Generate dynamic prediction dataset names with time-specific variables in Qlik AutoML

When setting up a prediction configuration from an ML deployment in Qlik AutoML, you can now generate dynamic prediction dataset names with time-specific variables. This is available for both manually-run and scheduled predictions. It is particularly useful when scheduling multiple prediction jobs from the same ML deployment, and helps differentiate each dataset that you are generating based on when the prediction job was run.

Using variables in prediction dataset file names

Direct Query now supports Amazon Redshift

You can now connect to Amazon Redshift databases using Direct Query. Direct Query allows analytics applications to directly query cloud databases with SQL pushdown as users interact with data through visualizations and filtering. App developers can use Direct Query to build SQL-centric applications for big-data analysis or near real-time scenarios.

Accessing cloud databases directly with Direct Query

Supported Amazon Redshift SQL data types