Microsoft Fabric Warehouse Tutorial for Beginners:
Lakehouse vs Warehouse
What is a Microsoft Fabric Warehouse?
A Microsoft Fabric Warehouse is a dedicated SQL-based storage and compute layer in Microsoft Fabric that delivers data-warehousing performance on an elastic, serverless foundation. Built on the same open Delta Lake format that powers Fabric Lakehouse, it provides full T-SQL support, sophisticated cost-based query optimization, and seamless integration with Power BI, Data Factory pipelines, and Fabric’s real-time experiences.
Think of it as your “single source of truth” for structured, curated data that requires fast analytic queries, governed schemas, and enterprise security—all without the traditional admin overhead of provisioning or scaling physical hardware.
Key Features & Use-Cases of a Fabric Warehouse
- Instant Elasticity – compute spins up on demand and pauses automatically when idle.
- Open Delta Storage – lake-native tables stored in your OneLake, enabling ACID transactions plus time-travel.
- End-to-End T-SQL – use the language you know for DDL, DML, and advanced analytics.
- Built-in Integration – drag-and-drop pipelines, notebooks, and Power BI all in one workspace.
- Fine-Grained Security – row-level & column-level security, AAD-based access, and Purview tag lineage.
Typical scenarios include: enterprise reporting marts, finance conformed dimensions, customer 360 views, or any workload where sub-second SQL latency is business-critical.
Lakehouse vs Warehouse – Where Do They Differ?
| Dimension | Lakehouse | Warehouse |
|---|---|---|
| Interface & Language | Spark, PySpark, Scala, SQL on Spark | Fully ANSI-T-SQL; no Spark surface required |
| Typical Data | Semi-structured & big data | Highly structured, curated facts & dims |
| Performance Goal | High-throughput, ELT heavy lifting | Low-latency BI & ad-hoc SQL analytics |
| Compute Model | Spark clusters (per job or session) | Instant-on serverless SQL engines |
| Best When… | You need ML, streaming, or huge parquet ingest | You need governed, predictable BI query speed |
When Should You Use Each Model?
Use a Lakehouse when your data teams are heavily Spark-oriented, working with large semi-structured sources, or preparing data for machine learning. It shines for data engineering pipelines, streaming ingestion, and advanced data science.
Use a Warehouse when business users demand consistently fast SQL reports, dimensional models, and fine-grained RBAC. Warehouses are optimized for dashboard refreshes, financial consolidations, and ad-hoc slicing by analysts who live in Power BI or Excel.
In practice, many Fabric solutions combine both—a Lakehouse for raw/bronze & silver data processing and a Warehouse for the gold, consumption-ready layer.
Step-by-Step Walkthrough for Absolute Beginners
- Create a Workspace: In the Fabric portal, hit New ► Workspace and assign a Fabric capacity.
- Add a Warehouse: Click New ► Warehouse, give it a name, and hit Create.
- Load Data: Open Get Data, choose Azure Data Lake / OneLake, or simply upload a CSV file.
- Build Tables: Use the
CREATE TABLE … AS SELECTpattern or define schemas via the designer, then load your data. - Query with T-SQL: Run
SELECT TOP 100 *to validate and explore. - Secure & Share: Set row-level security, assign workspace roles, and publish a Power BI report directly from the Warehouse ribbon.
- Monitor Performance: Check the Warehouse Monitoring pane for query metrics and auto-pause settings.