Data Lake

A data lake is a centralized store, often built on cloud object storage, that keeps data as-is: database exports, logs, JSON, images, and documents. Structure is applied when the data is read, an approach called schema-on-read.

Why it matters

Lakes are cheap and flexible, letting teams keep everything now and decide how to analyze it later. They suit machine learning and exploration. Without governance, however, a lake can turn into a "data swamp" nobody trusts or can find anything in.

Example

A folder layout for raw web logs:

s3://company-lake/raw/web-logs/2026/10/06/part-0001.json.gz
s3://company-lake/raw/crm/2026/10/06/contacts.csv

Query engines can read these files directly, or a pipeline can load cleaned subsets into a data warehouse.

Common mistakes

• Dumping data with no catalog, ownership, or metadata.
• Storing sensitive personal data without access controls or retention rules.
• Treating it as a replacement for a warehouse when users need fast, consistent reports.

Related

• Data warehouse
• Big data
• Structured vs unstructured data
• Data pipeline

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