Glossary

Data lakehouse

A storage architecture that adds warehouse-style structure and transactions directly on top of low-cost data lake storage.

Also called: lakehouse

A data lakehouse combines the low-cost, flexible storage of a data lake with the structure, transactions and query performance historically associated with a data warehouse. It stores data in open file formats on object storage, but adds a metadata and table layer that provides schema enforcement, ACID transactions and versioning on top of that storage.

The practical difference from a plain lake is that a lakehouse lets teams run reliable, warehouse-grade SQL and OLAP queries directly against the same data used for machine learning or exploratory work, removing the need to maintain separate lake and warehouse copies with duplicate ETL pipelines between them. Compared with a traditional warehouse, storage and compute are decoupled and typically cheaper, and raw or semi-structured data can live alongside modeled tables.

Lakehouses matter for organizations that want to consolidate infrastructure and avoid copying data between a lake and a warehouse for different workloads. Pitfalls include underestimating the engineering effort needed to match a mature warehouse's performance and governance, and adopting the architecture without also investing in data modeling and data quality practices, which the storage format alone does not provide.

Last reviewed September 19, 2026

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Related terms

Data warehouse A centralized, structured database optimized for fast historical reporting rather than day-to-day transactions. Data lake A storage repository that holds raw data of any format cheaply, with structure applied only when the data is read. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. ELT (extract, load, transform) A data integration pattern that loads raw data first and transforms it afterward using the warehouse's own compute. Data modeling The practice of organizing data into entities, relationships and schemas that make it accurate and efficient to query. OLAP (online analytical processing) A category of technology optimized for fast, multi-dimensional aggregation and slicing of data for analysis.

Related tools

Apache Doris Open-source MPP database combining real-time analytics with sub-second query response at large scale. Apache Hudi Open-source table format optimized for high-volume upserts, deletes, and incremental data-lake pipelines. Apache Iceberg Open-source table format that adds warehouse-style transactions, schema evolution, and time travel to data-lake files. Apache Paimon Open-source lake table format built for unified streaming and batch updates, originating from the Apache Flink community. Cloudera Data Platform Enterprise data platform unifying data engineering, warehousing, ML, and streaming across hybrid and multi-cloud environments. Databricks Feature Store Feature registry and serving layer built into Databricks Unity Catalog, using Delta tables as feature tables.

Related guides

How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a cloud data warehouse Start from your cloud, your pricing tolerance and your workload shape — general BI, high-concurrency apps, or enterprise mixed workloads — not the feature list. How to choose a data marketplace The marketplace that matters is the one your data lands in already — cloud-native listings versus a platform-agnostic directory of external providers. How to choose a lakehouse platform Choosing a lakehouse means two decisions, not one — which open table format your data sits in, and which platform manages and queries it.