Microsoft Fabric Dataflow Learning Series | Dataflow Gen2 Complete Tutorial | Episode 3
Welcome to Episode 3 of the Microsoft Fabric Dataflow Complete Overview series from NextGen Data Experts Community. In this session, we take a deep dive into Microsoft Fabric Dataflow Gen2 and explore its architecture, core components, supported connectors, and practical implementation through live demonstrations. This series is designed for Power BI Developers, Data Analysts, Data Engineers, and Microsoft Fabric learners who want hands-on experience with modern data integration capabilities. If you find this video useful, please Like , Share , and Subscribe to NextGen Data Experts Community for more Microsoft Fabric, Power BI, and Data Engineering content.Microsoft Fabric Dataflow Gen1 Complete Tutorial | Creation, Transformations & Reporting | Episode 2
Welcome to Episode 2 of the Microsoft Fabric Dataflow Learning Series from NextGen Data Aspirants Community. In this session, we take a deep dive into Dataflow Gen1 and explore its complete workflow through live demonstrations in the Microsoft Fabric environment. If you find this content useful, please Like, Share, and Subscribe to NextGen Data Aspirants Community for more Microsoft Fabric, Power BI, Data Engineering, and Analytics content. watch?v=oVqd-F6-65A?si=CYrAQmRmq12JLQA4Sportsphere-Fabric-End-to-End-Data-Pipeline
Project Overview sportsphere is an end-to-end e-commerce analytics solution built on Microsoft Fabric. The project leverages Fabric's unified data platform to automate data ingestion, transformation, storage, and reporting, enabling business users to analyze sales performance, product trends, customer behavior, and regional growth. Business Question: Which regions and products drive sales performance, and where should the business focus to maximize growth? Microsoft Fabric Architecture Source Data → Fabric Data Factory → Fabric Lakehouse (Bronze) → PySpark (Silver) → Gold Layer → Power BI Microsoft Fabric Features Used Fabric Data Factory for automated and incremental data ingestion Fabric Lakehouse with OneLake for centralized data storage PySpark Notebooks for data cleansing and transformation Medallion Architecture (Bronze, Silver, Gold) for scalable data processing Delta Lake tables for reliable and efficient data storage Power BI for interactive dashboards and business insights GitHub integration for version control Project Workflow Bronze Layer: Ingest raw CSV files into the Fabric Lakehouse using Data Factory. Silver Layer: Transform, clean, and standardize data using PySpark notebooks. Gold Layer: Create analytics-ready business tables and aggregations. Power BI: Build interactive dashboards to analyze sales, products, regions, and customer trends. Business Insights The dashboard helps answer: Which regions generate the highest revenue? Which products drive sales growth? How do sales trends change over time? Where should the business focus inventory and marketing efforts? watch?v=l-85-p_1CR4Microsoft Fabric Dataflows FULL Course | Gen1 vs Gen2 Explained | Episode 1
Welcome to the NEW Microsoft Fabric Dataflows Complete Playlist by NextGen Data Aspirants Community! In Episode 1, we start with the complete theoretical foundation of Microsoft Fabric Dataflows and understand: ✅ What are Dataflows in Microsoft Fabric ✅ Dataflow Gen1 vs Dataflow Gen2 ✅ Why Gen2 is powerful ✅ Connectors and Destinations ✅ Low-Code ETL Concepts ✅ CI/CD and Git Integration ✅ Fabric Lakehouse & Warehouse Support ✅ Automation and Data Transformation Concepts If you want to master Microsoft Fabric from scratch with real-time concepts and practical learning, this playlist will help you step-by-step. 🔥 Subscribe now and start your Fabric journey with us! watch?v=aX5Vf7V3hSs
Fabric Monday 113: Notebook Connections
📓 Your Fabric notebook shouldn't know your password. Hardcoding credentials in notebook code is a risk hiding in plain sight. Anyone with access to the notebook can read them. Connection Objects solve this. 🔐 Authentication lives inside the Connection Object — not in your code. The notebook uses the connection. It never sees the credentials. In this week's Fabric Monday, I show how to wire notebooks to Connection Objects and why this is the right way to handle authentication in Fabric. 🎥 watch?v=qjUa_gyBAeMFabric Monday 118: SparkSQL in Lakehouse UI
▶️ Fabric Monday #118 — Spark SQL in Lakehouse UI 🏠 You can now run Spark SQL directly inside the Microsoft Fabric Lakehouse UI. No notebook. No switching context. Just open the Lakehouse, write your query, and run it — right there. This is completely new, and it changes how you work with a Lakehouse day to day. 🔍 SPARK SQL QUERY EXPLORER The new Query Explorer is built into Lakehouse Explorer and gives you: ⌨️ IntelliSense as you type 📑 Dynamic tabs — multiple queries open at once 🔗 Cross-schema and cross-lakehouse querying 📊 Inline chart visualization 📋 View creation — directly from the UI 🏷️ VIEWS ARE NOW IN THE UI TOO 🗂️ Create a Spark SQL view in a schema-enabled lakehouse — it appears in Lakehouse Explorer alongside your tables, stored in OneLake. ✏️ Rename it, delete it, or inspect the full query definition — right from the context menu. 🔗 The view can JOIN tables from other lakehouses or workspaces in its SELECT. ⚠️ TWO THINGS TO KNOW 🔒 Schema-enabled lakehouse is required — non-schema lakehouses store views in the metastore, invisible in the UI. ⏳ Current limitation: SQL analytics endpoint and Semantic Models don't yet recognize Spark views. watch?v=sksEw4VkOeoFabric Monday 102: New Shortcut Transformations - Parquet and JSON
Ever wondered how far Shortcut Transformations can really go in Microsoft Fabric? I’ve just published a new video that explores a powerful new angle on working with Parquet and JSON data. No long explanations here — just a practical look at what’s now possible, why it matters, and how it can simplify real-world data workflows. If you work with: 👉 Lakehouses 👉 External data sources 👉 Parquet or JSON at scale …this one’s for you 👀 watch?v=2kA5B8DHplcFabric Monday 110: Onelake Row and Column Security
🔐 How many places in Microsoft Fabric do you define who sees what? 🤔 🧩 Semantic model → its own RLS 🧩 SQL Endpoint → its own access rules 🧩 Spark notebook → another layer Each one enforced independently. Each one maintained separately. Each one a place where something can drift. 🎯 OneLake Security defines security once — at the storage layer. 🔸 Row-Level Security → SQL WHERE clause, scoped per role 🔸 Column-Level Security → control exactly which columns each role can see One definition. Automatically enforced across Spark, SQL Endpoint, Power BI — every engine reading the data. ⚠️ What to know before you start: → RLS and CLS must live in the same role when used together → Mixing them across roles breaks queries entirely → Once you enable OneLake Security on an item, it cannot be turned off watch?v=U1Jdbw-pal0Fabric Monday 112: Virtual Members In Onelake Security
🔐 OneLake security roles can have members nobody explicitly added. That's not a bug. That's Virtual Members. 👥 In Microsoft Fabric, workspace roles and item permissions don't just control the workspace. They automatically map into OneLake security roles inside a Lakehouse. 🏢 Assign someone as a Workspace Viewer. 🏠 They land in the matching OneLake role — automatically, without a separate step. Most teams don't know this exists and end up managing two layers of security separately. You don't have to. ✅ In this week's Fabric Monday, I walk through how Virtual Members work, how the mapping is structured, and what it means for your Lakehouse security model. 🎥 watch?v=GPI50TzJNCs
