Three architectures, one question: where should your company's data live so reporting and AI can trust it?
Every data platform answers the same question: where does data land, how is it cleaned, and how do people use it? The three common answers differ mainly in structure and flexibility.
In short
- Data warehouse: structured, modelled data for reporting. Fast and reliable for BI.
- Data lake: raw data of every type, cheap to store, harder to govern.
- Lakehouse: lake storage with warehouse-style structure and governance on top.
Which one is right?
Most mid-sized businesses start well with a warehouse focused on their key reports. Organisations with large volumes, varied data or machine learning plans benefit from a lakehouse. What matters most is the layer above any of them: agreed definitions, quality checks and ownership. That governed layer is your data backbone, and it is what makes AI trustworthy.