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What is Data Lake

Definition

A data lake is a centralised repository that stores vast amounts of raw data in its native format, structured, semi-structured, or unstructured, allowing organisations to keep everything cheaply and apply structure only when the data is read for a specific use.

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Stores any data type at low cost without forcing a schema up front
Preserves raw data so future, unanticipated use cases can be served from it
Decouples cheap storage from compute, letting many engines query the same files
Supports data science and ML workloads that need access to granular raw data

Real World Example

A connected-car company lands raw sensor logs, images, and JSON event streams into a data lake, where data scientists later apply schema-on-read to train models without having predefined every field at ingestion.

FAQs

What can a data lake store?

It stores structured, semi-structured, and unstructured data, from database extracts to logs, images, and free text, in raw form.

What is the risk of a data lake?

Without governance and cataloging a lake can become a 'data swamp' where data is hard to find, trust, or use.

How does a lake differ from a warehouse?

A lake stores raw data with schema applied on read, while a warehouse stores curated, structured data with schema enforced on write.