Data warehousing in Microsoft Fabric represents the next-generation approach to handling enterprise analytics. Unlike traditional warehousing solutions, Fabric integrates tightly with the OneLake architecture, providing real-time data ingestion, simplified data modeling, and powerful T-SQL-based querying — all in a unified SaaS environment.
What You Will Learn
- What is a Data Warehouse in Microsoft Fabric?
- How it differs from Lakehouse
- Warehouse architecture and how it integrates with OneLake
- Supported workloads and real-time analytics
- T-SQL support and semantic model auto-generation
- Best practices and real-world scenarios
Understanding Fabric Data Warehouse
Microsoft Fabric Data Warehouse is a high-performance, scalable solution built natively on Delta-Parquet files, optimized for structured data. It supports high-concurrency workloads and integrates deeply with tools like Power BI and Data Factory.
Key Characteristics:
- Fully managed and serverless compute
- Delta-Parquet based open format
- Built-in semantic model integration
- Auto-scaling, high-throughput performance
- Governed and secure using Microsoft Purview
Fabric Data Warehouse Architecture
At the core of Fabric’s warehouse is the Lakehouse engine. However, it abstracts that complexity and exposes a simplified, fully SQL-based interface.
Warehouse Architecture Layers:
- Storage: Delta-Parquet tables in OneLake
- Compute: Serverless SQL runtime optimized for warehousing
- Model: Automatically generated semantic models for Power BI
- Access: Power BI, Notebooks, T-SQL endpoints, REST APIs
Lakehouse vs Data Warehouse in Fabric
Although both Lakehouse and Warehouse share the same OneLake foundation, they serve different use cases. Here’s how they compare:
| Feature | Lakehouse | Data Warehouse |
|---|---|---|
| Storage | File-based (Delta + Parquet) | Delta-Parquet (structured, governed) |
| Best For | Flexible ETL, Semi/Unstructured data | Structured analytics, governed data |
| Interface | Notebooks, Spark, Power BI | T-SQL, Power BI, Visual Query Editor |
| Use Case | Data science, raw data landing | BI dashboards, reporting, governed modelling |
T-SQL Support and Semantic Model
Fabric’s warehouse lets you run rich T-SQL queries directly on your warehouse tables. Key features:
- DDL, DML, window functions, temp tables
- Optimized performance using serverless engine
- Automatic creation of semantic models for Power BI
Real-Time Analytics with Warehouse
Data Warehouses in Fabric can connect to real-time data pipelines. With auto-refresh and direct lake queries, Power BI reports update almost instantly.
Scenarios:
- Sales reporting dashboards
- Inventory and supply chain visibility
- Customer interaction analytics
When to Use Data Warehouse over Lakehouse?
- Data is structured and doesn’t change frequently
- Real-time dashboarding and reporting needed
- End-users interact with Power BI
- Requires fine-grained security and governance
Best Practices
- Partition large tables by date or logical keys
- Use star schema for Power BI integration
- Secure with Microsoft Purview & Workspace Roles
- Leverage semantic models to avoid redundancy
What’s Next?
In the next tutorial, we explore Dataflow Gen2 in Microsoft Fabric — a powerful tool to visually build data transformation logic with Power Query Online.
Read Next: Dataflow Gen2 – Microsoft Fabric Tutorial Series
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Series: Free Microsoft Fabric Tutorial: A Step-by-Step Learning Series
