Why it matters
Raw lakes are flexible but messy. Warehouses are structured but can be rigid. The lakehouse tries to give you both.
8 min
Beginner
Databricks
Lakehouse Explained
A lakehouse aims to combine the low-cost flexibility of a data lake with the reliability and structure of a warehouse.
Real-world example
A company keeps raw files and curated analytics tables in the same platform instead of maintaining separate lake and warehouse estates.
Common mistake
Thinking lakehouse means no governance. The opposite is true: governance is part of making the lake usable.
Next move
Read: Workspace Basics
Quick sketch
Remember
A lakehouse aims to combine the low-cost flexibility of a data lake with the reliability and structure of a warehouse. Raw lakes are flexible but messy. Warehouses are structured but can be rigid. The lakehouse tries to give you both.
What to remember
- A lakehouse aims to combine the low-cost flexibility of a data lake with the reliability and structure of a warehouse.
- Raw lakes are flexible but messy. Warehouses are structured but can be rigid. The lakehouse tries to give you both.
- A company keeps raw files and curated analytics tables in the same platform instead of maintaining separate lake and warehouse estates.