dimensiemodellering

Dimensiemodellering is a technique used in data warehousing to organize and structure data for analytical purposes. It is a conceptual design approach that focuses on making data easily understandable and queryable for business intelligence and reporting. The core of dimensional modeling involves creating two types of tables: fact tables and dimension tables.

Fact tables contain the quantitative measurements or metrics that represent business events or transactions. These are typically numerical values like sales amounts, quantities, or counts. Fact tables also contain foreign keys that link to the dimension tables. Dimension tables, on the other hand, contain descriptive attributes that provide context to the facts. These attributes answer the "who, what, where, when, why, and how" questions about the data. Examples of dimension attributes include customer names, product descriptions, store locations, and dates.

The most common dimensional modeling schema is the star schema, where a central fact table is surrounded by multiple dimension tables, resembling a star. Another common schema is the snowflake schema, which is a variation of the star schema where dimension tables are normalized into multiple related tables. Dimensional modeling aims to optimize for read performance and ease of use for end-users rather than for transaction processing efficiency, which is the focus of normalized modeling. This design facilitates faster query execution and intuitive data exploration for business analysis.