What Is a Data Lakehouse?
A data lakehouse is a data architecture that combines the low-cost, flexible storage of a data lake with the performance, structure, and governance of a data warehouse.
A data lakehouse is a data architecture that combines the low-cost, flexible storage of a data lake with the performance, structure, and governance of a data warehouse — supporting both analytics and machine learning on one platform.
Why it matters
Historically, organizations ran a data lake (cheap, flexible, but messy) and a warehouse (structured, fast, but costly and rigid), duplicating data and effort. The lakehouse unifies them, which is especially valuable when you need both BI reporting and machine learning on structured and unstructured data — increasingly the default for AI-first organizations.
Common platforms
Databricks popularized the lakehouse; Snowflake and cloud-native stacks offer similar unified capabilities. The right choice depends on workloads, existing stack, and cost — see cloud architecture.
Related
Compare options in data warehouse vs. data lakehouse, and see data engineering.
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