A data lakehouse combines the reliability of a data warehouse with the scale of a data lake by using a single object storage layer, open table formats (Apache Iceberg, Delta Lake, Apache Hudi), a shared catalog, and a governance layer. Open table formats enforce ACID-like guarantees on raw files, while a shared catalog lets multiple engines (Spark, Trino) read consistent data. The architecture eliminates costly data duplication across systems but introduces platform engineering responsibilities like compacting small files and managing schema changes carefully. The post concludes with guidance on when to choose a warehouse, lake, or lakehouse based on team size and workload needs.
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