3 min lesson
Lakehouse fundamentals on Databricks
Walk through "Lakehouse fundamentals on Databricks" in order, then name the proof that tells you it worked.
Step 1 of 3
A lakehouse is cheap object-store storage that behaves like a warehouse. Once you can say exactly which warehouse guarantees Delta Lake adds on top of plain files, the rest of this module is mechanics.
Classic architectures forced a choice. A data lake gave you cheap storage and open formats but no transactions, so concurrent writers corrupted tables and readers saw half-written data. A warehouse gave you ACID and schemas but locked your data in a proprietary engine and charged warehouse prices for every byte. The lakehouse keeps the lake's storage and adds the warehouse's guarantees through a metadata layer.
On Databricks that layer is Delta Lake. The data still lands as Parquet files in an object store like S3, but every table carries a transaction log: an ordered set of JSON commit files in a _delta_log directory that records which Parquet files belong to the table at each version.
Interactive diagram. Step through it with the Next and Previous controls below, or Tab to a region to read its detail.
The commit entry, not the Parquet files, makes a write visible.
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Optional practice