Data & Databases
Data lakehouse
An architecture combining features of data lakes and data warehouses.
Example
A lakehouse adds reliable tables and query features on top of data-lake storage.
Why it matters
It aims to make varied stored data useful for reliable analysis without maintaining entirely separate approaches.
You'll hear it
“Can this setup support both our raw files and reporting needs?”
Can you control it?
Set by the team
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Is every lakehouse built the same way?
- No. Products differ in storage, management and analysis features.
- Does the label guarantee good data quality?
- No. Preparation and ownership remain necessary.
- Can adding analytical features create extra operating work?
- Yes. More capabilities can bring maintenance, configuration and cost choices.