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Data Analysis Expressions (DAX) is the native formula and query language for Microsoft PowerPivot, Power BI Desktop and SQL Server Analysis Services (SSAS) Tabular models. DAX includes some of the functions that are used in Excel formulas with additional functions that are designed to work with relational data and perform dynamic aggregation.
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 ...
Pivot tables are not created automatically. For example, in Microsoft Excel one must first select the entire data in the original table and then go to the Insert tab and select "Pivot Table" (or "Pivot Chart"). The user then has the option of either inserting the pivot table into an existing sheet or creating a new sheet to house the pivot table.
In their book Pivot Table Data Crunching, authors Bill Jelen and Mike Alexander call Pito Salas the "father of pivot tables" and credit the pivot table concept with allowing an analyst to replace fifteen minutes of complicated data table and database functions with "just seconds" of dragging fields into place.
Access 2010 includes table-level triggers and stored procedures built into the ACE data engine. Thus a Client-server database system is not a requirement for using stored procedures or table triggers with Access 2010. Tables, queries, forms, reports and macros can now be developed specifically for web based applications in Access 2010.
An associative (or junction) table maps two or more tables together by referencing the primary keys (PK) of each data table. In effect, it contains a number of foreign keys (FK), each in a many-to-one relationship from the junction table to the individual data tables. The PK of the associative table is typically composed of the FK columns ...
The example above is the simplest kind of contingency table, a table in which each variable has only two levels; this is called a 2 × 2 contingency table. In principle, any number of rows and columns may be used. There may also be more than two variables, but higher order contingency tables are difficult to represent visually.
For example, think of A as Authors, and B as Books. An Author can write several Books, and a Book can be written by several Authors. In a relational database management system, such relationships are usually implemented by means of an associative table (also known as join table, junction table or cross-reference table), say, AB with two one-to-many relationships A → AB and B → AB.
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