Warehouse or lakehouse: choose the constraint, not the label
The useful choice is about file types, freshness, and who will operate the platform — not about winning an architecture argument.
“Lakehouse” is often used as a synonym for modern. A warehouse with tested models and a small set of certified tables will beat a lake of files with no owners. The label matters less than the jobs: landing raw data, transforming it, and serving it.
When a warehouse is enough
If your sources are relational, your consumers are BI tools, and a daily or hourly refresh is acceptable, a modeled warehouse is a strong default. You get governance and performance without teaching the whole company a new file format.
When open table formats help
If you must land diverse files, share the same data with multiple engines, or keep a cheap raw history that several teams process differently, a lake with a table format and a curated layer can be the right shape. The curated layer is not optional. Without it you have cheap storage and expensive arguments.
- Do not copy a vendor reference architecture that assumes a team you do not have.
- Streaming belongs where latency is a requirement, not where a demo looks better.
- Cost will follow storage layout and how often you rewrite partitions. Measure that early.
Common questions
What is the difference between a data warehouse and a lakehouse?
A data warehouse stores structured, modelled tables optimised for SQL analytics and BI. A lakehouse stores data as files in object storage using open table formats that add warehouse-like transactions and schema management, so BI, data science, and large-scale processing can share one copy of the data.
Do I need a lakehouse?
Not necessarily. If your data is mostly relational and your main consumers are analysts and dashboards, a well-modelled warehouse is usually simpler to run. A lakehouse earns its complexity when you handle large or semi-structured files, multiple processing engines, or machine learning workloads alongside BI.
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