Fabric Warehouse vs Fabric Lakehouse
T-SQL Analytics vs. Spark & Delta Lake Architecture
Compare Microsoft Fabric Warehouse and Lakehouse items to select the right engine for your data engineering and reporting teams.
Specification Matrix
| Feature / Capability | Fabric Data Warehouse | Fabric Lakehouse |
|---|---|---|
| Primary Persona | SQL Developers & Data Warehouse Engineers | Data Engineers & Data Scientists |
| Primary Language | T-SQL | PySpark, Scala, Spark SQL, Python |
| ACID Transactions | Full T-SQL ACID Transactions | Delta Lake ACID Transactions |
| Storage Format | OneLake Delta Parquet (T-SQL managed) | OneLake Delta Parquet (Tables & Files) |
| Unstructured Data Support | No (Structured Tabular Data) | Yes (Files, Images, PDFs, Delta) |
Key Architectural Differences
1Developer Paradigm
Fabric Warehouse provides a standard T-SQL engine ideal for enterprise data warehouse developers. Lakehouse provides a unified Apache Spark environment with support for unstructured files and notebooks.
2Cross-Querying via OneLake
Because both Warehouse and Lakehouse store tables in open Delta Parquet format in OneLake, T-SQL queries can seamlessly join data across Warehouses and Lakehouses within the same workspace.
Use Lakehouse for data ingestion, unstructured files, and PySpark transformations. Use Warehouse for enterprise dimensional modeling and T-SQL analytics. Combine both in a Medallion Architecture.
Frequently Asked Questions
Microsoft Fabric Knowledge Graph & Related Tools
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