Snowflake, BigQuery, Databricks. Where structured analysis happens — and where the lakehouse merges both worlds.
Category · Data & Analytics
Warehouse, lake, lakehouse.
A data warehouse is a central database for structured analysis — optimised for fast, repeatable queries. Snowflake and BigQuery are the typical examples. A data lake, by contrast, stores everything raw, including unstructured data, with no fixed schema.
The lakehouse, made prominent by Databricks, brings the two worlds together: cheap raw storage like the lake, plus table structure and transactions like the warehouse.
Where it sits with us.
We set up the warehouse as the central source of truth for reporting and analytics: one place where the numbers are consistent, instead of five Excel versions with five truths. BigQuery when you're in the Google world anyway; Snowflake when multi-cloud matters; Databricks when ML and data engineering come into play.
When it's over-engineered.
A warehouse pays off above a certain data volume and variety of sources. With three tables and 50,000 rows, a Postgres database is faster, cheaper and more honest. Lakehouse complexity only pays off when unstructured data and analytics genuinely have to live in the same system.