Building a Modern Data Lakehouse with Microsoft Fabric
Step-by-step guide to implementing a data lakehouse architecture using Microsoft Fabric and OneLake.
The data lakehouse architecture combines the best of data warehouses and data lakes. Microsoft Fabric makes building one easier than ever.
Understanding the Lakehouse Architecture
A lakehouse provides: - **Schema enforcement** like a data warehouse - **Low-cost storage** like a data lake - **ACID transactions** for data reliability - **Direct BI access** without data movement
The Medallion Architecture
Fabric Lakehouses typically follow the medallion pattern:
Bronze Layer (Raw) - Ingested data in original format - Minimal transformations - Full history preservation - Schema-on-read flexibility
Silver Layer (Cleansed) - Validated and deduplicated - Standardized schemas - Business rules applied - Quality checks passed
Gold Layer (Curated) - Business-ready aggregations - Dimension and fact tables - Optimized for reporting - Semantic layer ready
Step-by-Step Implementation
Step 1: Create Your Lakehouse In a Fabric workspace, create a new Lakehouse. This automatically provisions OneLake storage and the SQL analytics endpoint.
Step 2: Ingest Data to Bronze Use Data Factory pipelines or Notebooks to land raw data: - Copy activities for batch ingestion - Eventstreams for real-time data - Shortcuts for existing cloud storage
Step 3: Transform to Silver Use Spark notebooks or Dataflows Gen2: - Clean and validate data - Apply business rules - Handle slowly changing dimensions - Output Delta tables
Step 4: Create Gold Layer Build consumption-ready datasets: - Aggregate tables - Star schema models - Pre-calculated metrics
Step 5: Connect Power BI Create semantic models directly from the Lakehouse SQL endpoint for instant reporting.
Best Practices
- Use Delta format for all tables
- Implement proper partitioning
- Set up data quality rules
- Monitor with Fabric Monitoring Hub
- Use Git integration for version control
Frequently Asked Questions
What is the difference between a data lake and a lakehouse?
A data lake stores raw data in various formats without enforcing structure. A lakehouse adds data warehouse capabilities like ACID transactions, schema enforcement, and SQL query support while maintaining the cost benefits of lake storage.
Why use medallion architecture in Microsoft Fabric?
Medallion architecture (Bronze, Silver, Gold layers) provides clear data lineage, separates concerns, enables incremental processing, and ensures data quality improves at each stage before reaching business users.