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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 ...
Queries can drill into different process fact tables separately, then join the results on common dimension attributes. Reduced development time to market. The common dimensions are available without recreating them. Over time, the attributes of a given row in a dimension table may change. For example, the shipping address for a company may change.
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
Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product. Each dimension table has a primary key on its Id column, relating to one of the columns (viewed as rows in the example schema) of the Fact_Sales table's three-column (compound) primary key (Date_Id, Store_Id, Product_Id).
An example of an OLAP cube. An OLAP cube is a multi-dimensional array of data. [1] Online analytical processing (OLAP) [2] is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three.
A fact is represented by a box that displays the fact name along with the measure names. Small circles represent the dimensions, which are linked to the fact by straight lines (see Figure 1). A dimensional attribute is a property, with a finite domain, of a dimension. Like dimensions, a dimensional attribute is represented by a circle.
The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels, or meta-data associated with it.
In a dimensional approach, transaction data is partitioned into "facts", which are usually numeric transaction data, and "dimensions", which are the reference information that gives context to the facts. For example, a sales transaction can be broken up into facts such as the number of products ordered and the total price paid for the products ...