Lakehouse vs Warehouse

There are real-world scenarios where one is clearly a better fit.

I’d keep it simple:

Use a Lakehouse when the data is still raw, messy, changing, or coming from different formats. For example CSV, JSON, logs, APIs, files, or data that needs Spark/notebook processing. It fits well for ingestion, cleaning, bronze/silver layers, data engineering, and data science.

Use a Warehouse when the data is already clean, structured, and ready for analytics. For example curated sales, finance, customer, or operational reporting tables. It fits well for T-SQL, star schemas, dimensional models, and Power BI reporting. You have created good data products in your own on-premise data warehouse and you import into Fabric Warehouse and so some last mile transformations.

A common real-world pattern is to use both:

Lakehouse = land and prepare the data
Warehouse = serve clean, structured data for BI

Example:
API data, raw files → Lakehouse
Curated finance/sales reporting model → Warehouse

Microsoft’s decision guide says something similar: choose Lakehouse for Spark, mixed or unknown data types, and flexible data engineering; choose Warehouse for T-SQL, structured data, and multi-table transactional warehouse needs.  https://learn.microsoft.com/en-us/fabric/fundamentals/decision-guide-lakehouse-warehouse 

Best regards,

Parchitect - Solutions Architect

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