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 that serve as the single source of truth for enterprise analytics.

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