Glossary
What is data warehouse?
A data warehouse is a central database in which data from different source systems is brought together, cleaned and organised for reporting and analysis. It keeps history and applies one set of definitions.
Data warehouse explained
A source system such as an ERP or CRM is built to process transactions quickly, not to analyse across years. A data warehouse collects that data, puts it side by side and organises it for questions like "what was the margin per customer per quarter". That way you do not burden the source system and you can combine data from different systems.
Data usually arrives through an ETL or ELT process and is modelled, often as a star schema with facts and dimensions. Because the warehouse keeps history, you can still look back when a source system overwrites old records.
The difference from a data lake: a warehouse holds structured, cleaned tables with fixed definitions, while a lake stores raw data in any format. For reporting and AI questions about figures, a warehouse is usually the more reliable foundation.
Example
Every night, Acme Groothandel loads orders from its ERP, customer data from its CRM and budgets from Excel into one data warehouse. The question "revenue against budget per account manager" can then be answered with a single query, without stitching exports together.
Data warehouse in ENABLE
Every ENABLE customer has its own PostgreSQL data warehouse. Sources such as Microsoft Dynamics 365 Business Central are loaded outside the portal, for example with Airbyte, and then used in the SQL Explorer, the Data API and the Dashboard Builder (pilot).
FAQ
Frequently asked questions
What is the difference between a data warehouse and a data lake?
A data warehouse holds structured, cleaned tables with fixed definitions, organised for reporting. A data lake stores large volumes of raw data in any format, and you only define the structure when you read it.
What is the difference between a data warehouse and a database?
A data warehouse is a database, but organised for analysis rather than for processing transactions. It combines data from several sources and keeps history.
Do you need a data warehouse for Power BI?
Not necessarily, as Power BI can connect directly to sources. Once you combine several sources, want to keep history or use the same definitions across reports and tools, a data warehouse is strongly recommended.
Further reading
- ResourcesWhat is a data warehouse? Explained and comparedA data warehouse is a central database in which you bring together data from different source systems, such as your ERP, CRM and webshop, clean it up and organise it so that you can report on it and analyse it effectively. A source system is built to process transactions; a data warehouse is built for reading, comparing and keeping history. A data lake stores raw data in any format, and a data lakehouse combines the storage of a lake with the structure of a warehouse.Read more
- SolutionSingle source of truth: one version of the truthOne central source for your business data, so every department sees the same figures.Read more
- SolutionBuild a data platform: layers, choices and approachFrom sources to dashboards and AI: how a data platform is structured.Read more
- SolutionData integration: connect data sources for insightConnect data sources for reporting and AI, via one central data warehouse.Read more
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