Data Warehouses and Business Intelligence: An Overview
A data warehouse is a centralised repository that stores integrated data from multiple sources — operational databases, SaaS platforms, spreadsheets, and external data — in a format optimised for analysis. Combined with Business Intelligence (BI) tools, it enables self-service analytics and consistent reporting across your organisation.
The Modern Data Stack
The modern data stack follows an ELT (Extract, Load, Transform) pattern:
- Extract and Load: Data is extracted from source systems and loaded into the warehouse — using tools like Fivetran, Stitch, or Airbyte
- Transform: Raw data in the warehouse is transformed into clean, structured models using dbt (data build tool)
- Serve: Transformed data is queried by BI tools (Looker, Metabase, Tableau, Power BI) to build dashboards and reports
Popular Data Warehouse Platforms
- BigQuery (Google): Serverless, highly scalable, excellent for GA4 integration and ML capabilities
- Snowflake: Cloud-agnostic, strong performance, widely adopted enterprise standard
- Redshift (AWS): Tight AWS ecosystem integration, cost-effective for moderate data volumes
- Databricks: Unified analytics platform combining data warehouse and data lake capabilities
When You Need a Data Warehouse
A data warehouse becomes valuable when you need to: combine data from multiple sources, analyse large historical datasets, provide self-service analytics to non-technical stakeholders, or answer complex business questions that operational databases cannot efficiently support.