Rank

Allows the user to determine the rank of a value in a group of values, output as a new column. The function used depends on the data warehouse as below:

Snowflake: Snowflake window function.
Redshift: Redshift window function.
BigQuery: BigQuery window function.
Synapse: Synapse window function.
Delta Lake: Delta Lake window function.

Properties

Name = string

A human-readable name for the component.

Include Input Columns = drop-down

Defines whether the component passes all input columns into the output.

Partition Data = dual listbox

Defines how the input data is partitioned to perform the rank calculation. The calculation is then performed on each partition.

Ordering within partitions = column editor

Input Column: The input column name for sorting within the partitioned data. You can drag to reorder.
Ordering: The order of the sorting: Ascending (Asc) or Descending (Desc).

Functions = column editor

Window Function:
- Rank determines the rank of a value in a group of values.
- Dense Rank determines the rank of a value in a group of values. The Dense Rank function differs from Rank in one respect: if two or more rows tie, there is no gap in the sequence of ranked values.
- Cumulative Distribution determines the cumulative distribution of a value within a window or partition.
- Percent Rank Calculates the percent rank of a given row. Row Number Determines the ordinal number of the current row within a group of rows, counting from 1.
Output Column: The name of the output column that the window function will create.

Strategy

Generates a select statement with a window function in line using the OVER keyword. For more information, read:

Video

Snowflake	Delta Lake on Databricks	Amazon Redshift	Google BigQuery	Azure Synapse Analytics
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