Which data store is designed to hold both raw and curated data for analytics?

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The idea being tested is the ability of a data store to hold data in both its raw form and in a prepared, analytics-ready state. A data lake is built to ingest and retain large volumes of raw data in its native formats—structured, semi-structured, and unstructured—from many sources. It also supports transforming and curating data as needed for analysis, often using a schema-on-read approach, which means the structure is defined when you query or process the data rather than when it’s stored. This combination—storing raw data for flexibility and enabling curated datasets for analytics—fits data lakes best.

Data warehouses or data marts, in contrast, store structured, cleaned, and transformed data optimized for fast reporting, with less emphasis on keeping raw sources. A data silo is an isolated repository that typically serves a single department and doesn’t integrate well with other data. A generic data store doesn’t specify this dual capability. So the data lake best matches the requirement of holding both raw and curated data for analytics.