Dimension Tables:
· The dimension tables contain attributes (or fields) used to constrain and group data when performing data warehousing queries.
· In a data warehouse, a dimension is a data element that categorizes each item in a data set into non-overlapping regions.
· For example, "Customer", "Date", and "Product" are all dimensions that could be applied meaningfully to a sales receipt.
5. Types of Dimensions Tables:
I. Conformed Dimension: The dimension that is shared across multiple fact tables. At the most basic level, conformed dimensions mean the exact same thing with every possible fact table to which they are joined. The date dimension table connected to the sales facts is identical to the date dimension connected to the inventory facts. Ex: Time Dimension.
II.Junk Dimension: Junk dimension is just a dimension that stores unwanted attributes. A junk dimension is a convenient grouping of typically low-cardinality flags and indicators. By creating an abstract dimension, these flags and indicators are removed from the fact table while placing them into a useful dimensional framework.
III. Degenerated Dimension: In a data warehouse, a degenerate dimension is a dimension which is derived from the fact table and doesn't have its own dimension table. The decision to use degenerate dimensions is often based on the desire to provide a direct reference back to a transactional system without the overhead of maintaining a separate dimension table.
IV. Slowly Changing Dimension: Slowly Changing Dimensions (SCDs) are dimensions that have data that changes slowly, rather than changing on a time-based, regular schedule. It’s further classified into 3 types.
· SCD Type 1: This type of dimension table maintains the latest or current data.
· SCD Type 2: This type of dimension table maintains complete history.
· SCD Type 3: This type of dimension table maintains partial history.
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