This article describes the transforms you can use when creating a data cube (ETL process).

The Tabular Select transform is created when you drag a structure onto the data cube canvas from a data connector that supports tabular data (e.g., Excel, XML, SQL Server table-valued functions).

The SQL Select transform is created when a structure is dragged onto the canvas from a data connector that supports relational queries.

The MDX Select transform is created when a cube from an OLAP database connector is dragged onto the canvas.

Enter a SQL or MDX statement to make a selection from a data connector instead of dragging native structures onto the canvas.

The Stored Procedure Select transform lets you retrieve data using a relational stored procedure.

The Data Cube transform is created when another data cube is dragged onto the canvas.

The Aggregate transform allows use of aggregate functions such as sum, average, count, minimum, maximum, etc.

The Calculated Element transform lets you create new elements by writing DundasScript expressions. 

The Data Conversion transform allows you to change the data type of a column.

The Filter transform filters out rows that do not meet the configured criteria/settings.

The Join transform combines two separate tables together by matching up their rows. All of the columns from both tables can be included.

The Lookup transform replaces the values of one or more key columns with the values you choose in another table.

The String transform manipulates string columns by applying string functions.

The Union transform combines the rows from two tables that have matching columns.

The Math transform lets you perform simple math functions such as Absolute, Round, and Square Root on numeric input columns.

The Copy Element transform creates new columns by copying selected input columns and adding the new columns to the output.

The Fuzzy Grouping transform allows grouping of records by looking at the similarity between the values of various columns.

The Fuzzy Lookup transform joins the columns of two tables into one table by matching key values, where they may not match exactly between the two tables.

This transform returns the specified percentage out of the full set of input records using random selection.

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