Microsoft Fabric: Warehouse vs Lakehouse - When to Use Each

Microsoft Fabric provides two primary storage options for analytical data: the Warehouse and the Lakehouse. Choosing between them depends on your team skills, data processing patterns, compliance requirements, and performance needs. This guide breaks down the key differences with detailed comparison to help you make the right architectural decision. Our Microsoft Fabric consulting team helps enterprises design the optimal storage architecture.

Head-to-Head Feature Comparison

| Capability | Fabric Warehouse | Fabric Lakehouse | |---|---|---| | Query engine | Full T-SQL (DDL + DML) | Spark SQL + SQL analytics endpoint (read-only T-SQL) | | Data format | Managed internal (Delta-based) | Delta Lake (Parquet + transaction log) on OneLake | | Schema approach | Schema-on-write (strict) | Schema-on-read (flexible) or schema-on-write | | Power BI integration | SQL analytics endpoint (DirectQuery) | Direct Lake mode (import-speed, no copy) | | Stored procedures | Yes (T-SQL) | No (use Spark notebooks instead) | | Cross-database queries | Yes (same workspace) | No (use OneLake shortcuts instead) | | Unstructured data | Not supported | Yes (Files section: images, PDFs, logs) | | Data science / ML | Limited (T-SQL only) | Native Spark with Python, Scala, R | | Real-time ingestion | T-SQL INSERT/COPY INTO | Eventstreams, Spark Structured Streaming | | Storage cost | Same Fabric CU billing | Same Fabric CU billing | | ACID transactions | Yes | Yes (Delta Lake) | | Time travel | Limited | Yes (Delta versioning) | | Security | T-SQL GRANT/DENY, column-level | Spark-level + SQL endpoint RBAC |

Fabric Warehouse Deep Dive

The Fabric Warehouse is a fully managed SQL-based analytical data warehouse built on the same distributed engine as Azure Synapse Analytics. It is designed for teams with strong SQL Server and T-SQL expertise who need structured, governed data storage with familiar tooling.

Key capabilities: - Full T-SQL support for DDL (CREATE TABLE, ALTER, DROP) and DML (INSERT, UPDATE, DELETE, MERGE) - Cross-database queries within the same workspace—join tables across multiple warehouses without data movement - Automatic distribution, indexing, and statistics management (no manual tuning required) - Native integration with Power BI through the auto-generated SQL analytics endpoint - Support for views, stored procedures, and functions for encapsulating business logic - COPY INTO command for high-throughput data loading from OneLake, Azure Blob, or ADLS Gen2

When to Choose Warehouse

• Your team has deep SQL Server, Azure Synapse, or Snowflake experience and prefers T-SQL workflows
• Workloads are primarily structured SQL queries, stored procedures, and scheduled jobs
• You need cross-database joins across multiple data domains in the same workspace
• Strict schema-on-write enforcement is required for healthcare compliance (HIPAA, SOC 2)
• You are migrating from an existing SQL Server, Azure Synapse, or on-premises data warehouse
• Business logic is heavily encoded in stored procedures that are difficult to rewrite in Python

Fabric Lakehouse Deep Dive

The Fabric Lakehouse combines the flexibility of a data lake with the analytical capabilities of a data warehouse. All data is stored in Delta Lake format on OneLake, supporting both Apache Spark (Python, Scala, R) and SQL query engines simultaneously.

**Key capabilities:** - Schema-on-read flexibility for semi-structured data (JSON, Parquet, CSV, Avro) in the Files section - Native Apache Spark runtime with PySpark, Spark SQL, Scala, and R for complex data engineering - Automatic Delta Lake format with ACID transactions—no "data swamp" problem - Built-in OneLake shortcuts to external data in ADLS Gen2, Amazon S3, Google Cloud Storage, and other Fabric items - Direct Lake mode for Power BI semantic models—import-like query speed without copying data - Tables section (managed Delta tables) and Files section (unstructured/raw data) in a single item

When to Choose Lakehouse

• Your team includes data engineers who prefer Python, PySpark, and notebook-driven development
• You need to process semi-structured or unstructured data (JSON logs, IoT telemetry, images, PDFs)
• Data engineering pipelines require complex transformations best expressed in Spark
• You want Direct Lake mode for zero-copy Power BI reporting at import speeds
• You are building a medallion architecture (Bronze, Silver, Gold layers)
• Machine learning workloads need native Spark ML, MLflow, and Python library access

Decision Framework

Use this framework to choose the right storage item for each workload:

Choose Warehouse when: 1. The data is fully structured with well-defined schemas 2. The primary consumers are SQL analysts and BI developers 3. You need stored procedures for complex, reusable business logic 4. Migration from existing SQL-based warehouses should preserve T-SQL investments 5. Cross-database queries are required for multi-domain reporting

Choose Lakehouse when: 1. Data arrives in mixed formats (structured, semi-structured, unstructured) 2. The primary consumers are data engineers and data scientists 3. You need Spark for transformation, ML, or advanced analytics 4. Direct Lake mode for Power BI is a priority to eliminate import refresh overhead 5. You want maximum flexibility with schema evolution as requirements change

Choose both when: 1. Different teams have different skill sets (SQL analysts and Python engineers) 2. Raw data needs Spark processing but curated data serves SQL reporting 3. You are implementing a medallion architecture: Lakehouse for Bronze/Silver, Warehouse for Gold

Using Both Together: The Hybrid Pattern

Many organizations use both Warehouse and Lakehouse in the same Fabric workspace. The most common hybrid pattern:

1. Lakehouse (Bronze layer): Ingest raw data from source systems using Spark notebooks, Dataflows Gen2, or data pipelines. Store raw data in the Files section and initial Delta tables in the Tables section.
2. Lakehouse (Silver layer): Transform, clean, and conform data using PySpark. Apply data quality rules, standardize schemas, and handle slowly changing dimensions.
3. Warehouse (Gold layer): Load curated data into the Warehouse using COPY INTO or cross-item queries. Apply business logic through stored procedures and views. This is the layer that Power BI reports and SQL analysts query directly.

This pattern leverages the strengths of each engine where they excel: Spark for data engineering flexibility, T-SQL for governed business logic and reporting.

Performance Considerations

Warehouse strengths: - Highly optimized for structured analytical queries with automatic columnar storage and intelligent result caching - Cross-database joins execute efficiently within the distributed SQL engine - Automatic statistics and distribution eliminate manual tuning - Best for repetitive, parameterized queries that benefit from cached query plans

Lakehouse strengths: - Superior throughput for large-scale data processing with complex transformations and joins across semi-structured data - Spark engine scales horizontally for ML feature engineering and batch processing - Direct Lake mode in Power BI provides the fastest path from raw data to dashboard without data duplication - V-Order optimization on Delta files improves both Spark and SQL analytics endpoint query performance

**Capacity impact**: Both consume Fabric Capacity Units (CUs) but at different rates. Long-running Spark jobs consume CUs continuously, while SQL warehouse queries consume CUs per query. Monitor the Fabric Capacity Metrics app to understand the cost profile of each workload.

Migration Paths

From Azure Synapse Dedicated SQL Pool → Fabric Warehouse: High T-SQL compatibility. Most DDL and DML statements transfer directly. Test complex stored procedures, materialized views, and external tables which may need adjustment.

From Azure Data Lake + Databricks → Fabric Lakehouse: Delta Lake compatibility enables direct migration of Delta tables. OneLake shortcuts can reference existing ADLS Gen2 data without copying. Spark notebooks require minor syntax adjustments for Fabric's runtime.

From Power BI Premium dataflows → Fabric Lakehouse: Dataflows Gen2 output directly to Lakehouse tables in OneLake, enabling Direct Lake mode. This is the recommended modernization path for organizations on Power BI Premium moving to Fabric.

Related Resources

• Fabric Lakehouse vs Warehouse 2026
• Modern Data Lakehouse
• Direct Lake Mode
• Microsoft Fabric Services