Get productive in Microsoft Fabric: OneLake, lakehouses, warehouses, Dataflow Gen2, data pipelines, Spark notebooks, and Direct Lake reporting in Power BI.
Microsoft Fabric promises to end the fragmentation of modern data stacks — but what actually is it, and how does it work? This foundational lesson breaks down every Fabric workload, explains how OneLake eliminates data duplication, and shows exactly where Power BI fits into the picture.
OneLake is the storage backbone of Microsoft Fabric — but understanding it deeply means going beyond the marketing pitch. This lesson covers how OneLake is physically structured, how Delta Lake transaction logs actually work, how Shortcuts virtualize data across clouds, and the operational pitfalls that will slow you down if you skip them.
Learn how to create a Microsoft Fabric Lakehouse from scratch, load CSV files into both the Files and Tables zones, and query your data using the SQL Analytics Endpoint. This hands-on lesson builds the foundational skills you need for every Fabric data engineering project.
Both the Fabric Lakehouse and Warehouse store Delta tables in OneLake — so why does picking the wrong one cause so much pain? This lesson explains the architectural differences that actually matter and shows you how to make a confident, defensible choice for any workload.
Dataflow Gen2 is more than a drag-and-drop ETL tool — it's a Power Query runtime with query folding, a staging layer, and Delta table output built into Microsoft Fabric. This deep-dive lesson teaches you how to connect to diverse sources, write effective M transformations, maximize query folding, configure incremental refresh, and troubleshoot the failures that trip up even experienced practitioners.
Learn how to build reliable, automated data pipelines in Microsoft Fabric from the ground up. This hands-on lesson covers Copy Activities, dynamic parameters, activity chaining with dependencies, and scheduled triggers — everything you need to load data without manual intervention.
Learn how to use PySpark in Fabric Spark notebooks to clean, enrich, and write production-quality Delta tables to your lakehouse. Covers deduplication, Delta merge, partitioning, and parameterized notebooks that plug into Data Pipelines.
Learn how to design and build a production-ready medallion architecture in Microsoft Fabric using three separate lakehouses for Bronze, Silver, and Gold layers. This lesson goes beyond theory — you'll write real PySpark notebooks with MERGE patterns, quarantine logic, and Delta optimization for Power BI Direct Lake reporting.
Direct Lake mode reads Delta Parquet files directly from OneLake into Power BI's in-memory engine — delivering Import-speed performance without scheduled refreshes. Learn how framing, transcoding, and fallback work, and build a production-ready Direct Lake semantic model from a Fabric lakehouse.
Learn how to design and build a production-ready dimensional model in Microsoft Fabric Warehouse using T-SQL. This lesson covers DDL for fact and dimension tables, view design patterns, and cross-database queries that join warehouse and lakehouse data without moving it.
OneLake Shortcuts let you query data in ADLS Gen2 and Amazon S3 directly from Microsoft Fabric — no copying, no pipelines, no duplication. This deep-dive lesson covers the internal architecture, credential configuration, Spark and T-SQL querying patterns, performance optimization, and the security model you must understand before deploying shortcuts in production.
Learn how to build a complete real-time analytics pipeline in Microsoft Fabric using Eventstreams for ingestion, Eventhouses for ultra-fast time-series storage, and KQL Querysets for millisecond-latency queries. This expert-level lesson covers architecture internals, KQL query patterns, materialized views, and Power BI integration.
Database Mirroring in Microsoft Fabric creates continuous, near-real-time replication from Azure SQL Database and Snowflake directly into OneLake as Delta tables — eliminating scheduled ETL and enabling Direct Lake Power BI reporting on always-fresh operational data. This deep-dive lesson covers CDC configuration, Snowflake change tracking, schema change handling, and production operations.
Learn how to combine Microsoft Fabric's Git integration and deployment pipelines into a production-grade SDLC. This deep-dive lesson covers repository serialization, branching strategies, deployment rules, CI/CD automation, and the edge cases that trip up even experienced Fabric engineers.
Fabric's security model has four distinct permission layers — and most teams only know about one of them. This deep-dive lesson teaches you how workspace roles, item permissions, and OneLake data access policies interact so you can design and audit a governance model that actually holds up in production.
Learn how to use Microsoft Fabric's Pipeline Copy Activity to ingest data from REST APIs, Azure Blob Storage, and Azure SQL databases into a Lakehouse. This hands-on lesson walks through real configuration steps, explains every key decision, and covers common pitfalls so your first ingestion pipeline actually works.
When pipelines run slow or fail silently, you need more than hope — you need visibility. This lesson teaches you how to use the Fabric Monitoring Hub and Capacity Metrics app to track activity, diagnose failures, and understand what your workloads are actually costing your capacity.
Learn how to turn a basic Fabric pipeline into a production-grade, self-healing automation. This lesson covers schedule triggers, activity-level retry policies, workspace alerts, and custom email notifications with dynamic content — everything you need to stop monitoring pipelines manually.
Learn how to build a real PySpark notebook in Microsoft Fabric from scratch — reading CSV files from Lakehouse storage, cleaning and transforming data, and writing Delta tables that are immediately queryable via SQL. This hands-on lesson teaches Spark fundamentals in the context you'll actually use them.
Direct Lake mode is Power BI's fastest connection strategy for Fabric lakehouses — but only if your Delta tables are structured correctly. This lesson teaches you how to create V-Ordered gold tables, manage framing, diagnose DirectQuery fallback, and keep your reports fresh without expensive import cycles.
Every Fabric lakehouse comes with a built-in SQL interface that lets you run T-SQL against Delta tables — no warehouse required. Learn how to write queries, create views, join across tables, and connect from SSMS or Power BI, all against the same data your Spark notebooks write.
Full table loads don't scale — and sooner or later every production pipeline needs an incremental strategy. Learn how to build a complete watermark-based incremental load pattern in Microsoft Fabric using Lookup activities, parameterized Copy activities, and PySpark notebook watermark updates.
Delta tables don't optimize themselves. Learn how V-Order encoding, file compaction with OPTIMIZE, multi-dimensional data skipping with Z-Order, and storage reclamation with VACUUM work together to make your Fabric Lakehouse queries dramatically faster and cheaper to store. This lesson goes deep into the internals so you know exactly when and why to apply each technique.
Learn how to bulk-load Parquet and CSV files from OneLake into a Microsoft Fabric Warehouse using COPY INTO and the Pipeline Copy Activity. This hands-on lesson covers file path patterns, CSV configuration options, column mapping, and post-load verification — everything you need to run production-grade ingestion jobs.
Learn how to define Power Query parameters in Dataflow Gen2 and pass runtime values from Fabric pipelines — building a single, reusable ingestion flow that adapts to different regions, time periods, or environments without duplicating logic. Includes a full hands-on exercise and troubleshooting guide.
Row-Level Security in Microsoft Fabric isn't just a T-SQL exercise — it's an architecture decision with serious implications for workspace roles, Power BI Direct Lake, and Spark access paths. This lesson walks through building dynamic RLS policies from scratch, understanding where the security envelope stops, and verifying your policies work as intended before real users hit production.
Learn the three essential write patterns for Delta tables in Microsoft Fabric: append, overwrite, and merge. This hands-on lesson teaches you exactly when to use each pattern — and how to avoid the silent data quality problems that come from choosing the wrong one.
Learn how to use Spark notebooks in Microsoft Fabric to read CSV and Parquet files from OneLake, write clean Delta tables to the lakehouse Tables zone, and validate your results in the Lakehouse Explorer — the complete data engineering loop from raw files to queryable data. This foundation-level lesson explains every step from first principles with realistic examples and working PySpark code.
Learn how to connect Power BI Desktop to a Fabric lakehouse SQL Analytics Endpoint, build a star schema with DAX measures, and publish a production-ready report — no Direct Lake required. This lesson explains every step from finding the connection string to scheduling refreshes, with clear guidance on when to use this approach over Direct Lake mode.
Dataflow Gen2 lets you attach multiple output destinations to a single query — run your transformation once and write results to both a Fabric Lakehouse table and a Warehouse table simultaneously. This lesson teaches you the complete pattern: from Power Query transformation to dual-destination configuration, data type gotchas, and production-ready pipeline orchestration.
Schema changes in production Delta lakehouses cascade across medallion layers in ways that break pipelines, corrupt reports, and silently produce wrong results. This deep-dive lesson teaches you exactly how Delta Lake enforces and evolves schemas, how to build drift detection into your pipelines, and how to use CHECK and NOT NULL constraints to enforce data quality where it matters.
Hardcoded file paths and table names don't belong in production notebooks. Learn how to use Fabric's parameter cell pattern to pass dynamic values from a data pipeline into PySpark, turning a one-time script into a reusable ingestion engine.