Date feature engineering

We now offer feature engineering for date fields in Qlik AutoML, building on existing automated capabilities around data ingestion and preparation. Date feature engineering automatically breaks down date fields into a series of usable elements, such as year, month, day, etc. that can be easily factored into ML models for analysis.

Automatic feature engineering

Public collections

We are pleased to introduce public collections, which let Tenant and Analytics admins create collations of content from one or more spaces for their users. These collections provide a simple, yet powerful way to group content into more logical buckets for your end users, which is especially valuable when content that’s regularly used by specific business teams or roles are spread across multiple spaces.

As public collections respect spaces access control, users will automatically see collections if they have access to at least one piece of content in that collection via space membership. They will only see content in the collection that they have permission to see. Content can be added to one or many collections.

Collections

Custom SQL with incremental load

Enable incremental pipelines for complex, custom SQL in support of near real-time analytics and data engineering use cases. Custom SQL supports a broad set of use cases and persona’s from analytics engineers to data engineers. Macros provide the ability to customize initial and incremental load jobs to support a variety of use cases including:

• Filter support to define a filter and reduce data processed during incremental load jobs

• Adjusting joins or column mappings for initial v incremental loads

• Union legacy data into an initial load process for the purpose of migrations

Adding SQL-based datasets