Data Lakehouse vs Data Warehouse
One open platform for BI, ML, and streaming versus a mature, SQL-first analytics engine — how to choose the foundation for your data platform.
The short answer
Choose a data warehouse (Snowflake, BigQuery, Redshift) when your workloads are BI and SQL analytics on mostly structured data and you value simplicity, governance, and a small operational footprint. Choose a lakehouse (Databricks and open table formats like Delta or Iceberg) when you need machine learning, streaming, and BI on one copy of large or semi-structured data, want open formats to limit lock-in, and have the engineering to run it. In practice the two are converging — warehouses are adding lake features and lakehouses are getting easier — so decide on your dominant workload and your appetite for openness versus simplicity.
Data Warehouse vs Data Lakehouse, at a glance
Tap any row for the detail behind the summary.
When to choose each
Choose Data Warehouse if…
Mature, SQL-first analytics (Snowflake, BigQuery)
- Your primary workload is BI and SQL analytics on structured data.
- You want the simplest path with strong governance and minimal platform engineering.
- Your team is SQL-fluent and you value ease of use over maximum flexibility.
- Fast, reliable dashboards matter more than ML/streaming on the same platform.
Choose Data Lakehouse if…
Unified BI + ML + streaming on open formats (Databricks)
- You need machine learning and BI on the same data without copying it around.
- You have very large or semi-structured/unstructured data (logs, events, media).
- You want open table formats (Delta, Iceberg) to avoid proprietary lock-in.
- Streaming and batch on one platform matters, and you have data engineers to run it.
The convergence: they're borrowing each other's strengths
Treating this as a binary is increasingly misleading. Warehouses have added lake capabilities — Snowflake reads and writes Iceberg tables; BigQuery queries open formats — while lakehouses have gotten far easier to run and now offer strong SQL/BI performance. The gap that used to force a hard choice is closing.
That means the sharpest question isn't 'which architecture is objectively better,' but 'which one fits my dominant workload and my tolerance for engineering.' A BI-heavy team with little platform engineering will be happier on a warehouse; a team doing serious ML and streaming on huge data will get more from a lakehouse.
Open table formats change the lock-in math
The rise of Delta Lake and Apache Iceberg is the most important shift in this debate. When your data lives in an open format on your own object storage, the engine becomes a swappable choice rather than a permanent commitment. You can run Databricks today and query the same tables with Trino or Snowflake tomorrow.
If avoiding lock-in is a strategic priority, that alone can tip the decision toward a lakehouse — or toward a warehouse that natively supports Iceberg. Either way, ask any vendor how your data can be read by engines you don't buy from them.
Frequently asked questions
Is a lakehouse replacing the data warehouse?
Not wholesale. Lakehouses are taking share for ML- and streaming-heavy, large-scale workloads, while warehouses remain excellent for BI and SQL analytics. Many organizations run both, or use a warehouse that has adopted lakehouse features like open table formats.
Is Snowflake a warehouse and Databricks a lakehouse?
Roughly, historically — Snowflake started as a cloud warehouse and Databricks pioneered the lakehouse. But they've converged: Snowflake now supports open Iceberg tables and ML, and Databricks offers strong SQL/BI. Evaluate them on your workload, not the label.
Do I need both a lakehouse and a warehouse?
Sometimes. A common pattern is a lakehouse for large-scale/ML/streaming data and a warehouse (or the lakehouse's SQL layer) for polished BI. With open table formats, you can increasingly serve both from one copy of the data.
Is a lakehouse cheaper than a warehouse?
It can be at large scale, because storage is commodity object storage and you control compute. But warehouses are cheaper to operate for smaller teams once you account for the engineering needed to run a lakehouse well.
Can I run BI and dashboards on a lakehouse?
Yes. Modern lakehouse SQL engines deliver strong BI performance, and BI tools connect directly. For very high dashboard concurrency you may add caching or a serving layer that a warehouse would handle out of the box.
Choosing your data platform foundation?
Tell us your workloads (BI, ML, streaming) and scale — we'll send a short, vendor-neutral recommendation on lakehouse vs warehouse for your situation.