Calculated Column FAQs

This section answers the most frequently asked questions (FAQs) about creating and using calculated columns in an AtScale cube.

What can I do with a calculated column?

Calculated columns are useful for deriving values from the original dataset columns (such as calculating someone's age based on their birth date), doing data cleansing and pre-processing (such as combining column values or substituting one value for another), or for computing new data values based on a number of input variables (such as calculating a profit margin value based on revenue and costs).

Can I refer to a calculated column in another calculated column formula?

No. Calculated column formulas can operate only on the original dataset columns.

What formulas can I use to define a calculated column?

The formulas that describe a calculated column must be valid SQL expressions that:

  • Would be valid as an item in the column list of a SELECT statement.
  • Use SQL functions and expression syntax supported by the engine you are using to query your data warehouse.
  • Do not require an input or output type that AtScale doesn't support. (For example, SQL functions that operate on map or array data types.)

Can I use aggregate functions in a calculated column formula?

No. Although it is valid SQL, it is not the correct way to add aggregations to an AtScale cube. Instead, define a measure or a calculated member using the aggregate calculations that AtScale supports.

What SQL operators and functions can I use in a calculated column?

If you are using Google BigQuery as your data warehouse, see Functions & Operators in the BigQuery documentation.

If you are using a Hadoop cluster as your data warehouse, the answer depends on the underlying SQL-on-Hadoop engine that you are using. Here are links to the documentation for the SQL-on-Hadoop engines that AtScale supports:

EngineLink to SQL Docs
HiveSQL Functions: Operators:
ImpalaSQL Functions: Operators:
SparkSQLSpark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. Currently Spark SQL is based on Hive 0.12.0 and 0.13.1.

Is the AS statement supported?

Yes, except when using InterSystems IRIS as a data warehouse.

The AS (alias) statement is not supported by InterSystems IRIS, and you should not use it in the formula for a calculated column. Using it with such a data warehouse would lead to failed queries with "Unsupported CAST target specified" message.