New in 10.2 - Data360_DQ+ - Latest

Save your filters on the case store search screen

A new Save My Filters option has been added on the case store search screen. Selecting this option allows you to save your filter settings on a per search screen basis, for each search screen that you use. If you do not want to save your filter settings, you can leave Save My Filters unchecked.

In previous releases, case store search screens saved your filter settings on a per search screen basis, and restored those settings when you return to the screen, however there was not previously an option to control this behavior.

Enforce settings when transitioning cases

A new Enforce field behaviors on multiple case transition option has been added on the case store transition dialog. Selecting this option allows you to enforce Required and Read only settings when transitioning multiple cases.

For all transitions that have already been defined, the Enforce field behaviors on multiple case transition option will not be selected by default. For new transitions, the option will be selected by default. When multiple cases are being transitioned and the option is checked, required fields will display as empty, but a value will be required to complete the dialog. Read only fields will be empty and read-only.

Import case store field definitions from a data store

A new Import From button has been added to the case store search and edit screens. Clicking Import From allows you to import screen field definitions from a data store search or edit screen. The Import From button is enabled when you are creating a case store definition and you have not yet added fields.

Define multiple case store value filters

When defining a case store, you can now define multiple value filters for non-codeset fields. Previously, when a non-codeset field was chosen as a filter field, the selection type was disabled and defaulted to single.

Create profile-based rules in a rule library

When adding rules to a rule library, Data360 DQ+ can now create basic rules based on the profile metrics of a data store that has profiling enabled. This allows you to set data quality rules against profiled data elements on the data source, select which rules to apply and tailor any defined values to meet changing source inputs.

Show current environment on profile page

On the View Profile page, you can now see an environment drop-down if you have access to at least one environment. Previously, you would only see the environment drop-down if you had access to more than one environment.

Usability enhancements

• Improved the formatting of code on the Edit Expression dialog.
• Improved the usability of the Group By Aggregation dialog and Compute Field definition dialog by making fields stretch to use the available dialog width and increasing the default dialog width.
• Added a context menu to data tables in the Analysis and Case Store screens to allow you to set column widths based on header text or column data.
• Users who have been disabled will no longer receive notification emails.
• A warning message is displayed if the provided 'tenantId' URL parameter is invalid.

SSO enhancements

Data Observability integration

• To enable data observability schema drift alerts, tableStructureInfo is now included on data profiles for data stores.
• To enable data observability freshness alerts, the source last modified time is now included on data profiles for data stores.

Update to Google Dataproc cluster creation

Data360 DQ+ can connect to Google Dataproc when running on Google Cloud Platform. In this release, the command to create the required Google Dataproc cluster has changed. If you are an existing Enterprise/Google Dataproc customer, you need to recreate your Google Dataproc cluster to the newer version.

See the "Google Dataproc prerequisites" section in the Enterprise Installation Guide for details of the required steps and commands.

Other changes

• Upgraded the version of Apache HTTPD in the load balancer container to 2.4.57.
• Updated the FTA library to the latest version to get support for identifying more semantic types.
• The execution engine has been upgraded to run on infrastructure based on Spark 3.3, including Azure/Databricks, AWS EMR, Cloudera CDP, and Google Dataproc.

Corrected issues