Dimensional Modeling in Data Warehousing
Dimensional modeling (DM) is the name of a set of techniques and concepts used in data warehouse design. It is considered to be different from entity-relationship modeling (ER). Dimensional Modeling does not necessarily involve a relational database. The same modeling approach, at the logical level, can be used for any physical form, such as multidimensional database or even flat files. According to data warehousing consultant Ralph Kimball, DM is a design technique for databases intended to support end-user queries in a data warehouse. It is oriented around understandability and performance. According to him, although transaction-oriented ER is very useful for the transaction capture, it should be avoided for end-user delivery.
Dimensional modeling always uses the concepts of facts (measures), and dimensions (context). Facts are typically (but not always) numeric values that can be aggregated, and dimensions are groups of hierarchies and descriptors that define the facts. For example, sales amount is a fact; timestamp, product, register#, store#, etc. are elements of dimensions. Dimensional models are built by business process area, e.g. store sales, inventory, claims, etc. Because the different business process areas share some but not all dimensions, efficiency in design, operation, and consistency, is achieved using conformed dimensions, i.e. using one copy of the shared dimension across subject areas. The term “conformed dimensions” was originated by Ralph Kimball.
It consists of following topics –
- Data design and dimensional modeling
- Star Schema star schema keys and advantages
- Snowflake schema aggregate fact tables and families of stars