Teams choosing a data platform today usually end up comparing two architectures: a data warehouse and a lakehouse. Both let you run SQL on large amounts of data, but they make different trade-offs on storage, openness, cost and the kinds of workloads they serve well.
This post explains how each works, where they really differ, and how to decide.
What a data warehouse is
A data warehouse is a database built for analytics. Data is loaded into it, stored in the warehouse's own optimized format, and queried with SQL by the warehouse's own engine.
- Integrated: storage, metadata, query engine and security come as one product.
- Strong SQL performance: the engine controls the storage layout, statistics and caching, so BI queries are fast with little tuning.
- Mature governance: users, roles, row and column security, and auditing are built in.
- Structured data first: it works best with tables that have a clear schema.
The trade-off is that your data lives inside one system. Other engines, such as Spark jobs or ML pipelines, usually have to export or copy it.
What a lakehouse is
A lakehouse keeps data as open files, typically Parquet, in object storage such as S3 or MinIO. An open table format such as Apache Iceberg, Delta Lake or Apache Hudi adds a metadata layer on top, so those files behave like reliable database tables.
That table format is what turns a data lake into a lakehouse. It provides:
- ACID transactions: writers commit atomically and readers never see half-written data.
- Schema evolution: add, rename or drop columns without rewriting the table.
- Time travel: query a table as it was at an earlier snapshot, for audits, debugging or reproducible ML.
- Partition and file pruning: metadata lets engines skip files that cannot match a query.
Because the format is open, several engines can work on the same tables: Spark or Flink for writing and processing, Trino for interactive SQL, and Python tools for data science, all without copying data.
-- Iceberg time travel in Trino
SELECT count(*) FROM lake.events
FOR TIMESTAMP AS OF TIMESTAMP '2026-10-01 00:00:00 UTC';The real differences
Storage and openness. A warehouse stores data in a proprietary format. A lakehouse stores it in open files you own, which avoids lock-in and lets you change engines later.
Compute. A warehouse bundles its engine. A lakehouse separates storage from compute, so you choose and scale engines independently and can run several against the same data.
Workloads. Warehouses excel at BI dashboards and SQL reporting. Lakehouses handle those plus large-scale data processing, streaming ingestion and machine learning on the same copy of the data, including semi-structured data.
Operations. A warehouse is mostly managed for you. A lakehouse needs more engineering: catalog setup, compaction of small files, snapshot expiry, and choosing and running engines. Managed catalogs and services reduce this, but it does not disappear.
Cost profile. Object storage is inexpensive and a lakehouse lets you pay for compute only when engines run. A warehouse can be simpler to budget for small teams, but costs can rise as data and query volume grow. The right answer depends on your usage pattern, so model it with your own numbers.
Governance. Warehouses have the most complete built-in controls. In a lakehouse, governance is enforced through the catalog and each engine, and needs deliberate design so every engine respects the same rules.
When to choose each
Choose a data warehouse when:
- Your main need is BI and SQL reporting on structured data.
- You have a small data team and want minimal infrastructure to run.
- Built-in governance and predictable performance matter more than flexibility.
Choose a lakehouse when:
- Several engines or teams (analytics, streaming, ML) need the same data.
- Data volume is large or growing fast and you want storage and compute to scale separately.
- You need streaming ingestion, schema evolution and time travel for audit or reproducibility.
- Avoiding vendor lock-in and owning your data in open formats is a priority.
Many organisations use both: a lakehouse as the central store for raw and processed data, with a warehouse or a serving layer for the most demanding dashboards. Several warehouses can now also read open table formats such as Iceberg, which narrows the gap.
Takeaway
A data warehouse gives you the simplest path to fast, governed SQL analytics. A lakehouse gives you open storage, multiple engines and support for streaming and ML on one copy of the data, at the price of more engineering. Pick based on your workloads, team size and how much flexibility you will need in two years, not on which term is trending.