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The snowflake schema is a variation of the star schema, featuring normalization of dimension tables. In computing , a snowflake schema or snowflake model is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape.
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
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 schema.
Consider a database of sales, perhaps from a store chain, classified by date, store and product. The image of the schema to the right is a star schema version of the sample schema provided in the snowflake schema article. Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product.
OLAP data is typically stored in a star schema or snowflake schema in a relational data warehouse or in a special-purpose data management system. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables .
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system.. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
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