A data lakehouse is a data architecture that stores data in open file formats on low-cost cloud object storage, and adds warehouse features on top — ACID transactions, schemas, governance and fast SQL — so data engineering, BI and machine learning work on one copy of the data.
Before lakehouses, organisations typically kept a data lake for raw files and machine learning, and a separate data warehouse for reporting, copying data between the two. A lakehouse removes that copy by adding a transactional table layer (such as Delta Lake, Apache Iceberg or Apache Hudi) directly over the lake.
Databricks popularised the term; today most major data platforms support lakehouse-style open table formats.
Read more about Data lakehouse
- Inside the Databricks Lakehouse: Delta Log, Liquid Clustering, Deletion Vectors and Unity CatalogHow Delta's transaction log and concurrency work, plus time travel, deletion vectors, liquid clustering, change data feed, Unity Catalog, Auto Loader and Lakeflow.
- Medallion Architecture on Databricks: A Practical GuideHow to design bronze, silver and gold layers on Databricks that stay reliable at scale — with patterns for ingestion, data quality, governance and cost.
- Choosing a Data Platform in India: Databricks, Snowflake or BigQueryHow to choose between Databricks, Snowflake, BigQuery and other cloud data platforms in India: workloads, cost, skills, data residency and DPDP Act considerations.
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