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A Data Warehouse is a centralized repository that stores integrated data from multiple sources, optimized for analytical querying and reporting rather than day-to-day transaction processing. It enables organizations to make data-driven decisions by analyzing historical trends across the entire business.
| Feature | OLTP (Online Transaction Processing) | OLAP (Online Analytical Processing) |
|---|---|---|
| Purpose | Day-to-day operations | Business analysis and reporting |
| Operations | INSERT, UPDATE, DELETE | Complex SELECT with aggregations |
| Data | Current, real-time | Historical, aggregated |
| Schema | Normalized (3NF) | Denormalized (Star/Snowflake) |
| Users | Thousands (customers, clerks) | Few (analysts, managers) |
| Example | Banking transaction system | Quarterly sales trend analysis |
The process of moving data from operational databases into the warehouse.
A central Fact Table (containing measurable metrics like sales amount, quantity sold) is surrounded by Dimension Tables (containing descriptive attributes like Product details, Customer info, Time, Location).
Similar to Star Schema, but dimension tables are further normalized into sub-dimension tables. Reduces data redundancy at the cost of more complex queries (more JOINs).
A Data Mart is a subset of a data warehouse focused on a specific business department (e.g., a Sales Data Mart, a Marketing Data Mart). It provides department-level analysts with faster access to their relevant data.