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Lakehouse vs Warehouse vs SQL Database

Choose the right Fabric storage engine for your workload

Category Status Last Updated

Last Updated: 2026-05-05 | Version: 1.0.0

TL;DR

Use Lakehouse when your team writes PySpark and needs schema-on-read flexibility for semi-structured or mixed data. Use Warehouse when your team is T-SQL-first and the workload is read-heavy analytics with star-schema models. Use SQL Database when you need full OLTP capabilities (CRUD, stored procedures, triggers) for operational applications or transactional microservices.

When This Question Comes Up

  • Starting a new Fabric project and choosing the primary data store
  • Migrating an existing SQL Server, Synapse, or Databricks workload to Fabric
  • A team with mixed T-SQL and PySpark skills needs to pick a default engine
  • You need to expose data to Power BI via Direct Lake and must decide where Delta tables live
  • An application requires both analytical queries and operational transactions

Decision Flowchart

flowchart TD
    START([What kind of data workload?]) --> FORMAT{Data format?}

    FORMAT -->|Semi-structured<br/>JSON, Parquet, CSV, images| LH_DIRECT[Lakehouse]
    FORMAT -->|Structured relational| DML{Need full DML?<br/>INSERT, UPDATE, DELETE<br/>stored procs, triggers}

    DML -->|No - read-heavy<br/>analytics| TEAM{Team's primary<br/>query language?}
    DML -->|Yes - full CRUD| WORKLOAD{Workload type?}

    WORKLOAD -->|OLTP / operational<br/>high concurrency| SQLDB[SQL Database]
    WORKLOAD -->|OLAP / analytical<br/>batch transforms| WH_CHECK{Need T-SQL<br/>CTAS / MERGE?}

    WH_CHECK -->|Yes| WH[Warehouse]
    WH_CHECK -->|No - Spark OK| LH_DIRECT

    TEAM -->|T-SQL, stored procs| WH
    TEAM -->|PySpark, notebooks| LH_DIRECT
    TEAM -->|Both equally| VOLUME{Data volume?}

    VOLUME -->|< 100 GB| WH
    VOLUME -->|> 100 GB or<br/>unstructured mix| LH_DIRECT

    LH_DIRECT --> DL_CHECK{Direct Lake<br/>for Power BI?}
    DL_CHECK -->|Yes| LH_VORDER[Lakehouse<br/>+ V-Order optimization]
    DL_CHECK -->|No| LH_STANDARD[Lakehouse<br/>standard Delta tables]

    WH --> WH_DONE[Warehouse<br/>star-schema analytics]
    SQLDB --> SQLDB_DONE[SQL Database<br/>operational workload]

    style LH_VORDER fill:#4CAF50,color:#fff
    style LH_STANDARD fill:#4CAF50,color:#fff
    style WH_DONE fill:#2196F3,color:#fff
    style SQLDB_DONE fill:#FF9800,color:#fff

Lakehouse

When

  • Data engineering workloads with PySpark, Spark SQL, or notebook-driven development
  • Semi-structured or mixed-format data (JSON, CSV, Parquet, images alongside Delta tables)
  • Medallion architecture (Bronze/Silver/Gold) is the processing pattern
  • Direct Lake connectivity is required for Power BI

Why

  • Native Delta Lake support with schema evolution and time travel
  • Unified storage for files and tables in OneLake
  • Best Direct Lake performance with V-Order optimization
  • Scales to petabyte-level datasets with Spark parallelism
  • Schema-on-read flexibility for exploratory workloads

Tradeoffs

Dimension Assessment
Cost CU consumption on Spark; auto-scale can spike during large jobs
Latency Batch-oriented; not ideal for sub-second transactional queries
Compliance Delta change data feed supports audit trails; no built-in CDC like SQL Database
Skill match Requires PySpark / Spark SQL skills; SQL endpoint is read-only

Anti-patterns

  • Using Lakehouse as an OLTP store for high-frequency INSERT/UPDATE from applications
  • Relying on the SQL analytics endpoint for write operations (it is read-only)
  • Skipping V-Order optimization when Direct Lake is the consumption layer
  • Storing thousands of small files without compaction (small-file problem)

Warehouse

When

  • BI-centric analytics with T-SQL-fluent teams
  • Star-schema dimensional models with CTAS, views, and stored procedures
  • Cross-database queries spanning multiple Fabric workspaces
  • Workloads that need high concurrency for concurrent BI report queries

Why

  • Full T-SQL DML (INSERT, UPDATE, DELETE, MERGE) on managed Delta tables
  • Familiar SQL Server / Synapse DW experience with zero infrastructure management
  • High-concurrency SQL endpoint optimized for BI query patterns
  • Native support for cross-database queries and schema-on-write enforcement

Tradeoffs

Dimension Assessment
Cost CU consumption on SQL engine; long-running transforms can be expensive vs Spark
Latency Good for analytics queries; not designed for OLTP concurrency levels
Compliance No native CDC; relies on pipeline-driven change capture
Skill match Excellent for T-SQL teams; limited for teams needing Python/ML workflows

Anti-patterns

  • Running heavy ETL transforms in Warehouse when Spark would be more cost-effective at scale
  • Using Warehouse for operational OLTP workloads (use SQL Database instead)
  • Expecting Direct Lake to work natively (requires shortcuts to Lakehouse Delta tables)
  • Ignoring result-set caching configuration for repetitive BI queries

SQL Database

When

  • Operational / OLTP workloads requiring high-concurrency CRUD
  • Application backends needing stored procedures, triggers, and row-level security
  • Change data capture (CDC) or change tracking is required at the database level
  • Microservices that need a fully managed SQL Server engine inside Fabric

Why

  • Full SQL Server engine with complete DML, DDL, stored procedures, and triggers
  • Native CDC and change tracking for downstream analytics pipelines
  • Highest concurrency of the three engines for transactional workloads
  • Automatic mirroring to OneLake for analytical consumption via Lakehouse or Warehouse

Tradeoffs

Dimension Assessment
Cost CU consumption on SQL; separate from Spark/Warehouse budgets
Latency Sub-millisecond transactional reads; analytical queries better served by Lakehouse/Warehouse
Compliance Built-in audit, CDC, TDE; strongest compliance posture of the three
Skill match Standard SQL Server skills; no PySpark needed for operational layer

Anti-patterns

  • Using SQL Database as a data warehouse for large-scale analytical aggregations
  • Skipping mirroring configuration and then querying operational tables directly for BI
  • Running Spark notebooks against SQL Database instead of letting mirroring push data to OneLake
  • Over-provisioning for analytics when the workload is purely operational

Quick Comparison

Capability Lakehouse Warehouse SQL Database
Engine Spark + SQL endpoint T-SQL (Synapse DW) Full SQL Server
DML Delta Lake via Spark T-SQL CRUD Full CRUD + triggers
Query Language PySpark, Spark SQL T-SQL T-SQL
Schema Schema-on-read or write Schema-on-write Schema-on-write
Direct Lake Native Via shortcuts Via mirroring
Best For Data engineering, ML BI analytics, T-SQL teams OLTP, operational apps
Concurrency Medium High Very High
CDC Delta change data feed N/A Native CDC