The hero feature of Ajilius 2.1 is Data Quality Automation.

This is yet another unique feature brought to data warehouse automation by Ajilius.

In the 2.1 release, Ajilius adds three types of data quality screens to the extract process:

  • Data type validation, where values are tested for conformance to the column data type.
  • Range validation, where values are tested for set and range boundaries.
  • Regex validation, where values are tested against regex regular expressions.

In Version 2.3 (due September 2016) we will be adding Lookup validation to data quality rules, to check the existence of values in data warehouse tables.

Rows breaking validation are logged to an error file, along with the reason/s for row rejection.

A new return code from the extract job signals that validation errors have occurred, enabling the scheduler to choose to continue or suspend the batch pending user remediation of the errors.

And once again, we’re adding this as a standard feature of the Ajilius platform. If you’re licensed for Ajilius, upgrade to the latest version and you can immediately identify and screen data quality problems before they hit your data warehouse.

Ajilius. Committed to innovation in data warehouse automation.

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