Data Lakes and Lakehouses

Data lakes store large volumes of raw data in its original format for later processing, while lakehouses add a structured metadata and transaction layer on top so the same storage supports both analytics queries and machine learning workloads with warehouse-like reliability.

itData engineering and analytics

Don't Panic: Data Lakes and Lakehouses

A data lake is where varied data can wait before everyone agrees what it is for. It keeps events, tables, documents, images, and other stubbornly different things in shared storage. This is useful because forcing every source into one database before asking a question tends to produce either delay or a collection of heroic spreadsheets. Neither is a durable architecture.

A lakehouse adds the missing clerk behind the filing cabinets. The files remain in lake storage, but a table-management layer records which files form a table now. The memorable part is files below, tables above. Storage keeps objects. A file format, such as Parquet, arranges values inside one object. A table format records the valid state across many objects. A catalog helps people and engines find it. Compute reads and writes it. Five layers, because one layer was apparently too restful.

The surprise is that a directory full of Parquet files is not a transactional table. A snapshot is one committed table state, connected through manifest metadata to its data files. Writers prepare a change, then publish metadata atomically. Readers select a committed snapshot rather than trying to infer reality from a folder that is changing beneath them. That also makes retained historical states useful for investigation and rollback, although history remains only as long as retention policy preserves it.

This arrangement earns its keep when SQL, streaming, notebooks, machine learning, and business intelligence need governed analytical tables over shared storage. It does not repair inaccurate source events, decide business meaning, make every engine compatible, or appoint an owner. A lake can collect data with remarkable enthusiasm. Trust arrives later, through ownership, access control, quality checks, retention rules, and maintenance.

Read the Intro when the layer model and the lake-versus-warehouse decision need the full map. Use Slides for the relationships: files, metadata, catalogs, and engines. Keep the Cheatsheet nearby when a snapshot, manifest, partition change, or small-file problem appears in a design review. The useful next move is modest: follow one representative dataset through the layers and name the source of truth at each point. The architecture becomes less mysterious once every responsibility has an address.

Don't Panic: Data Lakes and Lakehouses

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