Qlik Cloud Update October 2026: Semantic Models, AI, and Modern Development

October brought 24 updates to Qlik Cloud, ranging from minor interface improvements to new capabilities for developers and administrators. But one development clearly stands out: the introduction of semantic models in Data Products.

We discussed the key updates with several of our Qlik consultants. What is new, what does it actually add, and what should you keep in mind?

Semantic Models: centralized management of your KPIs at last

Anyone who develops multiple Qlik applications will recognize the problem. You have a revenue definition that is used in five applications. The definition is then updated, but not everywhere. Before you know it, the same concept shows different figures in different dashboards.

Of course, we have had solutions for this for years, such as shared scripts, QVDs, and shared conventions for master items. But keeping everything consistent requires discipline.

With the new Semantic Models in Data Products, Qlik now offers a more structured solution.

You define relationships, dimensions, and measures centrally in a Data Product. Applications can then reuse these definitions without rebuilding them each time. Central dimensions and measures appear as read-only master items. If you want to change something, you do so centrally.

This is particularly useful for organizations with multiple applications that use the same business concepts. For developers, it also means less duplicated work.

Qlik also makes it possible to extract a Data Product from an existing application. This allows you to reuse existing data models and master items instead of starting over.

This extraction feature is still in Public Preview and has limitations. For example, extraction does not currently support applications using Section Access, or cyclic and drill-down dimensions.

There are practical questions as well. For example, how do semantic models fit into an existing development, testing, acceptance, and production pipeline? Can you easily move definitions from development to production? We want to investigate this thoroughly before adapting existing development processes around it.

And then AI enters the picture

With the introduction of Agentic Semantic Modeling, AI agents can help build and maintain semantic models through Qlik Answers or the Qlik MCP server.

That is an interesting development. For example, you can ask an agent to create relationships between tables, define dimensions, or modify existing measures.

But an AI agent does not automatically know what your data means. What exactly does a particular order status mean? When can a field be used, and when should it not be used? That information is often not stored in the database.

AI can speed up the technical work, but the business knowledge still has to come from somewhere. We previously discussed the role of a semantic layer in AI in our article about the semantic layer as an API for AI.

Clear rules are particularly important when multiple developers and AI agents are working on the same semantic model. Who is allowed to modify definitions, what instructions does the AI agent receive, and how do we review the changes? Without those agreements, you risk losing the very consistency you are trying to achieve with a centralized semantic model.

In our view, semantic models are the most important development from October. Not only because they enable reuse across applications, but also because they provide a foundation for AI applications that work with the same business definitions and rules as your dashboards.

Qlik Code: developing in VS Code

The new Qlik Code extension lets you develop and manage Qlik load scripts directly from Visual Studio Code.

For developers who already use VS Code, this is a welcome improvement. You can use the editor you are familiar with, manage scripts more easily with Git version control, and use AI tools such as GitHub Copilot or Claude during development.

Several of our consultants have now tested the extension and are enthusiastic about how it works.

However, there is an important caveat. Qlik Code currently supports only the load script. Other parts of an application, such as measures, dimensions, and visualizations, are not yet included.

Full version control for Qlik applications has, of course, been possible for some time with other solutions. But if you manage your scripts through VS Code and Git while handling the other application objects through a different route, you risk fragmenting the development process.

So the question is not only whether Qlik Code is easy to work with, but also how it fits into your existing development workflow.

For teams that work primarily with scripts, the extension offers immediate benefits. For organizations with a mature development and deployment process, it makes sense to first assess how this new way of working fits into that process.

Data preparation becomes more accessible

Qlik also continues to develop its data preparation capabilities.

Data Flow now supports manual data entry and a Map Fields processor. This allows you, for example, to create reference tables and map codes to readable descriptions without writing scripts.

In addition, Table Recipe now lets you view the results after each processing step and give steps recognizable names.

Our consultants are positive about these features, although experienced Qlik developers often still prefer to write scripts themselves.

The main benefit is accessibility. Not everyone who wants to perform a simple data transformation needs to learn Qlik scripting.

Want to know how Data Flow works and when it is better to use Qlik scripting? In our article Qlik Data Flow: Simplifying Data Transformation Without Code, we discuss its capabilities and limitations.

For organizations where the heavy transformation work already takes place in another data platform, such as TimeXtender, these are useful additions.

Direct Access gateway 1.8.1: keep your environment up to date

Not every important update introduces new functionality.

In addition to enhancements for container deployments, the new Direct Access gateway 1.8.1 also includes several bug fixes.

One of these deserves extra attention: Qlik has fixed an issue where overlapping reload requests could, in rare cases, lead to silent data loss. This is a particularly troublesome category of errors because you may not immediately notice that data is missing.

According to Qlik, this issue occurred only in exceptional circumstances. So there is no reason to panic, but it is a useful reminder of the importance of systematic gateway management.

In practice, we regularly see gateway updates being postponed. The gateway runs on the customer’s infrastructure, but it is not always clear who is responsible for maintenance. Agree on who owns this and make sure updates are installed on time. CloudScan for Qlik checks your gateway versions daily and flags when an update is needed.

What are our key takeaways from October?

Qlik continues to invest heavily in Data Products and AI. Semantic models make it easier to centrally manage and reuse definitions, both in dashboards and AI applications.

In our view, this is the most important development this month, especially for organizations with multiple Qlik applications or plans to use Qlik Answers.

Qlik Code is also a valuable step forward for developers, although you need to carefully consider how it fits into your existing development process.

Meanwhile, it remains important to keep an eye on the less exciting updates too. A gateway update may not make the front page, but it can certainly affect the reliability of your environment.

Not every new feature needs to be implemented immediately. But October brought several developments that are worth exploring further.

Want to get more from your Qlik environment?