- Home
- /
- Qlik Cloud August 2026: what’s new
Reading time: 20 min 
ANALYTICS
The revised Qlik Answers business logic experience in AI optimization provides a focused experience for optimizing your applications for Qlik Answers. The new Semantic understanding tab lets you review and refine AI-generated descriptions for fields and master items, giving you fine-grained control over how Qlik Answers interprets your data model.
Optimizing your application for Qlik Answers with business logic
Managing Qlik Answers-generated field and master item descriptions
Direct Access gateway now surfaces benchmark test results directly in the Administration activity center, giving administrators more transparency into gateway performance without needing to dig through log files.
Benchmark tests are diagnostic operations that measure network connectivity and performance between the Direct Access gateway and Qlik Cloud. Use them to troubleshoot reload performance, establish performance baselines, or compare gateways.
You can now view the results of these benchmark runs directly in the Administration activity center, making it easier to identify network conditions that affect reload performance and to gather the information you need for troubleshooting — without leaving the admin experience.
To use this functionality, you need Direct Access gateway version 1.7.17.
For more information, see Viewing and running benchmark tests.
For more information, see Date picker.
QVD and Parquet files larger than 1GB are now supported for registration to the catalog from cloud storage. The same size-based conditions apply to profiling: datasets exceeding 12 million rows will undergo light profiling instead of full profiling.
Managing field-level metadata and data profiling
You can now store data stewardship records in Qlik Cloud, in addition to Snowflake.
When you set up a sprint, you choose where resolved records are written: your own Snowflake environment, or Qlik Cloud native storage. Qlik Cloud storage removes the need to configure an external database connection before running a sprint. This means teams without a Snowflake environment, or who would rather keep stewardship data inside Qlik Cloud, can get started faster.
Data contracts in Qlik Cloud add a continuous evaluation layer to dataset quality management. The contract checks the dataset against defined thresholds, and produces a status signal before a violation reaches consumers. Owners and consumers see that status through visual indicators and are notified when expectations are not met. The result: quality degradation surfaces to the owner directly, without needing a consumer to notice or report it first.
What is available
Data stewardship in Qlik Cloud adds an AI remediation layer to your stewardship sprint workflow. Before records are assigned to human stewards, the AI analyzes each issue and proposes a corrected value where its confidence is sufficient. Stewards then review, accept, or modify each suggestion. The result: stewards can apply their judgment at scale, on larger volumes of recurring data issues, without losing the human accountability that makes remediated data trustworthy.
Reviewing AI fixes in a resolution sprint
Semantic types can now be imported and exported directly from the interface, using a JSON file. This supports migrating types between tenants and replicating configurations across environments in a few clicks.
……. … ………… ….
Any questions please contact our consultants. Responding in one working day.
