Data Lakehouse

A lakehouse is an architecture that puts data-warehouse features — ACID transactions, schema enforcement, fast SQL — directly on top of cheap object storage like S3, rather than in a proprietary warehouse. It aims to combine the low cost and flexibility of a data lake with the reliability and performance of a warehouse, so you don't maintain two separate systems. The enabling technology is open table formats — Apache Iceberg, Delta Lake, and Apache Hudi — which add a transaction log and metadata layer over columnar Parquet files. That layer brings atomic writes, time travel, and schema evolution to files that would otherwise be a messy "data swamp". For SaaS builders, the lakehouse pitch is avoiding lock-in and duplicate storage: your raw data and your analytics-ready tables live in the same open files, queryable by many engines (Spark, Trino, DuckDB, Snowflake, Databricks). Practical note: the promise is real but the tooling is younger than classic warehouses. If your data is modest, a managed warehouse is often simpler; reach for a lakehouse when scale, cost, or multi-engine access justify the extra moving parts.