Modern data platforms are evolving fast, and Microsoft Fabric introduces two powerful storage/analytics experiences: Lakehouse and Warehouse.
Both sit on top of One Lake, but they serve different personas and use cases.
Let’s break it down in a practical, real-world way
A Lakehouse combines the flexibility of a data lake with the structure of a warehouse.
Key Characteristics
- Built on Delta Lake (Parquet files)
- Uses Spark / Notebooks / PySpark / SQL
- Supports structured + semi-structured + unstructured data
- Ideal for data engineering & data science
What you typically do here
- Ingest raw data from multiple sources
- Transform using PySpark / Spark SQL
- Build Bronze → Silver → Gold layers
- Store large-scale historical data
Example (your kind of work)
- Ingest logistics data → Ocean + Land shipments
- Clean + join datasets in Spark
- Calculate emissions using PySpark
- Store curated tables for reporting
A Warehouse is a fully managed SQL-based analytical engine.
Key Characteristics
- Uses T-SQL (like Azure Synapse / SQL Server)
- Structured data only
- Optimized for BI & reporting
- Supports star schema (Fact + Dimension)
What you typically do here
- Create Fact tables (Fact Land Shipments, Fact Ocean Shipments)
- Create Dimensions (Dim Calendar)
- Build views / stored procedures.
- Connect directly to Power BI
Example (your use case)
- Build Fact Land Shipments, Fact Ocean Shipments
- Create KPI measures like:
- YTD Sales
- R12M Sales
- Profit %
- Feed Power BI dashboards.
Lakehouse vs Warehouse — Side-by-Side
Feature | Lakehouse | Warehouse |
Engine | Spark | SQL (T-SQL) |
Data Type | All (structured + unstructured) | Structured only |
Users | Data Engineers, Data Scientists | BI Developers, Analysts |
Transformations | PySpark, Notebooks | SQL |
Performance | Big data scale | Optimized for reporting |
Schema | Flexible | Strict (Star Schema) |
Best For | ETL, ML, raw + curated data | Reporting, dashboards |
When to Use Lakehouse?
Use Lakehouse when:
You are:
- Working with Databricks-like workloads
- Handling huge datasets (millions/billions of rows)
- Building ETL pipelines
- Using PySpark / Delta tables
- Creating medallion architecture
Example
You are:
- Loading SAP + logistics data
- Cleaning + joining in Spark
- Computing emissions
- Storing Delta tables
This is a Lakehouse job
When to Use Warehouse?
Use Warehouse when:
You are:
- Building Power BI models
- Creating Fact + Dimension tables
- Writing DAX / SQL-based transformations
- Optimizing for report performance
Example
You are:
- Creating Fact Sales
- Joining with Dim Calendar,
- Building KPIs like:
- YTD Sales
- R12M Sales
- Publishing to Power BI
This is a Warehouse job
Best Practice (Very Important)
Use BOTH together (Hybrid Architecture)
Recommended Flow
Step-by-step
- Lakehouse
- Ingest raw data
- Transform using Spark
- Create Gold tables
- Warehouse
- Load curated data
- Create a star schema
- Optimize for BI
- Power BI
- Build a semantic model
- Create DAX measures
- Build dashboards
Common Mistakes
Using Lakehouse for reporting
→ Slow visuals, poor performance
Using Warehouse for heavy ETL
→ Not scalable
Mixing logic randomly
→ Hard to maintain
Simple Rule to Remember
Lakehouse = Data Engineering
Warehouse = Data Modelling & BI
Real-Life Mapping (Your Scenario)
Task | Best Choice |
Databricks-style transformations | Lakehouse |
CO₂ calculations using Spark | Lakehouse |
Fact tables for Power BI | Warehouse |
KPI measures (DAX) | Warehouse |
Sustainability dashboard | Warehouse |
Final Thoughts
In Microsoft Fabric:
- Lakehouse gives you power & flexibility.
- Warehouse gives you speed & structure.
The winning strategy is not choosing one —
It’s using both correctly together
