A data warehouse is a centralized repository that stores integrated data from multiple sources, optimized for analytical queries and reporting. Unlike operational databases designed for transactions (OLTP), data warehouses are built for analysis (OLAP).
Key Characteristics
1. Subject-Oriented: Organized around business subjects (customers, products, sales) rather than applications
2. Integrated: Data from disparate sources is cleansed, transformed, and unified into a consistent format
3. Time-Variant: Historical data is preserved, enabling trend analysis over time
4. Non-Volatile: Once data enters the warehouse, it's stable and doesn't change (unlike operational systems)
Data Warehouse Architecture
Modern data warehouses typically follow a layered architecture:
- Raw/Staging Layer: Stores data as-is from source systems
- Integration Layer: Cleaned and transformed data
- Presentation Layer: Business-ready datasets for reporting and analytics
- Semantic Layer: Business definitions and metrics
Cloud Data Warehouses
The industry has shifted from on-premise solutions to cloud-native platforms:
| Platform | Provider | Key Feature |
|----------|----------|-------------|
| Snowflake | Independent | Separate storage/compute |
| BigQuery | Google Cloud | Serverless, pay-per-query |
| Redshift | AWS | Tight AWS integration |
| Synapse | Azure | Unified analytics |
| Databricks | Independent | Lakehouse architecture |
Data Warehouse vs Data Lake
- Data Warehouse: Structured data, schema-on-write, optimized for BI
- Data Lake: All data types, schema-on-read, optimized for data science
- Data Lakehouse: Combines benefits of both (e.g., Databricks, Snowflake)
Benefits for Organizations
- Single Source of Truth: Unified view across business domains
- Historical Analysis: Track trends and patterns over time
- Performance: Optimized for complex analytical queries
- Governance: Centralized security and access control