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A pivot table is a table of values which are aggregations of groups of individual values from a more extensive table (such as from a database, spreadsheet, or business intelligence program) within one or more discrete categories. The aggregations or summaries of the groups of the individual terms might include sums, averages, counts, or other ...
A database refactoring is a simple change to a database schema that improves its design while retaining both its behavioral and informational semantics. Database refactoring does not change the way data is interpreted or used and does not fix bugs or add new functionality. Every refactoring to a database leaves the system in a working state ...
For example, an Employees table might include fields such as Last Name and Hire Date. Specify primary keys – Choose each table's primary key. The primary key is a column, or a set of columns, that is used to uniquely identify each row. An example might be Product ID or Order ID. Set up the table relationships – Look at each table and decide ...
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.
Power Pivot expands on the standard pivot table functionality in Excel. In the Power Pivot editor, relationships can be established between multiple tables to effectively create foreign key joins . Power Pivot can scale to process very large datasets in memory, which allows users to analyze datasets that would otherwise surpass Excel's limit of ...
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.
You don't need to reprocess the fact table if there is a change in the dimension table (e.g. adding additional fields retrospectively which change the time slices, or if one makes a mistake in the dates on the dimension table one can correct them easily). You can introduce bi-temporal dates in the dimension table.
Clearly creating a table (or a set of tables) with thousands of columns is not feasible, because the vast majority of columns would be null. To complicate things, in a longitudinal medical record that follows the patient over time, there may be multiple values of the same parameter: the height and weight of a child, for example, change as the ...