Definition: A data lakehouse is a data architecture that combines low-cost data lake storage with warehouse features such as ACID transactions, schemas and fast SQL, so analytics and machine learning run on the same data.
Data sits in open file formats such as Parquet on cloud object storage. An open table format, such as Delta Lake, Apache Iceberg or Apache Hudi, adds transactions, versioning and schema enforcement. Engines and platforms such as Databricks, Snowflake, Spark and Trino can query the same tables.
A data warehouse is optimized for structured BI queries; a data lake stores raw files cheaply but is harder to govern. The lakehouse aims to keep one copy of data for BI, data science and AI, reducing duplicate pipelines.
Teams commonly organize tables in layers, such as raw, cleaned and business-ready, add a catalog for access control and lineage, and feed feature stores and RAG indexes from the curated layers.
Ryz Databricks engineers and data engineers build lakehouse platforms in your cloud, working on your team.
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