Data Warehousing Questions Medium
There are three main data warehouse modeling techniques:
1. Dimensional Modeling: This technique focuses on organizing data into dimensions and facts. Dimensions represent the descriptive attributes of the data, such as time, location, and product, while facts represent the numerical measures or metrics. Dimensional modeling uses a star schema or snowflake schema to create a simplified and intuitive structure for querying and analyzing data.
2. Entity-Relationship Modeling: This technique is based on the traditional entity-relationship (ER) model used in database design. It involves identifying entities (objects or concepts) and their relationships in the data warehouse. The ER model represents entities as tables and relationships as foreign keys. Entity-relationship modeling is useful when dealing with complex data structures and relationships.
3. Data Vault Modeling: This technique focuses on creating a flexible and scalable data warehouse model. It involves separating the data into three main components: hubs, links, and satellites. Hubs represent the core business entities, links represent the relationships between these entities, and satellites contain the descriptive attributes of the entities. Data vault modeling allows for easy integration of new data sources and provides a historical view of the data.
Each modeling technique has its own advantages and is suitable for different types of data warehouse projects. The choice of modeling technique depends on factors such as the complexity of the data, the analytical requirements, and the scalability needs of the organization.