Fact Table vs Dimension Table: Understanding Data Warehousing Components

A star schema design relies on fact tables and dimension tables, which work together to enable meaningful data analysis in a data warehouse. Fact tables store quantitative metrics, while dimension tables provide descriptive information to interpret those metrics. Understanding the differences between fact tables and dimension tables is crucial for designing an efficient and scalable data warehouse. Optimizing these components, including reducing table size, using indexing strategies, partitioning tables, pre-aggregating data, and regularly reviewing and optimizing indexing, can improve performance and support fast querying and accurate reporting in Business Intelligence (BI) applications such as Power BI, Tableau, and QlikView.