What’s New in Qlik Cloud

Data Integration

This month’s Qlik Talend Cloud updates bring significant improvements to data integration workflows.

The introduction of AI-powered dataset documentation automates the creation of comprehensive descriptions, while enhanced parallel loading capabilities now support table segmentation for faster data processing.

A standout addition is the new Dates processor, offering eleven powerful functions that simplify date and time manipulations in transformation flows.

As expected from Qlik, these updates focus on improving efficiency, performance, and ease of use across the platform.

1. Easier way of documenting dataset descriptions

Tired of writing dataset documentation? Qlik Talend Cloud now leverages the power of generative AI to automatically create intelligent dataset descriptions. The AI analyses dataset and column names to generate comprehensive documentation, giving you full control to approve, refine, or reject the suggested descriptions.

The benefits are clear; saving time and remaining consistency, by easily capturing new or modified datasets.

This feature needs to be activated:

1. Navigate to Administration > Settings.
2. Scroll to Feature control.
3. Turn on the toggle Generative AI-based description assistant. The option is activated.

Please see the following link for more information.

2. Parallel Load improvements

Qlik Talend Cloud takes data movement performance to the next level with enhanced parallel loading capabilities. While parallel loading of multiple tables has been a standard feature, this release introduces an exciting advancement: parallel processing for individual tables during full load operations.

The new feature intelligently splits source tables into configurable segments that load simultaneously into your target platform. Think of it as multiple lanes on a motorway instead of a single-lane road, your data moves faster and more efficiently.

Smart Segmentation Options

• Date range divisions: Split your data into manageable chunks based on date field.
• Primary key segmentation: Allows data to be segmented by primary key ranges.
• Automatic partition recognition: For supported sources with partitioned tables, the system automatically identifies these and utilises existing partition structures.
• Sub-partition support: For even more refined parallel processing it is now possible to leverage sub-partitions within your source tables.

Key Benefits

• Faster data loading through parallel processing
• Support across multiple task types (Replication, Landing, and Lake Landing)
• Configurable table segmentation

Note: The system manages performance through a configurable ”Maximum number of tables to load in parallel” setting, which controls both the number of segments and concurrent table loads.

For more information and a list of supported sources for partition recognition, please see the following link.

3. New Dates processor in transformation flows

Qlik Talend Cloud introduces a powerful new Dates processor for transformation flows. We all know how painful manipulating dates can be, this update helps simplify the way you handle date and time data.

The functions included are as follows:

• Compare dates: Compare date values, returning a boolean result based on operators like ’equals’ or ’before’.
• Convert to date: Transform data from other types into date format.
• Convert to datetime: Transform data into datetime format.
• Convert to time: Transform data into time format.
• Create date from parts: Build a complete date by combining individual parts.
• Create time from parts: Construct a time value by combining individual parts.
• Create datetime from parts: You guessed it! Construct a datetime value by combining individual parts.
• Truncate date: Remove unwanted date or time parts from a datetime field.
• Add duration: Extend date values by adding a specified time duration.
• Calculate date difference: Compute the time difference between dates, comparing against current time, specific dates, or dates in other columns.
• Extract date parts: Split a date, time, or datetime into its component parts to store in separate columns with custom names.

Gone are the days of complex date manipulations – the Dates processor streamlines these common data transformation tasks into straightforward operations.

For more details, please visit the following link.