What’s New in Qlik Cloud

Data Integration

This month’s updates bring a strong focus on interoperability, intelligent documentation, and secure connectivity, alongside some quality-of-life upgrades that make daily work just that little bit smoother.

Whether you’re streamlining pipelines, preparing for RAG AI use cases, or cleaning up how data products are managed and presented, there’s something in here to make your day easier and your platform smarter.

1. Secure Data Source Access via SSH Tunnel

Users who aren’t using a Data Movement Gateway can now connect to supported data sources via SSH tunnelling. This allows secure access to databases behind firewalls, with all traffic encrypted as it moves through the tunnel.

Supported connectors:

• MySQL
• PostgreSQL
• Microsoft SQL Server (CDC-based)

This is a useful option especially for teams working in more locked-down environments or hybrid network setups.

Note: Not available in Qlik Cloud Government or Qlik Cloud Government – DoD.

More information: SHH tunnel

2. Dedicated Menu for Data Product Management

Data product management now has its own menu entry, separate from the Data Marketplace. This is a small change that will help data product managers find what they need faster while also setting the stage for future improvements to the data product experience.

More information: Working with data products

3. New Import Format and Project Import UI

There’s a new project import interface in Qlik Talend Cloud Pipelines that gives users more control when importing from a ZIP file. You can now select key project settings during import, including:

• Database connections for each task
• Target connections
• Cross-project pipeline sources
• Iceberg volume and catalog details

Alongside this, the export format has changed: projects now export as a ZIP file with multiple JSON files rather than a single JSON. The old format is still supported for import until January 30, 2026, but cannot be used with the API.

To convert existing projects, simply import them in the old format, then re-export using the new one.

More information:
Exporting and importing data pipelines
Exporting and importing replication projects

4. Use Qlik Talend Cloud Rules Directly in Talend Studio

Talend Studio users can now apply validation rules and semantic types defined in Qlik Talend Cloud without having to recreate them locally. A new component makes it possible to re-use centralised rules. This will help teams stay consistent while saving time.

Additional benefits:

• No need to duplicate rule logic across platforms
• One source of truth for validation rules
• Faster development and fewer discrepancies

A practical step toward tighter governance and smoother collaboration across your data projects.

More information:
Working with validation rules
tDQRules properties

5. Data Product Documentation — Now All in One Place

Documentation for data products has been given a usability boost. Instead of clicking through multiple tabs and datasets to find profiles, quality scores, field descriptions, and lineage, you can now view a single, comprehensive, Wikipedia-style summary.

This overview is available directly within the Data Marketplace, or you can export it as a PDF for easy sharing and offline review. It’s a small change that makes understanding and consuming data products far more efficient.

More information: Generating the documentation of a Data Product

6. Catalog Filter Label Update: “Data” Becomes “Datasets”

In the Catalog filter menu, the label “Data” has been updated to “Datasets” to better reflect what you’re actually filtering. It’s a small change but it keeps things clearer and consistent.

More information: Browsing datasets from the Catalog

7. Smarter SQL Editing for a Smoother Workflow

Writing SQL in Qlik Talend Cloud Pipelines is now a much friendlier and neater experience. With auto-completion and formatting built in, the editor will suggest table names, column names, and SQL keywords as you type.

This update is available to all users, helping you write cleaner code, faster.

8. Introducing Knowledge Marts: Fuel for Your Retrieval-Augmented Generation (RAG) AI Workflows

Qlik Talend Cloud now supports Knowledge Marts, a new task type that transforms structured and unstructured data into domain-aware documents, vectorises them, and loads them into a vector store, ready for semantic search and RAG-style AI applications.

There are two task types:

• Knowledge Mart Task
  Automates the transformation of structured data into searchable documents using a familiar data mart-style interface, and pushes them into a supported vector store with incremental updates and vectorisation included.
• File-based Knowledge Mart Task
  Handles unstructured content from file shares, FTP, OneDrive, or SharePoint, chunking and embedding documents for vector search.

Both task types support natural language testing using the Test Assistant, so you can ask questions and explore your data semantically right in the interface.

Supported vector stores include:

• Snowflake Cortex
• ElasticSearch
• OpenSearch
• Pinecone

Supported embedding models:

• Snowflake Cortex (required when Cortex is the target vector store)
• Amazon Bedrock
• Azure OpenAI
• OpenAI

Knowledge Mart tasks are available in Qlik Talend Cloud Enterprise Edition. This is a major step forward for teams building intelligent, search-ready data pipelines to power modern AI applications.

More information: Creating knowledge marts

9. Generate Data Product Descriptions with AI

Documenting data products is now faster. Qlik Talend Cloud Enterprise users can now use generative AI to auto-generate descriptions for data products. These are based on the datasets, columns, and existing metadata within the product.

You can accept, reject, or edit the suggested description, making it easier to keep documentation clear and consistent. This is currently available in English only.

Note: Not supported in Qlik Cloud Government or Qlik Cloud Government – DoD.

10. Validation Rules Get Boolean Logic Support

Validation rules in Qlik Talend Cloud now support Boolean operators. This update will allow you to check if the content of one field equals or differs from another and evaluate true/false conditions directly.

A simple but powerful addition for building simpler data quality checks.

More information: Working with validation rules

11. Data products retain model when opened in Qlik Cloud Analytics

Creating a Qlik Cloud Analytics app from a data product? Well, the good news is, the data model defined in Qlik Talend Cloud now carries over automatically. That means no need to rebuild relationships manually, and a much smoother handoff from pipeline to analysis.

You’ll see the model in the Data Model Viewer within Qlik Cloud Analytics, just as you would for any standard app.

A few things to note:

• Only the model explicitly defined in the Qlik Talend Cloud project is included. Column names alone don’t drive structure.
• An issue with automatic renaming of duplicate column names has been fixed, making model recommendations more accurate.
• The data model isn’t viewable within the data product itself. It is visible in the Qlik Talend Cloud project or the Qlik Cloud Analytics app.
• Exporting the data model as a separate asset isn’t currently supported.

A very welcome improvement for teams building governed data products at scale.

More information: Creating an analytics app using datasets generated in Data Integration

12. AI Assistants Now Available in More Regions

Qlik’s SQL assistant and data model relationship assistant are now supported in two additional Qlik Cloud regions: Tokyo (Asia-Pacific North East 1) and Singapore (Asia-Pacific South East 1).

Note: Not supported in Qlik Cloud Government or Qlik Cloud Government – DoD.

More information: Generative AI based assistant in Data Integration

13. Rollover and Purging for Replication Log Files

To improve performance and manage disk space, rollover and purge policies have been introduced for replication log files on the gateway machine.

Logs will now rotate before they get too large, and old files will be automatically cleaned up, keeping things tidy and efficient behind the scenes.

More information: Log retention and rollover policy for replication logs

14. Open Databricks datasets in Databricks

In a major step toward greater interoperability, Qlik now supports opening Databricks datasets directly in Databricks. This is the first non-Qlik integration point for datasets within the Qlik Cloud platform.

This update allows users to work with governed data products outside the Qlik ecosystem. It’s a clear sign of Qlik’s shift toward open, flexible data collaboration, and perhaps a sign of more integrations to come.

More information: Opening data sets in third parties

15. New task logging option

See more information on viewing logs for Data Tasks not using Data Movement gateway here.