Data Lakehouse: Who, Why and How
summary
Explains why merging lakes and warehouses fixes silos and lowers cost, naming S3 + Parquet for storage and Delta Lake and Apache Hudi/Iceberg as the transactional table formats that enable single-copy BI, ML and streaming. Gives a concrete consolidation example: 25 data lakes and 100 warehouses unified for a global retailer. Advises start small with file-format standardization, governance (catalog/lineage) early, and expect visible business value in one to three months if a clear use case exists.
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chapters (14)
- 0:00Introduction
- 1:11Data is the new gold
- 2:20Data management issues
- 7:24What is a lakehouse
- 10:14Lakehouse fixes lake problems
- 13:05Lakehouse evolution
- 16:19Lakehouse features
- 18:50Lakehouse ecosystem
- 22:06Lakehouse adoption best practices
- 23:11Consider a lakehouse?
- 24:38Lakehouse use cases
- 27:39Open standards vs vendor lock-in
- 28:46AI and human decisions
- 30:10Summary
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