DataWarehouseStrukturen

DataWarehouseStrukturen refers to the underlying organization and arrangement of data within a data warehouse. A data warehouse is designed for reporting and analysis, and its structure is optimized for these purposes, differing significantly from transactional databases. Commonly, data warehouses employ dimensional modeling as their primary structural approach. This involves organizing data into fact tables and dimension tables. Fact tables contain quantitative measures or metrics, such as sales figures or website visits, and foreign keys linking to dimension tables. Dimension tables provide descriptive context for the facts, including attributes like product names, customer demographics, or time periods.

Two prevalent dimensional modeling techniques are star schemas and snowflake schemas. A star schema is characterized by a central fact table surrounded by multiple dimension tables, resembling a star. This design is simpler and often performs better for queries. A snowflake schema, an extension of the star schema, normalizes dimension tables into further sub-dimension tables. This reduces redundancy but can increase query complexity and potentially impact performance. Understanding these DataWarehouseStrukturen is crucial for efficient data retrieval, analysis, and business intelligence. The chosen structure directly influences the ease with which users can extract meaningful insights from the data.