Data Lake & Lakehouse
A lake is a central store of raw files in any format (Parquet, text, images, video) for data scientists that SQL can't serve. A lakehouse layers warehouse-style tables and transactions on top of lake files.
What is it?
Data scientists kept hitting walls in warehouses: feature engineering for ML needs custom code SQL expresses badly; text/photos need NLP/vision tooling. They prefer Pandas, R, Spark — so organizations build lakes on cheap object storage, no imposed schema (“sushi principle”: raw data is better — each consumer transforms it their own way). The lakehouse merges both estates: warehouse tables on open lake formats so on copy serves SQL and Python crowds.
What I learned
“Raw data is better” is a philosophy with trade-offs: flexibility for every consumer vs no guarantees for anyone. The lakehouse is the industry’s attempt to get both.
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