Data Warehouse
A centralised repository that stores integrated data from multiple sources for reporting and analysis. Optimised for query performance rather than transaction processing.
In-Depth Explanation
A data warehouse is a system designed for storing and analysing large volumes of structured data from various sources. Unlike operational databases, warehouses are optimised for complex queries and reporting.
Data warehouse characteristics:
- Subject-oriented: Organised around business subjects (customers, sales)
- Integrated: Consistent data from multiple sources
- Time-variant: Historical data preserved
- Non-volatile: Data stable once loaded
Warehouse architecture:
- ETL/ELT: Data ingestion pipelines
- Staging area: Landing zone for raw data
- Data marts: Subject-specific subsets
- Presentation layer: Query and reporting interface
Modern cloud warehouses:
- Snowflake, Databricks, BigQuery
- Google BigQuery, Amazon Redshift
- Separate storage and compute
- Near-infinite scalability
Business Context
Data warehouses are the foundation of business intelligence. They enable cross-functional reporting and analytics that operational systems can't support.
How Clever Ops Uses This
We implement cloud data warehouses for Australian businesses, enabling analytics, AI training data preparation, and unified reporting.
Example Use Case
"Consolidating sales from POS, inventory from ERP, and marketing from CRM into Snowflake for unified revenue and customer analytics."