What is Data Lakehouse?
A data lakehouse is a data platform that combines the cheap, open storage of a data lake with the management, performance, and SQL capabilities of a data warehouse — so you can run BI, machine learning, and streaming on one copy of the data.
At a glance
- Combines a data lake (cheap object storage) with warehouse features (SQL, reliability, governance).
- Uses open table formats — Delta Lake or Apache Iceberg — on your own storage.
- One copy of the data serves BI, machine learning, and streaming.
- Reduces lock-in: many engines can read the same open tables.
Why it exists
Historically you had two separate systems: a data lake (cheap, flexible object storage, great for ML and raw data, but messy and hard to query reliably) and a data warehouse (fast, governed SQL analytics, but proprietary and pricier). Teams copied data between them and maintained both.
The lakehouse collapses that split. By adding a transactional table layer — open formats like Delta Lake or Apache Iceberg — on top of ordinary object storage, it gives lake data warehouse-grade reliability, ACID transactions, and fast SQL, without moving it into a proprietary engine.
What you get
One platform where analysts run SQL/BI, data scientists train models, and pipelines process streams — all against the same tables, with one governance model and no constant copying. Because the tables are in open formats on storage you control, you can query them with multiple engines (Databricks, Spark, Trino, and increasingly Snowflake), which limits vendor lock-in.
A lakehouse is the natural home for a medallion architecture, where raw, cleaned, and business-ready data live as Bronze/Silver/Gold tables.
Frequently asked questions
What's the difference between a data lake and a data lakehouse?
A data lake is raw object storage — cheap and flexible but hard to query reliably. A lakehouse adds a transactional table layer (Delta Lake or Iceberg) on top, giving that same storage warehouse-grade SQL, reliability, and governance.
Is a lakehouse better than a data warehouse?
Neither is universally better. Lakehouses excel at ML, streaming, and large or semi-structured data on open formats; warehouses excel at simple, governed BI. Many teams choose based on their dominant workload — see our lakehouse vs warehouse comparison.
What are open table formats?
Open table formats like Delta Lake and Apache Iceberg store data in an open, non-proprietary way on your object storage, so multiple engines can read and write the same tables — the key technology behind the lakehouse and a hedge against lock-in.
Do I still need a data warehouse if I have a lakehouse?
Often not as a separate system — modern lakehouses provide strong SQL/BI. Some teams still keep a warehouse (or the lakehouse's SQL layer) for polished, high-concurrency dashboards.
Putting Data Lakehouse to work?
We help data & AI teams design and ship this in production. Tell us what you're building and we'll point you at the shortest path.
Talk to our team