Aggregate FX

The Aggregate FX node is used create a new column based on an aggregation of multiple given numeric columns. This function supports the following numeric column types: Integer, Float 32, Float 64, and BigInteger.

The Aggregate FX function differs from defining aggregation from Data Model, in which case the aggregation type can be set for each measure and is used in the querying process only. Using the Aggregate FX node in the Data Flow creates a new aggregated column that is part of the schema of the underlying database.

The Aggregate FX node is useful if end users have a need to analyze aggregations of multiple columns. For example, there may be a need for a column summing the cost and overhead to show all expenses.

This can be creating by inputting both of those columns in the Aggregate FX node and setting the aggregation type to Sum.

  • Click here to learn more about aggregate functions.

Configure Aggregate FX

Connect the Aggregation FX node to the Select tables of the relevant table. Go to the Properties panel and set the aggregation preferences:

  • Select Column: choose the columns that want to apply the aggregation to.
  • Function Type: set the type of aggregation. That can be Sum, Average, Minimum, or Maximum.
  • Column Output: determine whether or not to keep the original column.
  • New Column Name: name the new column.

Function Type

The Aggregate FX node enables the following aggregate functions:

  • Sum: returns the sum of values in each row from the given numeric columns.
  • Average: returns the average values for each row of the given numeric columns.
  • Minimum: returns the lowest values in each row from the given numeric columns.
  • Maximum: returns the highest values in each row from the given numeric columns.

Split Nodes

The Aggregate Fx node can be used to produce 1 aggregated column. If you want to create multiple aggregated columns based on a single source table, you can connect multiple Aggregate Fx nodes to the relevant table, thereby 'splitting' the table node.

You can then use the merge function to combine the original table with the new aggregated columns. See the final example below for a demonstration of this process.