Managed data
What is Data as a Service (DaaS) and why it matters
A clear definition of Data as a Service (DaaS): what it includes, how it differs from building your own infrastructure and why more companies adopt it.
Read articleKey takeaways
- A data warehouse stores structured, modelled data for reporting; a data lake holds raw data of any type.
- The warehouse prioritises consistency and query performance; the lake, flexibility and volume.
- The lakehouse approach combines both in one platform.
- The choice depends on use cases, not on hype.
- Most mid-to-large companies end up needing both.
When a company decides to "get its data in order", the same question appears: warehouse or lake? Although often presented as rivals, they answer different needs and increasingly coexist in the same organisation.
The difference
A data warehouse is a store of structured, clean, modelled data, optimised for analytical queries and reporting. A data lake is a repository that holds raw data of any type — structured, semi-structured and unstructured — without imposing a prior schema.
Key differences
| Aspect | Data warehouse | Data lake |
|---|---|---|
| Schema | On write (modelled first) | On read |
| Data types | Tabular | Text, logs, JSON, Parquet |
| Performance | Fast, consistent | Capacity, flexibility |
| Cost | Compute-heavy | Cheap storage |
| Best for | BI, finance | Data science, AI |
When each suits
A warehouse is the natural choice for financial reporting and metrics with stable definitions, where consistency is non-negotiable. A lake shines with large volumes, heterogeneous data and exploratory or AI use cases that do not fit a rigid model.
Warehouse
ModelledReporting
Lake
RawAI, exploration
In practice
Often bothcombined
The lakehouse approach
The "lakehouse" describes architectures that unite the flexibility and cost of the lake with the reliability and performance of the warehouse, built on open formats with ACID transactions, reducing data duplication between systems.
It is not warehouse versus lake: most organisations need both, and the lakehouse unites them.
In summary
A data warehouse stores modelled data for consistent reporting; a data lake holds raw data of any type for flexibility and AI. They answer different needs and usually coexist, and the lakehouse unites both on one platform. Choose by use case, not by label.
Sources & further reading
Frequently asked questions
Does a data lake replace a data warehouse?
Not necessarily. They solve different needs and usually complement each other: the lake as flexible storage, the warehouse as a consistent consumption layer.
What is a lakehouse?
An architecture combining the lake’s flexibility and cost with the warehouse’s reliability and performance, usually on open formats with ACID transactions.
Which is cheaper?
Lake storage is usually cheaper, but real cost depends on analytical compute and governance. Compare by total cost of ownership.
When should I use a warehouse?
For financial reporting and metrics with stable definitions, where consistency is non-negotiable.
When does a data lake shine?
With large volumes, heterogeneous data and exploratory or AI use cases that do not fit a rigid model.
Do I have to choose just one?
No. Most mid-to-large organisations need both, and a managed service or lakehouse combines them without you deciding the technology.
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