What is Data warehouse vs data lake?
A warehouse stores cleaned, structured data for reporting. A lake stores raw data of any kind cheaply.
In plain English
A warehouse expects a defined structure (schema) before data goes in, which makes reporting fast and consistent. A lake keeps raw files, structured or not, and applies structure when the data is read. Many organisations use both, and the newer “lakehouse” idea tries to combine them.
Example
Finance reports run on warehouse tables. Data scientists explore raw logs and images in the lake.
Easy to confuse with
- ETL vs ELT: These are places data is stored. ETL and ELT are ways data gets there.
Where you will meet it
This idea belongs to the subject area of these practice tests. We do not publish or predict real exam questions.
- SnowPro Associate practice tests
- Google Cloud Associate Data Practitioner practice tests
- CompTIA Data+ practice tests
Related terms
- ETL vs ELT: ETL transforms data before loading it. ELT loads it first and transforms it inside the destination.
- Data governance: The rules and responsibilities for how an organisation manages, protects and uses its data.
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