQuantityPurchased' FROM SupplyChain.vProductsBySupplier AS vpbs INNER JOIN SupplyChain.Warehouse AS wh ON vpbs.ProductID = wh.ProductID GROUP BY wh.SupplierLocationID, vpbs.CompanyName; GO From Views to Visuals: Building the Power BI Report Views are only half the story — someone still has to look at them. Fabric makes the handoff from database to BI tool almost seamless, but there's a small manual step worth calling out: grabbing the connection string. Under the database's Settings, the connection string contains both the server name and the database name buried inside a longer string, and it's these two values — not the full string — that external tools like Power BI Desktop or SQL Server Management Studio actually want. Inside Fabric itself, though, the path to a report is more direct. Selecting New semantic model over the SQL analytics endpoint pulls in the tables and views I'd built — including all three SupplyChain views — and wraps them in a data model describing how everything relates. One default was worth overriding immediately: the Location field on vTotalProductsByVendorLocation is a location identifier, not a quantity, so it needed Summarize by set to None. Otherwise Power BI happily sums location IDs together into a meaningless total, which is a small trap worth knowing about before it shows up in a report. Why This Pattern Matters Fabric collapses a workflow that traditionally spans several tools — a database server, an ETL tool, a BI platform, a monitoring dashboard — into one connected environment. For a supply chain disruption app specifically, that matters because disruptions don't wait for data to be reconciled across systems. The faster raw supplier and inventory data can be joined, queried, and visualized, the faster a team can answer the question that actually matters in a crisis: what's affected, and what do we do next? Building this out end-to-end — workspace, database, ingested and generated data, a layered set of analytical views, and a working Power BI report — took a single sitting. That speed is really the headline feature. The tools get out of the way fast enough that most of the effort goes into modeling the problem, not the plumbing. What's Coming Next in This Series With the data foundation and the first report in place, the next posts in this series will build on top of it: Part 2 — Notebooks, DevOps, and GraphQL: exploring the data interactively in a Fabric Notebook, bringing the database schema under source control with Azure DevOps, and standing up a GraphQL API on top of the vProductsbySuppliers view. Part 3 — The Application: wiring all of it together into a working ASP.NET web app that lets a disruption-response team type in an affected location and instantly see every supplier and product count at risk. Stay tuned — the foundation and the first report are in place; next up is making the data interactive.
