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A junk dimension is a dimension table consisting of attributes that do not belong in the fact table or in any of the existing dimension tables. The nature of these attributes is usually text or various flags, e.g. non-generic comments or just simple yes/no or true/false indicators.
Dimensions can define a wide variety of characteristics, but some of the most common attributes defined by dimension tables include: Time dimension tables describe time at the lowest level of time granularity for which events are recorded in the star schema; Geography dimension tables describe location data, such as country, state, or city ...
The snowflake schema is similar to the star schema. However, in the snowflake schema, dimensions are normalized into multiple related tables, whereas the star schema's dimensions are denormalized with each dimension represented by a single table. A complex snowflake shape emerges when the dimensions of a snowflake schema are elaborate, having ...
Example of a star schema; the central table is the fact table. In data warehousing, a fact table consists of the measurements, metrics or facts of a business process. It is located at the center of a star schema or a snowflake schema surrounded by dimension tables. Where multiple fact tables are used, these are arranged as a fact constellation ...
Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc. These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday. Identify the facts
The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. [5]
The dimensional fact model (DFM) [1] is an ad hoc and graphical formalism specifically devised to support the conceptual modeling phase in a data warehouse project. DFM can be used by analysts and non-technical users as well.
To add further flexibility, more than one main table is allowed, with main and submain tables having a one-to-many relation. Each main table can have its own dimension tables. To provide further query optimization, a data set can be partitioned into separate physical schemas on either the same database server or different database servers.