Data warehousing is the process of collecting, organizing, and storing large volumes of data from multiple sources into a centralized repository optimized for analytical processing and business intelligence. Unlike transactional databases (OLTP) designed for real-time operations, data warehouses are purpose-built for historical analysis, complex queries, and reporting, enabling organizations to make data-driven decisions. The core design principle involves dimensional modeling—organizing data into fact tables (measurements) and dimension tables (descriptive context)—typically implemented through star or snowflake schemas. Understanding schema design, slowly changing dimensions, and ETL/ELT patterns is essential for building scalable, performant data warehouses; in 2026, modern architectures like lakehouses, medallion layers, and open table formats have extended the classical warehouse paradigm significantly.
What This Cheat Sheet Covers
This topic spans 19 focused tables and 94 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Schema Design Patterns
| Pattern | Example | Description |
|---|---|---|
Fact table (Sales) linked directly to denormalized dimensions (Product, Customer, Time) | • Central fact table surrounded by dimension tables • fast query performance due to fewer joins • most widely adopted pattern. | |
Normalized dimension tables with sub-dimensions: Product → Category → Subcategory | • Dimensions normalized into multiple related tables • reduces data redundancy but increases join complexity and query time. | |
Multiple fact tables (Sales, Inventory) sharing common dimensions (Time, Product, Location) | • Multiple fact tables share conformed dimensions • supports cross-process analysis like comparing sales and inventory trends. |