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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.
Create/alter table: Yes - can create table, alter its definition and data, and add new rows; Some - can only create/alter table definition, not data; Browse table: Yes - can browse table definition and data; Some - can only browse table definition; Multi-server support: Yes - can manage from the same window/session multiple servers
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.
For example, it is easy to support COUNT, MAX, MIN, and SUM in OLAP, since these can be computed for each cell of the OLAP cube and then rolled up, since on overall sum (or count etc.) is the sum of sub-sums, but it is difficult to support MEDIAN, as that must be computed for every view separately: the median of a set is not the median of ...
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Stored procedures may also contain declared variables for processing data and cursors that allow it to loop through multiple rows in a table. Stored-procedure flow-control statements typically include IF, WHILE, LOOP, REPEAT, and CASE statements, and more. Stored procedures can receive variables, return results or modify variables and return ...
OLAP clients include many spreadsheet programs like Excel, web application, SQL, dashboard tools, etc. Many clients support interactive data exploration where users select dimensions and measures of interest. Some dimensions are used as filters (for slicing and dicing the data) while others are selected as the axes of a pivot table or pivot chart.
In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration [1] and data management tasks such as data wrangling, data warehousing, data integration and application integration.