Aggregate Component
Aggregate Component
The Aggregate component groups together multiple input rows into a single output row. Input columns can be added to the groupings, or have an aggregation applied to them.
Default output names are chosen by combining the source column name and the aggregation type. If they are not appropriate, consider using a Rename Component immediately after the Aggregate component to choose more appropriate names.
Redshift Properties  

Property  Setting  Description 
Name  String  Specify the descriptive name for the component. 
Groupings  Column Select  Select one or more columns from the source table that will form the groupings. The output flow will contain one row for each distinct combination of values within the grouping columns. 
Aggregations  Source Columns  Select the input (source) column for the summary function (the summary function is the aggregation type). 
Aggregation Type  Users can add the same source column multiple times, each with a different aggregation type. The summary is calculated for each combination of the source column values. Min: Find the Minimum value of the source column. Max: Find the Maximum value of the source column. Count: Count the total number of records whose source column is not null. Count Distinct: Count the total number of distinct (unique) values of the source column. Approximate Count: This is a variant of Count Distinct that may have better performance in cases where an exact count is not required. See the Redshift documentation for more details. Sum: Sum the values in the source column. This is only sensible for numeric source columns. Average: Average the values in the source column. This is only sensible for numeric source columns. Standard Deviation (sample): Calculates the sample standard deviation of the source column. This is only sensible for numeric source columns. Standard Deviation (population): Calculates the population standard deviation of the source column. This is only sensible for numeric source columns. Variance (sample): Calculates the sample variance of the source column. This is only sensible for numeric source columns. Variance (population): Calculates the population variance of the source column. This is only sensible for numeric source columns. Median: Calculates the median value for the range of values. For more information, refer to theRedshift Median documentation. For more information on the Aggregation Types, refer the Redshift Aggregate Functions documentation. 
Snowflake Properties  

Property  Setting  Description 
Name  String  Specify the descriptive name for the component. 
Groupings  Column Select  Select one or more columns from the source table that will form the groupings. The output flow will contain one row for each distinct combination of values within the grouping columns. 
Aggregations  Source Columns  Select the input (source) column for the summary function (the summary function is the aggregation type). 
Aggregation Type  Users can add the same source column multiple times, each with a different aggregation type. The summary is calculated for each combination of the source column values. Min: Find the Minimum value of the source column. Max: Find the Maximum value of the source column. Count: Count the total number of records whose source column is not null. Count Distinct: Count the total number of distinct (unique) values of the source column. Approximate Count: This is a variant of Count Distinct that may have better performance in cases where an exact count is not required. Sum: Sum the values in the source column. This is only sensible for numeric source columns. Average: Average the values in the source column. This is only sensible for numeric source columns. Standard Deviation (sample): Calculates the sample standard deviation of the source column. This is only sensible for numeric source columns. Standard Deviation (population): Calculates the population standard deviation of the source column. This is only sensible for numeric source columns. Variance (sample): Calculates the sample variance of the source column. This is only sensible for numeric source columns. Variance (population): Calculates the population variance of the source column. This is only sensible for numeric source columns. Median: Calculates the median value for the range of values. For more information, refer to the Snowflake Median documentation. For more information, refer to the Snowflake Aggregate Functions documentation. 

Grouping Type  Select  Select how to group rows. Group By: Groups rows with the same groupbyitem expressions and computes aggregate functions for the resulting group. A GROUP BY expression can be a column name, a number referencing a position in the SELECT list, or a general expression. Group By Cube: GROUP BY CUBE is an extension of the GROUP BY clause similar to GROUP BY ROLLUP. In addition to producing all the rows of a GROUP BY ROLLUP, GROUP BY CUBE adds all the "crosstabulations" rows. Subtotal rows are rows that further aggregate whose values are derived by computing the same aggregate functions that were used to produce the grouped rows. Group By Grouping Sets: GROUP BY GROUPING SETS is a powerful extension of the GROUP BY clause that allows computing multiple groupby clauses in a single statement. The group set is a set of dimension columns. Group By Rollup: GROUP BY ROLLUP is an extension of the GROUP BY clause that produces subtotal rows (in addition to the grouped rows). Subtotal rows are rows that further aggregate whose values are derived by computing the same aggregate functions that were used to produce the grouped rows. 
BigQuery Properties  

Property  Setting  Description 
Name  String  Specify the descriptive name for the component. 
Groupings  Column Select  Select one or more columns from the source table that will form the groupings. The output flow will contain one row for each distinct combination of values within the grouping columns. 
Aggregations  Source Columns  Select the input (source) column for the summary function (the summary function is the aggregation type). 
Aggregation Type  Users can add the same source column multiple times, each with a different aggregation type. The summary is calculated for each combination of the source column values. Min: Find the Minimum value of the source column. Max: Find the Maximum value of the source column. Count: Count the total number of records whose source column is not null. Count Distinct: Count the total number of distinct (unique) values of the source column. Approximate Count: This is a variant of Count Distinct that may have better performance in cases where an exact count is not required. Sum: Sum the values in the source column. This is only sensible for numeric source columns. Average: Average the values in the source column. This is only sensible for numeric source columns. Standard Deviation (sample): Calculates the sample standard deviation of the source column. This is only sensible for numeric source columns. Standard Deviation (population): Calculates the population standard deviation of the source column. This is only sensible for numeric source columns. Variance (sample): Calculates the sample variance of the source column. This is only sensible for numeric source columns. Variance (population): Calculates the population variance of the source column. This is only sensible for numeric source columns. For more information on Aggregation functions in BigQuery, see the BigQuery documentation. Array Aggregate: Constructs an array from column data. See the BigQuery documentation for more information. 
Strategy
The Aggregate component generates summary (aggregate) functions along with a groupby clause.
Example
This job counts the number of flights and totals the distance travelled for each tail number in the input data.
Groupings are set to Tail Number (tailnum).
Aggregations are set to total the distance, and count the records whose flight number is not null.
The sample data confirms that the data is being summarised as expected.