Data Warehouses Meet Data Lakes
summary
Argues that modern data architecture merges warehouses and lakes by using table formats and transaction layers so teams can store everything and still run analytics; demonstrates use of Apache Iceberg–style features for delete/update and versioning, describes a live price-collection project that streams into a real-time subset of a lake, reports running production clusters of 5–6 nodes, contrasts batch efficiency with per-second updates and explains how warnings, errors and dynamic config are handled.
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chapters (6)
- 0:00Introduction and context
- 10:02Data warehouse fundamentals
- 14:13Data lakes and storage approach
- 22:19Technologies in data lakes
- 26:53Real-time price collection project
- 28:41Error handling and implementation
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