Quick Summary
Data warehouses consolidate data from various sources for business intelligence, analytics, and decision-making.
A Data Warehouse is a centralized data repository that integrates data from multiple disparate sources to support query, analysis, and reporting. It is designed for analytical processing rather than transaction processing.
Data Warehouse vs Database
| Aspect | Database | Data Warehouse |
|---|---|---|
| Purpose | Transaction processing | Analytics and reporting |
| Data Structure | Normalized | Denormalized (optimized for reads) |
| Data Volume | GB to TB | TB to PB |
| Query Type | Simple, frequent | Complex, aggregations |
Data Warehouse Architecture
- ETL Process: Extract, Transform, Load
- Staging Area: Temporary data storage
- Integration Layer: Data cleansing and transformation
- Storage Layer: Organized data storage
- Presentation Layer: Tools for querying and reporting
Popular Data Warehouse Solutions
| Solution | Provider | Type |
|---|---|---|
| Snowflake | Snowflake | Cloud-native |
| BigQuery | Serverless | |
| Redshift | AWS | Petabyte-scale |
| Azure Synapse | Microsoft | Analytics platform |
Data Warehouse Use Cases
- Business intelligence and reporting
- Historical data analysis
- Data mining and machine learning
- Regulatory compliance reporting
- Sales and marketing analytics
Modern Data Architecture
- Data Lake: Raw data storage (structured and unstructured)
- Data Lakehouse: Combines data lake and warehouse benefits
- Cloud Data Warehouse: Scalable, managed solutions
Key Points
- Centralized data repository
- For analytics not transactions
- ETL process for data loading
- Optimized for complex queries
- Snowflake, BigQuery popular
- Supports business intelligence