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Recent Blog Posts
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Agentic Data Engineering: Building the Nervous System of the Intelligent Enterpr...
For two decades, the job of a data engineer was deceptively simple to describe: move data from system A to system B so that someone downstream could a... Show MoreFor two decades, the job of a data engineer was deceptively simple to describe: move data from system A to system B so that someone downstream could analyze it. That era is ending. As agentic AI moves out of the lab and into core operations, a new and far more demanding consumer has arrived at the table — the autonomous agent. And agents are unforgiving about the data they are fed.
Welcome to agentic data engineering: the practice of both using and building agentic AI capabilities to deliver trusted data for AI workloads. It marks a shift from building pipelines by hand to orchestrating self-sustaining systems — and it changes what the modern data team is measured on.
The Great Reset in Data Engineering
In 2026, agentic AI is crossing the line from experimental pilots to operational infrastructure, and that crossing is redefining the data engineering role itself. Several forces are converging at once.
- Coding agents have swung the pendulum back to code. Tools like Anthropic's Claude Code make pipeline creation, data-quality cleansing, and workload automation dramatically faster — enough that the productivity gain outweighs the cost of managing a codebase again.
- The complexity wall has stalled manual workflows. Fragmented stacks and multi-SaaS sprawl have made hand-mapping every process prohibitively expensive. Agents are being adopted precisely because they can navigate that complexity where rules-based systems stall.
- Standards like MCP have made context portable. The Model Context Protocol gives teams a standardized way to feed enterprise data and context into agents and homegrown agentic systems — turning integration from bespoke plumbing into a repeatable pattern.
The net effect is a market shifting from pipeline building to system orchestration. Data engineers are no longer just moving data from A to B; they are building the semantic layers and feedback loops that agents depend on. The value of the role is no longer measured by the ability to deliver data, but by the architecture, context, and guardrails that let swarms of agents operate reliably and securely. In short, the data engineer is becoming the conductor of an agent orchestra.
This is not a threat to the profession — it is an expansion of it. The mundane, repetitive work that consumed so many engineering hours is exactly what agents handle well, freeing skilled engineers to focus on the design decisions only humans can make. Qlik has spent years at the forefront of data engineering productivity, recognized as a longstanding leader across the Gartner Magic Quadrants for Data Integration Tools and Augmented Data Quality Solutions. We see this shift not as a disruption to defend against, but as the natural next chapter.
What changes for the data engineer
From hands-on-keyboard pipeline work to designing context, architecture, and guardrails.
Less gritty manual toil, more time on the work that actually drives business value.
Figure 1. The agentic data engineering stack — from raw enterprise sources through build, trust, and serving layers that feed both human and AI consumers.
What “Trusted Data” Means When the Consumer Is an Agent
When a human reads a dashboard, they bring judgment — they can sense when a number looks wrong. An agent has no such instinct. It acts on what it is given, at machine speed. That raises the bar for what trusted data has to mean. Success in the agentic era looks like:
- Real-time freshness. Event streams replace batch jobs so agents reason on current state, not yesterday's snapshot.
- Agent-ready semantic context. Living metadata — provenance, business logic, quality scores — delivered directly to the consuming agent. Agents don't need tables; they need context.
- Verifiable provenance. Fully governed lineage that lets the business audit exactly how a piece of data shaped an agentic decision.
Get this wrong and the failure modes are equally specific: latency-induced inaccuracy, where agents decide on stale data; the context gap, where clean-looking data lacks the semantics agents need and produces “informed hallucinations”; and agent sprawl, where unoptimized, agent-triggered queries quietly run up the cloud bill. Trusted data, governed context, and cost guardrails are no longer nice-to-haves — they are the cost of entry.
How Qlik Talend Cloud Delivers It
Qlik's approach rests on three ideas working together: intent-driven engineering, autonomous operations, and AI-ready data. Each maps to a real capability shipping as part of our agentic data engineering release.
Intent-driven engineering
Rather than dragging components on a canvas or dropping out to scripts to join dozens of tables, engineers describe the outcome they want and let agents do the work. With declarative data pipelines, you can build and update Qlik pipelines in natural language from the IDE and coding agent of your choice — Claude Code, GitHub Copilot, and more. Five new data agents — Data Product, Data Quality, Catalog, and Glossary, with a Pipeline Agent in preview for H2 2026 — extend Qlik's agentic experience from analytics teams to the data engineering team itself.
Autonomous operations
Building the pipeline is only half the battle; keeping it alive is the other. Consider a familiar scenario at a large financial services firm. A finance director flags that the P&L report shows unexpected numbers. In the old world, that triggered hours — sometimes days — of manual investigation: tracing queries, combing logs, hunting down dataset owners.
With integrated, continuously updated metadata, that picture changes. The on-call engineer uses natural language to see end-to-end lineage immediately, tracing the issue from the report back to its source. Observability alerts enriched with historical context flag the anomaly, and governance teams instantly see which other reports are affected. What once took days can be resolved in under an hour.
Figure 2. Lineage-driven incident response — active metadata provides the lineage to find the problem, the context to understand it, and the clarity to fix it fast.
AI-ready data
In the age of AI, “garbage in, garbage out” is no longer a warning — it is a business risk. Data must be systematically prepared, scored, and governed to meet the needs of every AI project. Qlik treats data quality and governance as an integral part of the platform, not an afterthought: profiling, quality rules, lineage, and the Qlik Trust Score are built in. Agentic data stewardship (preview) adds a human-in-the-loop layer in which agents proactively identify, profile, and recommend remediations, while human stewards approve or refine the action — the first step toward an agentic data quality, governance, and observability ecosystem.
Where to Start
The fastest way to see this in practice is to pick one painful, well-understood pipeline and rebuild it the agentic way. A focused pilot makes the value concrete:
- Choose a high-friction pipeline. One your team already knows is brittle, slow to debug, or expensive to maintain.
- Rebuild it with declarative pipelines and CDC. Describe the outcome in natural language; ingest in real time rather than batch.
- Turn on active metadata and a Trust Score. Measure mean-time-to-recovery on a deliberately injected issue, before and after.
- Expose the result through the Qlik MCP server. Let an agent consume the curated data product and verify it acts on trusted, governed inputs.
The future of data isn't about having more of it; it's about how intelligently it moves and presents itself for agentic consumption. Agentic data engineering is how you scale human expertise to match the speed of modern business. With Qlik, you don't just build data pipelines — you architect the nervous system of the intelligent enterprise.
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Qlik Automate Automation ownership changes for Tenant Admin Automations scope Ju...
Qlik is introducing a change in which automation permissions are included in the Tenant Admin Automations scope. When is the change being introduced... Show MoreQlik is introducing a change in which automation permissions are included in the Tenant Admin Automations scope.
When is the change being introduced?
The change is being rolled out in early July 2026.
What does that mean for me?
Anyone assigned the Tenant Admin Automations scope in a custom role will be able to claim ownership of another user's automation. After claiming ownership, they can make necessary changes to it and enable the automation. However, they can no longer transfer ownership to another user.
The default Tenant Admin role will not be impacted. Tenant admins can still transfer ownership to any user with the appropriate access rights in the tenant.
How do I claim ownership of an automation?
As an Analytics admin, to claim ownership of an automation:
- Navigate to the Automations section in the Administration Console
- Locate the automation you want to claim ownership of, and click the Actions menu (...)
- Choose Claim ownership
This behavior change only applies to the Tenant Admin Automations scope when it is added to a custom role. Tenant admins can still transfer ownership to any user with the appropriate access rights in the tenant.
If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat.
Thank you for choosing Qlik,
Qlik Support -
Connect All the Things: Qlik Talend Cloud's Latest Connector Releases - and a Mi...
Back in 1985, Tears for Fears sang "Everybody Wants to Rule the World." They weren't thinking about data integration pipelines - but they might as wel... Show MoreBack in 1985, Tears for Fears sang "Everybody Wants to Rule the World." They weren't thinking about data integration pipelines - but they might as well have been. Because if you're a data engineer or an analytics leader in 2026, it’s not world domination you are after; it's domination of your data world: you want all your data, wherever it lives, moving wherever it needs to go.
This round of latest connector releases takes us a meaningful step closer to that.
What's New
We’ve been hard at work natively integrating Stitch's full SaaS connector catalogue into Qlik Talend Cloud, giving you access to 100+ pre-built connectors for popular sources including Salesforce, Jira, Marketo, Zendesk, and more.
Since May, we have released twelve more Stitch-powered connectors in Qlik Talend Cloud, some new and some improved, covering a broad sweep of the modern SaaS stack. Whether your data lives in a DevOps pipeline, an e-commerce platform, a marketing automation tool, or a customer engagement suite, there's something here for you.
The additional Stitch-powered connectors are:
- Circle CI - continuous integration and delivery pipelines
- NetSuite - ERP and financial management
- SendGrid - email delivery and engagement
- Shopify - e-commerce platform
- Contentful - headless CMS
- Delighted - customer experience and NPS
- Iterable - cross-channel marketing
- Branch - mobile attribution and linking
- Segment - customer data platform
- Microsoft Graph - Microsoft 365 data and services
- Microsoft Teams - collaboration and communications
- Sendwithus - transactional email
That's a wide net. From pulling CI/CD telemetry out of Circle CI to surfacing customer sentiment from Delighted, to unifying your Microsoft 365 data through MS Graph - the range reflects just how distributed the modern data landscape has become. Remember the saying “there’s an app for that”. Well, think of Qlik Talend Cloud when you want to move data from 'that app'.
The Milestone That Actually Matters
Here's where it gets interesting - and I'd argue this is the more important story with this update.
July 1st is an important milestone in Qlik Talend Cloud. This update is not just about extending the connector list. It also completes something we’ve been working on for a while. Because now All of the connectors, including every previous Stitch-originated SaaS connector that is available in Qlik Talend Cloud today and in the future, will support the full range of data movement use cases: Replication, Pipelines, and Qlik Open Lakehouse.
Previously, Stitch-based connectors were limited to the Replication use case. That was fine as far as it went, as that was exactly the use case Stitch was built for - but it left a gap in today's modern world. If you wanted to route data into a pipeline or land it directly into an open lakehouse architecture with iceberg tables, these connectors weren't available for the job.
That gap is now bridged.
What does that mean in practice? It means that whatever architectural path you're on - whether you're running straightforward replication into a warehouse, building out event-driven pipelines, or building a modern data architecture on open formats like Iceberg and open standards with Qlik Open Lakehouse - your Stitch-powered connectors are ready to travel the whole journey with you. No asterisks. No "supported for this use case only."
Why Stitch Connectors in Qlik Talend Cloud?
A quick word on the "under the hood" picture, for anyone coming to this fresh.
Stitch is a SaaS-native data ingestion technology from Qlik’s Talend acquisition, purpose-built for cloud-based source connectivity. The connectors delivered through this program are taking the best of Stitch technology and making it better in Qlik Talend Cloud, designed to connect to the APIs of modern SaaS applications - the kind of tools that power marketing, sales, operations, development, and finance teams - and bring that data into your data movement infrastructure securely, reliably and at scale.
The connectors are delivered continuously in the cloud, so you don't have to wait for a software release cycle to access new sources. When a new connector ships, it's available in your source and targets list when building your projects. These twelve are the latest in a steady cadence that's been building the breadth of Qlik's SaaS connectivity for some time now.
What's Next
The cadence continues. For a complete list of connectors, check out the Connector Factory on Qlik.com and our documentation in Qlik Help. These sites will be continuously updated as new connectors are released.
If you're looking for a specific connector that isn't listed on the Qlik Connector Factory, then you can request it by clicking the request button on the bottom of that page.
The best place to track what's coming - and to register your ideas to help shape Qlik - is the Qlik Community Ideation Forum.
If you're ready to start moving data from any of these latest new connectors, the Qlik Talend Cloud documentation has everything you need to get set up.
Qlik Help: Qlik Talend Cloud Connectors →
Not tried Qlik Talend Cloud yet? You should. Try for free here
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Qlik Talend Studio 2026-06 will move from Java 17 to Java 21
To all Talend customers, This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studi... Show MoreTo all Talend customers,
This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studio starting with the 2026-06 release.
This change only concerns the Talend Studio desktop application. It has no impact on the Jobs and Services you build with it, which will continue being compliant with Java 17 runtime environments (and Java 8 for Big Data Jobs).
You have multiple options to make a smooth transition:
- The Talend Studio embedded upgrade mechanism (Feature Manager) will offer handling the JDK version upgrade
- Should you prefer, a new installer of Talend Studio 2026-06 with JDK 21 embedded will also be provided
- You will also be free to manually configure Talend Studio to start with JDK 21, provided you are on Talend Studio 2025-09 or higher
Additional details:
Along with this change, the June release will come with many additions from a Java version perspective:
- Talend Studio will allow building Jobs and Services for Java 21 runtimes, in addition to Java 17 runtimes. Java 17 will remain the default setup for runtimes for the time being
- Remote Engines, Cloud Engines, Dynamic Engines, Job Server, and Talend Runtime will support running Jobs and Services in Java 21, in addition to the Java versions they already support
- Talend Administration Center (TAC), Data Preparation (client-managed), Data Stewardship (client-managed), Talend SAP RFC Server, Talend Semantic Dictionary, and Talend Identity and Access Management will also support running with Java 21 in addition to Java 17
For further questions, please start a chat with us to contact Qlik Support and subscribe to the Support Blog for future updates.
Thank you for choosing Qlik,
Qlik Support -
Introducing Agentic Data Engineering and extending Agentic Analytics in Qlik
June 30th brings two significant launches: an extended set of capabilities of the Agentic Analytics in Qlik, along with the introduction of Agentic Da... Show MoreJune 30th brings two significant launches: an extended set of capabilities of the Agentic Analytics in Qlik, along with the introduction of Agentic Data Engineering.
Here is what's new
Agentic Analytics
We are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effective action.
This launch builds on the agentic experience we introduced earlier this year and continues our vision of Qlik as the trusted intelligence layer for AI, helping you move beyond analytics consumption toward a system where AI reasons, predicts, acts, and assists, all grounded in trusted data and powered by the Qlik analytics engine.
What you'll find:
- Drive action from insight with the Automate Agent
- See what’s likely to happen with the Predict Agent
- Get faster, richer answers from Qlik Answers
- Unlock powerful insight through MCP with new advanced reasoning tools
Read more in Introducing the Next Evolution of Agentic Analytics in Qlik | Innovation Blog.
Agentic Data Engineering
Agentic Data Engineering closes the gap between ambition and AI-ready data by improving developer efficiency and productivity through AI assistance and automation.
There are five headline capabilities in this release. Each one is useful on its own, but they are designed to compound as a productivity force multiplier when used together:
- Qlik’s Agentic Experience – Qlik’s natural language AI assistant is now available to data users.
- Catalog and Business Glossary Agent - Automated, always-current documentation and discovery
- Data Quality Agent - Natural-language data observability and quality rule creation.
- Data Product Agent - Conversational lifecycle management for data products.
- Declarative Pipelines - YAML-based pipelines that your coding agent of choice can read, generate, and update.
Read more in From Intent to Trusted Data: Agentic Data Engineering | Innovation Blog.
Availability
All Agentic Data Engineering capabilities are available today to current Qlik Cloud users, although some Agents require an additional cost depending on the subscription tier. Check with your Qlik Account team for details.
Ready to learn more?
- Introducing the Next Evolution of Agentic Analytics in Qlik | Innovation Blog
- From Intent to Trusted Data: Agentic Data Engineering | Innovation Blog
- What’s new in Qlik Cloud | Qlik Help
Thank you for choosing Qlik,
Qlik Support -
Qlik DataTransfer Deprecation Notice for Q1 2026
Qlik DataTransfer will be officially End-of-Life by the end of Q1 2026. It will be removed from the Product Downloads site later this year and will no... Show MoreQlik DataTransfer will be officially End-of-Life by the end of Q1 2026.
It will be removed from the Product Downloads site later this year and will no longer be available for new installations or upgrades. Qlik will provide support until April 30, 2026.
While Qlik Data Transfer is deprecated and will no longer receive fixes or support starting April 30th, 2026, we expect it to function until November 29th, 2026, at which point it will have reached EOL (End of Life).
To ensure a smooth transition, we recommend you begin utilizing Qlik Data Gateway – Direct Access, the supported alternative.
Note that the initial release of Qlik DataTransfer (November 2024, version 10.4.0) will not work after June 24th, 2025. If you still need to use Qlik DataTransfer beyond June, upgrade to the Service Release version 10.4.4.
How do I get started with Qlik Data Gateway - Direct Access?
We have compiled a list of resources to assist you in adopting Qlik Data Gateway - Direct Access:
- Setup Guide: Qlik Data Gateway – Direct Access
- Walkthrough Video: Qlik Data Gateway – Direct Access
- Troubleshooting Video: STT - Troubleshooting Qlik Data Gateway - Direct Access
Additionally, for those needing feature parity with Qlik DataTransfer, we recommend pairing with the File Connector via Direct Access, REST Connector via Direct Access, and the generic ODBC Connector.
Further Resources:
- Connector Factory Blog: Connector Factory January and February 2025 Releases
- SaaS in 60: File Connector via Direct Access Data Gateway - SaaS in 60
We will share an update later this year with the exact deprecation and end-of-support dates.
For assistance, please contact Qlik Support. Questions on how to contact Qlik Support.
Thank you for choosing Qlik,
Qlik Support -
Introducing the Next Evolution of Agentic Analytics in Qlik
We are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effectiv... Show MoreWe are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effective action.
This launch builds on the agentic experience we introduced earlier this year and continues our vision of Qlik as the trusted intelligence layer for AI, helping you move beyond analytics consumption toward a system where AI reasons, predicts, acts, and assists—grounded in trusted data and powered by the Qlik analytics engine.
Here is what’s new.
Drive Action from Insight with Automate Agent
With the new Automate Agent, you can now use natural language to trigger actions and workflows directly from your analytics experience.
The agent executes workflows across Qlik and downstream systems based on user requests or AI-driven recommendations. It determines the appropriate automation, prompts confirmation when needed, and executes tasks end-to-end.
Instead of stopping at an insight, you can move immediately to execution, helping your teams respond faster and close the loop between analytics and outcomes.
See What’s Likely to Happen with Predict Agent
Our new Predict Agent lets you ask forward-looking questions in plain language and generate predictions without requiring data science expertise.
Built on Qlik Predict, the agent guides you through the full data science lifecycle from understanding the problem, building and validating models, to generating predictions and interpreting results—with full explainability.
Make it easier for more people to use predictive analytics to make proactive and better-informed decisions.
Get Faster, Richer Answers from Qlik Answers
Coming soon
We are introducing several new improvements to your Qlik Answers that improve speed, expand input types, and increase control over AI interpretation.
Fast Mode:
Get faster answers for more simple questions.
Fast mode will deliver concise, low-latency responses for quick exploring and faster-paced workflows while still benefiting from the accuracy and trust of the Qlik analytics engine.
Multimodal Capability:
Qlik Answers will be able to read images, charts, and documents.
By understanding and reasoning over visual content, Qlik Answers will unlock insights from a wider range of business materials, reducing manual effort of interpreting non-text data.
Semantic Customization:
Provide context for your data to guide Qlik Answers.
Customize semantic information by adding definitions for fields and master items directly within the logical model, to give Qlik Answers a better understanding of what your data means. This creates a transparent and correctable process to review, edit, and enrich business definitions and context to prevent misinterpretations before they influence answers or impact users.
Unlock powerful insight through MCP with new advanced reasoning tools
Coming soon
New engine-driven advanced reasoning MCP tools deliver mathematically rigorous analytics directly to AI agents and assistants. These capabilities extend analysis beyond basic querying and enable advanced decision intelligence driven by Qlik’s analytics engine.
- Analyze the Gray: Discover meaningful patterns hidden in excluded data and understand what may be missing from the current view
- Prove Causality: Estimate the causal effect of one variable on another and evaluate alternative scenarios
- Optimize Decisions: Determine the best allocation of resources under real-world constraints
- Simulate Risk: Explore thousands of possible outcomes and understand probabilities and confidence ranges
- Predict Outcomes: Train and deploy machine learning models directly against governed Qlik data
- Explore Alternatives: Re-run history under different conditions to understand the impact of actions not taken
The next frontier in AI won’t be determined by which LLM you choose, but by how much analytical power you have under the hood. These capabilities will enable assistants and agents to reason more effectively using governed data and engine-driven analytics that drive more informed, explainable, and actionable decisions.
The Next Step in Agentic Analytics
With this June release, you can now go further with AI across every stage of decision-making. This helps transform analytics into trusted intelligence your teams can rely on.
Get started and learn more:
- From Intent to Trusted Data: Agentic Data Engineering is Now Generally Available | June 2026 Data Launch Innovation Blog
- What’s new in Qlik Cloud | Qlik Help
- Qlik Delivers Agentic Data Engineering in Qlik Cloud | Qlik Press Release
- Qlik Agentic Data Engineering is Generally Available. Here's What That Actually Means. | Qlik Blog
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From Intent to Trusted Data: Agentic Data Engineering is Now Generally Available
If you build data pipelines for integration or analytics for a living, then you already know that the hard part is not writing code. It is everything ... Show MoreIf you build data pipelines for integration or analytics for a living, then you already know that the hard part is not writing code. It is everything around deploying and maintaining that code: interpreting field meanings, testing optimum data flows, inspecting lineage, ensuring governance, and developing quality rules that keep dashboards honest. Data engineers expend a lot of effort when it's done properly. That's why I'm happy to announce the release of new data engineering capabilities to help you. Qlik’s Agentic Data Engineering features in Qlik Talend Cloud are Generally Available (GA) and focused on making many of those supporting tasks easier and more responsive to the ever increasing demands of the business.
We first shared the idea at Qlik Connect in April, but an agentic vision is just a slide deck until features actually ship. Our new capabilities bring AI assistance to the entire data engineering workflow, not just code generation, and as a result, your data team can move faster from intent to trusted data products. All while leveraging your data domain knowledge and keeping your expertise firmly in the loop.
Note: Plenty of tools can autocomplete a transformation for you. The interesting question is no longer whether an agent can write a pipeline, but whether the data coming out at the other end is one you would stake a decision on. That is the bar we set, and it is the part most demos quietly skip over. Why now?
The single biggest obstacle to AI value is rarely the models themselves. It is the gap between ambition and data readiness. Agents and analytics teams need timely data, consistent meaning, excellent quality, lineage, and policy controls, all at once. Miss any one of those, and the impressive insights your confidently predicted turn into a supremely wrong answer in production.
Agentic Data Engineering closes that gap by improving data engineering developer efficiency through AI assistance and automation, and it does so without asking you to trade away governance to get there. As AI shifts from assisting to acting, the data engineer’s role is shifting too from pipeline builder toward architect of intent, trust, and outcomes.
What’s in the release?
There are five headline capabilities. Each one is useful on its own, but they are designed to compound as a productivity force multiplier when used together.
- Qlik’s Agentic Experience – Qlik’s natural language AI assistant is now available to data users.
- Catalog and Business Glossary Agent - Automated, always-current documentation and discovery
- Data Quality Agent - Natural-language data observability and quality rule creation.
- Data Product Agent - Conversational lifecycle management for data products.
- Declarative Pipelines - YAML-based pipelines that your coding agent of choice can read, generate, and update.
🔗 See the release notes [here]
Declarative Data Pipelines
Data pipeline building for everyone, to deliver trusted data that matters.
This is the one most engineers will reach for first. Declarative Pipelines let you define Qlik Talend Cloud data pipelines as YAML-based code that third-party AI coding agents, such as Anthropic Claude Code or GitHub Copilot, can read, generate, and update on your behalf. You prompt your coding agent of choice from the IDE of your choice. The agent pulls pipeline context from your GitHub repository, generates or modifies the pipeline, allowing you to commit the changes, and refresh your project.
The best thing is that Qlik now gives you the freedom to build pipelines visually, manually, or agentically with any third-party coding agent and any code editor. Agents fetch only the pipeline context they need when they need it, following best-practice, schema-enforced design integrated with GitHub. Competitors may offer similar natural-language pipeline generation, but many lack the integrated data quality and governance of the wider Qlik platform, which is exactly what turns generated pipelines into trusted data products for AI.
Note: Typically a new UI debuts when vendors rollout new features. However, we're meeting data engineers in the IDE and the repos they already use, rather than asking them to pivot to new tools. This is the right call, and the YAML-plus-LLM-plus-GitHub foundation means this fits into the CI/CD process you already trust. Why it matters: The market has moved since the debut of ChatGPT in late 2024, and we are meeting the data engineer where you naturally want to work, in the agentic development environment of your choosing. We are not forcing you into our web UI but rather offering our data expertise and heritage through new agentic tools. Organizations using agentic pipeline development anecdotally report meaningful gains in developer productivity and flexibility, and by redirecting engineering effort from pipeline maintenance towards more strategic work adds extra value.
Watch the demo below to see a quick overview of declarative data pipelines in action:
Catalog and Business Glossary Agent
Manages the lifecycle of catalog and glossary entities so data assets are accurately defined, classified, and discoverable by both humans and AI systems.
I think the Catalog and Business Glossary Agent is going to be one of the most used agents in a data engineer's arsenal. You can ask it to generate a comprehensive glossary for a business domain, or derive one directly from existing data products, datasets, or Qlik application. It auto-generates contextually accurate term definitions, organizes them into category hierarchies, and links them back to the catalog objects they describe. You can search and explore the whole Qlik catalog through the same conversation, and the agent handles the full lifecycle from creation through to deprecation.
The difference from the usual glossary tool is where the definitions come from. They are derived from business domain context, data product structure, field metadata, and app measures and dimensions, rather than hand authored. The terms are automatically linked to the assets they describe and therefore your "documentation" always stays in sync with the data itself instead of rotting from the moment someone renames a column.
Note: Virtually every analytics or data team has a glossary that was lovingly written once and never touched again. Documentation that regenerates from the data, and links itself back to that data, is the only kind that survives contact with a real backlog. Why it matters: The catalog and glossary agent removes the manual burden of documentation, classification, and maintenance, and it provides other AI agents the correct, governed metadata context they need to behave. The business payoff is faster time-to-insight through always-current terminology.
Watch the demo below to see the Catalog and Glossary Agent in action:
Data Quality Agent
Helps anyone explore the data quality of an asset, creates and monitor data quality rules in natural language, so decisions rest on data they can trust.
Describe what good data looks like, and the agent does the rest. It finds the target dataset, checks existing fields and rules to avoid redundancy, generates the rule expressions, and lets you refine them iteratively before anything is written. Rules are only created and bound to fields once you approve them. For a fuller pass, the guided assessment walks an entire dataset field by field, surfacing AI-generated suggestions for you to accept, refine, or skip, then summarizes what it found.
You can also ask for quality status at any time and get trust scores, valid and invalid counts, applied rules, and computation freshness for any dataset. Rules come from natural-language descriptions rather than hand-written expression syntax, the guided workflow makes sure no field is quietly missed, and status, including staleness indicators and disabled rules, is always available through conversation without hunting through separate UI views.
Note: The quiet win here is Qlik Trust Score™. This is a metric that evaluates the reliability and quality of datasets or data products, then provides a single, intuitive score for data trust and AI readiness. Why it matters: The Data Quality Agent removes the technical barrier to quality governance. Anyone who can describe good data can now create and apply rules, which widen participation well beyond specialist roles and gives you faster, more consistent coverage and always-current visibility into quality. Watch the demo below to see the Data Quality Agent in action:
Data Product Agent
Helps teams create and govern trusted, AI-ready data products that are easy to maintain and consume.
This agent manages the full data product lifecycle through conversation: creation, activation, deactivation, and deletion, each with confirmation steps, pre-activation validation, and dependency awareness. For creation it checks products with similar names, collects required fields, suggests optional metadata, and presents a full summary before committing anything. For activation it runs pre-activation checks on required fields, dataset inclusion, and owner assignment, then resolves gaps through dialogue. For deactivation it spells out downstream impact before asking for explicit confirmation, and for deletion it surfaces every known dependency and requires unambiguous sign-off.
Crucially, no operation executes without your explicit confirmation. Missing information is gathered through conversation rather than thrown back as a wall of validation errors, duplicates are caught before creation, downstream impact is always communicated before destructive actions, and failures are explained in plain language with suggested next steps.
Note: “Nothing happens without explicit confirmation” should be table stakes for any agent that can delete things. It is genuinely reassuring to see destructive actions gated behind dependency awareness rather than an optimistic shrug. Why it matters: Data Product lifecycle management means coordinating metadata, ownership, datasets, and consumers. The agent orchestrates that complexity behind a conversational interface, so you get faster creation and activation, a much lower risk of misconfiguration, duplication, or accidental deletion, and governance enforced at every step without extra process overhead.
Watch the demo below to see Data Product Agent in action:
How these capabilities fit together
Used individually, each agent saves time. Used together, they form a loop. The Catalog and Glossary Agent gives every asset consistent meaning. The Data Quality Agent ensures that meaning is backed by data you can trust, with a freshness signal attached. The Data Product Agent packages it all into governed products with their dependencies understood. Declarative Pipelines feed the whole thing from the IDE and code repository your team already maintains. The result is a path from intent to trusted, AI-ready data product that keeps human judgment in the loop at every decision point.
Note: Intent replaces instructions, and intelligence replaces toil. The teams that lean into this will not only ship data faster, but they will also change how their organizations think, decide, and act. The future of data engineering has arrived, and it is agentic! Availability and getting started
All Agentic Data Engineering capabilities are GA today and available to current Qlik Cloud users. Some of the Agents require an additional cost depending on subscription tier, so check with your Qlik administrator. If you want to go deeper, then I’d start with the release notes, then navigate to the new feature documentation. Alternatively contact your account rep or CSM for a personalized demonstration.
🔗Release notes: [here]
🔗Watch the launch webinar: [here]
🔗Book a guided demo with a Qlik Solutions Engineer: [here]
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Model vs. Mastery, Episode 2: When the Beautiful Game Meets AI
Meet the Panel Joining host Adam for this tournament check-in are two guests who bring very different — and very complementary — perspectives to the t... Show MoreMeet the Panel
Joining host Adam for this tournament check-in are two guests who bring very different — and very complementary — perspectives to the table:
- Steve Palmer, a football industry veteran with over 30 years of experience, including his Data Solutions Engineering work with the Premier League
- Nick Magnuson, Qlik's Head of AI, who brings the data science lens to everything football throws at us
JOIN THE CONVERSAION with the panel, and others, on our community forum
Together, they make for a fascinating collision of human intuition and machine intelligence.
So… How's the Model Holding Up?
Spoiler alert: football is doing what football does best — keeping everyone on their toes.
The early group stage threw up some genuine surprises, including a record-breaking number of red cards in a single game that no model could have seen coming. As Steve puts it, those early outlier results tested the predictions, but the group stage format — with three games per team — gives the model room to self-correct and normalize over time.
Nick adds important context around this year's expanded 48-team format. With two-thirds of teams now qualifying for the knockout stage (up from half in previous tournaments), team strategies are shifting in ways that add yet another layer of complexity for any prediction model to navigate.
The Real Challenge: The Knockout Stage
Both Steve and Nick agree — the real test for AI prediction is still to come. In the group stage, there's room for recovery. In the knockout rounds, it's winner takes all. One upset, one red card, one moment of magic from an unexpected player, and the model is back to square one.
That's what makes this experiment so compelling. It's not just about whether the AI gets it right — it's about understanding where and why it gets it wrong, and what that tells us about the limits of data-driven forecasting in a sport defined by human unpredictability.
The Wisdom of the Crowd
One of the highlights of the episode is a peek behind the curtain at Qlik's Choose Your Champion app, where over 330 people have submitted their own World Cup brackets. The crowd's favorites? Spain, France, and Brazil — a fairly conventional set of picks that will be fascinating to measure against the model as the tournament progresses.
There's also some fun analysis around national bias — how likely are fans to pick their own country to win? (England fans, it turns out, are more realistic than you might expect. The French, less so.)
Why This Matters Beyond Football
At its heart, Model vs. Mastery is about something bigger than the World Cup. It's about the relationship between AI and human expertise — when to trust the data, when to trust your gut, and how the two can work together to make smarter decisions.
Whether you're a football fan, a data professional, or just someone curious about how AI performs under pressure, this episode has something for you.
Don't Miss Out — Update Your Bracket!
If you haven't joined the Choose Your Champion competition yet, it's not too late. Head to the app, plot your path to the final, and see how your predictions stack up against both the AI model and the wisdom of the crowd. A team jersey from Adidas is up for grabs for our top prediction champion — not a bad prize for getting your football picks right.
And if you've already submitted your bracket, now is the time to revisit your knockout stage picks and make sure you're backing teams that have actually made it through.
Episode 2 of Model vs. Mastery is available now. Catch up on Episode 1 if you haven't already, and stay tuned for our final episode where we'll find out once and for all — did the model win, or did human mastery prevail?
The beautiful game. The power of data. Let's see who comes out on top.
Watch Episode 2 “Inside the Tournament” and create or update your bracket today!
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【オンデマンド配信】AI 時代に求められる行政データの品質管理
行政データの二次利用が進む中、AI 活用や官民データ連携の推進において、データ品質の確保は一層重要性を増しています。他方、限られた人員の中で品質を維持しつつデータ整備を進めることは、多くの自治体に共通する課題です。本 Web セミナーでは、データ流通に関わる国の動向や法改正などを踏まえ、利用可能なデ... Show More行政データの二次利用が進む中、AI 活用や官民データ連携の推進において、データ品質の確保は一層重要性を増しています。他方、限られた人員の中で品質を維持しつつデータ整備を進めることは、多くの自治体に共通する課題です。
本 Web セミナーでは、データ流通に関わる国の動向や法改正などを踏まえ、利用可能なデータを整備するための品質管理・標準化・自動化の要点を整理します。併せて、データ品質管理、データ変換などの作業を自動化できるソリューションをご紹介し、行政 DX を着実に前進させるための具体的な取り組みの方向性を提示します。※ パソコン・タブレット・スマートフォンで、どこからでもご視聴いただけます。
今すぐ視聴する -
Knowledge Nuggets-2026Q2
Knowledge Nuggets-2026Q2Insight ConsultingA collection of Qlik and Data & AI Literacy related videos and articles, updated to Jun 2026DiscoveriesContr... Show MoreKnowledge Nuggets-2026Q2Insight Consulting
A collection of Qlik and Data & AI Literacy related videos and articles, updated to Jun 2026
Discoveries
Contributors and Keywords
Impact
Learning resource
Audience
All Qlik stakeholders
Data and advanced analytics
Learning resource
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The Human Skills AI Can't Replace: Why Students Should Embrace AI, Not Fear It
The answer isn't as simple as yes or no. While AI is changing the way we work, it isn't replacing the need for human talent. Instead, it's changing th... Show MoreThe answer isn't as simple as yes or no. While AI is changing the way we work, it isn't replacing the need for human talent. Instead, it's changing the skills employers value most. The students who succeed in the future won't be the ones who avoid AI—they'll be the ones who learn how to use it effectively.The Skills AI Can't ReplaceAs AI becomes more capable, human skills become even more valuable.Critical thinking helps professionals determine whether AI-generated information is accurate or biased. Creativity allows people to develop innovative ideas that go beyond patterns AI has already learned. Ethical decision-making ensures technology is used responsibly and fairly.Perhaps most importantly, AI cannot replace human judgment. Businesses still need people who can understand context, collaborate with others, communicate effectively, and make decisions that consider both data and real-world consequences.These are the skills employers consistently seek—and they're becoming even more important in an AI-powered workplace.Learn AI Now—Before You GraduateStudents don't need to wait until they enter the workforce to start building AI skills. In fact, college is the perfect time to experiment with new technologies.Use AI to brainstorm ideas, summarize research, explore coding concepts, or analyze datasets. At the same time, challenge yourself to verify its responses, ask better questions, and think critically about the results. Learning how to use AI responsibly today will prepare you for the tools you'll encounter in nearly every profession tomorrow.Why Analytics Matters More Than EverAI depends on data. If the data is incomplete, inaccurate, or biased, the results can be misleading.That's why understanding analytics and data literacy is becoming one of the most valuable career skills students can develop. Professionals who know how to interpret data, identify trends, and evaluate AI-generated insights will be in high demand across industries.Rather than replacing analytics, AI makes analytics even more essential.Building Future-Ready Skills with the Qlik Academic ProgramThe Qlik Academic Program helps students prepare for this new era of work by providing free access to analytics and AI learning resources, Qlik products and qualifications.Students gain hands-on experience working with real data while learning how analytics and AI work together to solve business challenges. These practical skills help graduates build confidence and demonstrate to employers that they're ready to contribute in an AI-driven workplace.Don't Fear the Future—Help Shape ItThe graduates who thrive won't be those competing against AI—they'll be the ones who know how to partner with it. By developing strong analytical thinking, embracing new technology, and continuing to learn, today's students can prepare for careers that are not only resilient but full of opportunity. -
Model vs. Mastery – Episode 2: Inside the Tournament
In this special mid-tournament edition, host Adam is joined by Steve Palmer from the Premier League and Qlik's Head of AI, Nick Magnuson, to take stoc... Show MoreIn this special mid-tournament edition, host Adam is joined by Steve Palmer from the Premier League and Qlik's Head of AI, Nick Magnuson, to take stock of the 2026 FIFA World Cup so far.
The trio revisits the bracket predictions made in Episode 1, reflecting on early surprises and examining how red cards, tactical adjustments, and the expanded 48-team format have challenged the accuracy of Qlik's AI prediction model. They explore the balance between data-driven forecasting and football's inherent unpredictability, while also discussing how real-time data integration could reshape in-match analysis and decision-making.
The episode also highlights the collective wisdom of more than 330 user-submitted brackets in Qlik's "Choose Your Champion" app, where Spain, France, and Brazil have emerged as the leading fan favorites. As the knockout stage approaches, listeners are encouraged to revisit their own predictions and make their picks before the next round begins.
Watch Episode 2 “Inside the Tournament” and create or update your bracket today!
Meet the Panel
Joining host Adam for this tournament check-in are two guests who bring very different — and very complementary — perspectives to the table:
- Steve Palmer, a football industry veteran with over 30 years of experience, including his Data Solutions Engineering work with the Premier League
- Nick Magnuson, Qlik's Head of AI, who brings the data science lens to everything football throws at us
Together, they make for a fascinating collision of human intuition and machine intelligence.
JOIN THE CONVERSATION: with the panel, and others, on our dedicated community forum!
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You Spoke, We Listened Hub
Each month, we publish new updates showing how YOUR feedback is directly reflected in product changes, enhancements, and roadmap decisions. 🔦 J... Show MoreEach month, we publish new updates showing how YOUR feedback is directly reflected in product changes, enhancements, and roadmap decisions.
🔦 June's Spotlight Feature: Configurable Session Timeouts
What We've Heard You Say
We heard customers say they want more flexibility around session management in Qlik Cloud and admins wanted a way to configure authentication session timeout settings.
What Qlik Did About It
We heard you and delivered. Admins now have the controls to configure it under Settings -> Tenant -> Session timeouts.
To give you more clarity and understanding of session handling in Qlik Cloud, it's helpful to know that different types of sessions exist across the platform:
Authentication Session Settings:
These settings determine when users need to sign in again through their identity provider (IdP):
Inactivity Timeout
- How long Qlik Cloud waits before requiring re-authentication after inactivity
- Default is 30 minutes, adjustable from 15 minutes up to 24 hours.
Maximum Session Duration
- The maximum lifetime of an authenticated session before re-authentication is required, regardless of activity.
- Default is 24 hours, adjustable up to 7 days.
Qlik Sense Engine Session:
This is separate from authentication and relates to engine memory management, not user sign-in.
Engine Inactivity Timeout
- How long a Qlik Sense app remains loaded in memory during inactivity.
- Default is 30 minutes, currently not configurable.
If you'd like to explore the feature in more detail, find details in Qlik Help.
What's In It for You
Admins, this one's for you. This enhancement gives organizations more flexibility to balance user experience and security requirements based on their own policies and workflows.
Changes apply to new sessions only, so existing users do not get interrupted mid-work.
This update was featured in our LinkedIn 'You Spoke, We Listened' carousel.
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Qlik アプリで「FIFA ワールドカップ 2026」をもっと楽しく観戦！
史上最大 48 ヶ国が熱い戦いを広げている「FIFA ワールドカップ 2026」の勝敗の行方を Qlik の Web アプリ「Choose Your Champion 2026」でお楽しみください！Qlik Predict® を使用した AI による試合予測、過去のワールドカップデータの探索ができる... Show More史上最大 48 ヶ国が熱い戦いを広げている「FIFA ワールドカップ 2026」の勝敗の行方を Qlik の Web アプリ「Choose Your Champion 2026」でお楽しみください！Qlik Predict® を使用した AI による試合予測、過去のワールドカップデータの探索ができるだけでなく、大会の進行に合わせてリーダーボードで他のユーザーと競い合うことができます。アプリのデータモデルと過去データの分析には Qlik Cloud Analytics®、試合予測には Qlik Predict、そして React のフロントエンドとの連携にはさまざまな Qlik API を活用しています。
アプリの4つの機能
「Choose Your Champion 2026」は 4 つのパートに分かれています。
- ブラケットを作る
グループステージの勝者を選び、ノックアウトラウンドでチームを勝ち上がらせ、最終的な優勝チームを決定します。 -
予測を確認する
大会で起こりうるすべての対戦カードについて、Qlik Predict が生成した各チームの勝率と引き分け率を表示します。どちらのチームを選ぶか迷ったときは、予測を参考にして判断できます。 - 過去のワールドカップデータを探索する
歴代大会のデータをさまざまなビジュアライゼーションで深掘りできます。得点、得点王、開催国のパフォーマンス、最大の番狂わせなど、すべて Qlik の連想エンジンで動いています。
- リーダーボード
実際の試合が行われると、提出されたブラケットは自動的に採点され、プレイヤーはリーダーボードにランキングされます。
Web アプリについて
Web アプリは React のフロントエンドで、@qlik/api を使って Qlik テナントに匿名アクセスで接続しています。ユーザーはログインやテナントへの認証も不要です。ブラケット UI は Qlik Sense® のデータモデルから予測データを取得しているため、ユーザーが対戦カードを開くたびに、Qlik から直接データが表示されます。
過去のワールドカップセクションでは、手軽に使えるチャートが必要な場合は @qlik/embed コンポーネントを、アプリのデザインに合わせてスタイルをより細かくコントロールしたい場合はカスタムの nebula.js + picasso.js ビジュアライゼーションを使い分けました。どちらのアプローチも同じ Qlik Cloud Analytics アプリを基盤としているため、すべてが 1 ヶ所で一貫して管理されています。
詳しい予測の仕組みはこちらをご参考ください。決勝トーナメントにコマを進めるのはどのチームなのか？先日のチュニジア戦では、W 杯 史上最速先制ゴール ＆ 史上最多得点という圧勝で、日本中が歓喜に包まれました。がんばれ、サムライジャパン！
今すぐ Web アプリにアクセス - ブラケットを作る
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Qlik Talend Cloud Dynamic Engine: A technical overview
Who is this For? If you're a data engineer, platform architect, or on a DevOps team evaluating whether Qlik Talend Cloud's Dynamic Engine is the right... Show MoreWho is this For?
If you're a data engineer, platform architect, or on a DevOps team evaluating whether Qlik Talend Cloud's Dynamic Engine is the right move, or you're already committed and working through implementation, then this article is written for you. It covers architecture, configuration, and the operational patterns that matter in practice. Business value gets called out where it's relevant, but this isn't a sales pitch.
Kubernetes familiarity helps with some sections. It's not required to follow the concepts, but you'll get more out of the architecture details if you're comfortable with namespaces, operators, and Helm.
The Challenge with Always-On EnginesFor most of Talend's history, data integration infrastructure followed a familiar and predictable pattern: provision a server or VM, install the Remote Engine runtime, and keep it running. The engine polls Talend Management Console (TMC) for work, processes jobs with limited concurrency, and runs whether it has tasks to execute or not.
For stable, predictable workloads that model is dependable. But for organizations dealing with variable demand, multi-tenant requirements, or teams trying to align their data infrastructure with modern DevOps practices, it creates real friction. You end up paying for compute you're not using, managing a fleet of VMs that need individual attention for upgrades, and hitting concurrency ceilings at exactly the wrong moment.
Dynamic Engine is Qlik's answer to those constraints.
What Dynamic Engine IsThe Dynamic Engine is a Kubernetes-native processing platform that executes Talend workloads like Data Integration jobs, Data Services, and Routes, on customer-controlled infrastructure. TMC and the still handle all management, scheduling, and orchestration. What changes is the execution layer. Instead of a persistent JVM waiting for work, Dynamic Engine provisions isolated containers on demand, runs the job, and releases those resources when it's done.
Three Principles Drive the DesignExecution is on-demand. There are no persistent runtimes to babysit. Each Talend Job, Data Service, or Route gets its own container for the duration of its execution. When the work is done, the container is gone.
Scalability is elastic. During peak processing windows, multiple containers run in parallel. During quiet periods, you're not burning budget on idle infrastructure. The ceiling is your cluster capacity, not a per-engine configuration value.
The design is cloud-agnostic and Dynamic Engine runs on Amazon EKS (including Auto Mode), Azure AKS, Google GKE (including Autopilot), and on-premises Kubernetes today, with Red Hat OpenShift support planned. Your cloud strategy stays yours.
What This Means in Practice:- Cost: Eliminating idle resources can substantially reduce infrastructure costs compared to persistent engine models.
- Velocity: Developers spin up environments for testing without resource conflicts or provisioning delays.
- Reliability: Each job starts with a clean container, eliminating state-related failures common in long-running processes.
- Simplified operations: Infrastructure management is abstracted away, letting engineers focus on pipelines rather than execution environments.
Real-World Use CasesBatch at scale: A retailer processing millions of nightly transactions scales from a handful of containers during the day to hundreds at peak, automatically.
Multi-tenant SaaS: A SaaS provider uses separate environments per customer, ensuring isolation while maintaining centralized monitoring, governance, and FinOps.
Hybrid cloud integration: An enterprise connects on-premises data warehouses to cloud analytics using environments configured with VPN access, maintaining security while leveraging cloud scalability.
Architecture: Two Environments, One Control Plane
Dynamic Engine spans two distinct environments: the Qlik Talend Cloud layer managed by Qlik, and your Kubernetes cluster where execution happens. They communicate over HTTPS, with your cluster always initiating the outbound connection. No inbound firewall rules are required on your cluster for Dynamic Engine.
On the Qlik side, TMC is the user-facing control plane. Through it you define Dynamic Engine entities, manage Dynamic Engine Environments (DEEs), configure Run Profiles, deploy and monitor tasks, and trigger promotions across environments. The Talend Cloud API exposes the same operations programmatically via REST, and it's worth noting this is the same API available for all Talend Cloud interactions and not something specific to Dynamic Engine.
Inside your cluster, two namespaces do the work.
The Two-Namespace Design: Stability Meets ElasticityThis is one of the more important architectural details to understand, because it explains how Dynamic Engine achieves two things that typically work against each other: a stable, persistent control layer and an execution layer that scales up and down freely.
The infrastructure namespace is the fixed foundation. It contains the engine-operator, which is the Kubernetes operator responsible for managing the lifecycle of all Dynamic Engine resources in the cluster, along with the CRDs that extend the Kubernetes API with Dynamic Engine-specific resource types, and the associated RBAC primitives, so the ClusterRoles, RoleBindings, ServiceAccounts, Secrets, and ConfigMaps. This namespace is deployed once per Dynamic Engine instance and its footprint doesn't change with workload.
It also hosts a local container image registry used for Data Service and Route images. When a Data Service or Route task is deployed, an image-builder job builds the task-specific container image and pushes it to this registry, where execution pods pull from it. Batch Data Integration jobs use a different artifact delivery path.
If your organization requires internally managed and secured registries, your own container image registry can be substituted for the in-cluster repository.
The Dynamic Engine Environment namespace is where execution actually happens. Each DEE you create in TMC maps to its own namespace in the cluster, named
qlik-processing-env-<env-id>by default, though this is customizable via Helm. This namespace contains the service pods that enable task execution, the ephemeral job pods running your workloads, and the persistent storage resources supporting them.One Dynamic Engine instance can serve multiple Environment namespaces simultaneously. That one-to-many relationship is what makes it practical to maintain separate dev, test, and UAT environments, or to organize by business unit, like accounting-dev, marketing-uat, all without needing separate clusters or engine deployments.
It's also worth being explicit about something; Dynamic Engine does not require a dedicated Kubernetes cluster. It's also compatible with ARM-based Kubernetes nodes and autopilot flavors. It's designed to run within your existing infrastructure, alongside your existing workloads, using your existing security controls, network configuration, and monitoring setup.
How the Two Layers CommunicateTwo channels connect Qlik Talend Cloud to your cluster.
HTTPS handles provisioning and management. The engine-operator establishes an outbound connection to Talend Cloud services, through which the TMC issues deployment instructions, configuration changes, and version upgrades. All traffic is TLS-encrypted.
ActiveMQ over HTTPS handles Talend job dispatch. Environment namespace service pods use this channel to receive task execution requests and report status back to TMC. The reliable message delivery model means a job submission isn't lost if your cluster is temporarily unreachable. Like the HTTPS channel, this is outbound from your cluster, so no special inbound firewall rules needed.
Air-gapped and Restricted-Network DeploymentsDynamic Engine has been designed with security and risk mitigation in mind. Both channels support air-gapped deployments. Infrastructure Helm charts and required images can be sourced from a local registry, such as Harbor, though cloud provider registries and other commercially supported options are used as well. In this setup, your cluster nodes never pull images from the Internet.
In this example Helm command, you’ll see the domain references the internal registry.
helm install dynamic-engine-crd \
oci://your.internal.registry/talend/helm/dynamic-engine-crdFor TMC communication in restricted environments, an HTTPS proxy can be placed between your cluster and Qlik Talend Cloud services. You can find additional information for configuring the proxy in your Dynamic Engine setup in the documentation.
Dynamic Engine also provides support for Kubernetes Network Policies, allowing you to enable default network policies that are set during your installation or upgrade, and further, you're able to further define custom egress rules that restrict your outbound traffic.
Deploying Dynamic Engine
Once you've created a Dynamic Engine in TMC, deploying it to your cluster uses familiar Helm install, and it exposes the full range of configuration values, supports
helm upgradewith rollback capability, and lets you validate configurations before applying with--dry-run=server.Prerequisites include needing a cluster to be running Kubernetes v1.30–v1.36, and you'll need cluster admin permissions for CRD registration and namespace creation.
One thing worth highlighting is that once Dynamic Engine and its Environments are up and running, data engineers don't need cluster access for day-to-day work. All task deployment, monitoring, and management happens through TMC and the Talend Cloud API. Direct cluster interaction is only needed when adding, removing, or upgrading Dynamic Engine components themselves.
Customizing the Execution EnvironmentDynamic Engine environments support a range of customization through standard Kubernetes resource mounting. ConfigMaps work well for non-sensitive configuration like API endpoints or feature flags. Secrets can be injected as environment variables for credentials, or mounted as volumes when jobs need access to certificates or keystores. PersistentVolumeClaims cover shared storage scenarios where data needs to survive beyond a single pod's lifetime. These patterns can be combined, and they apply independently to Data Integration job pods and Data Service or Route pods, so you have separate control over each execution type.
Run Profiles give data engineers a TMC-native way to tune how jobs run without touching cluster configuration directly. A Run Profile defines the JVM arguments and Kubernetes resource parameters for a given task: CPU and memory requests and limits, replica counts for Data Services and Routes, and autoscaling thresholds if needed. A team running a memory-intensive transformation job can have its own profile with higher pod limits, while lighter workloads share a more constrained default.
Because Run Profiles are managed in TMC, data engineers can adjust execution parameters within the boundaries the platform team has established, without needing cluster access.Talend Run Profiles defined in Talend Management Console allow you to define resource configuration, JVM arguments, and Kubernetes Pod resources, allowing for finer declaration of your configuration.
Upgrades Without the Usual PainUpgrade management is one of the clearest operational advantages Dynamic Engine has over the Remote Engine model, and it's worth walking through how it works.
When a new version is available, TMC surfaces the notification directly in the console. Applying it uses the same manifest or Helm commands as the original deployment. The critical difference from Remote Engine is that running jobs and tasks are not interrupted. Kubernetes' rolling update mechanism brings up new engine pods, validates them, and only then terminates the old ones. The engine's control capabilities remain available throughout.
If something goes wrong during an upgrade, such as a misconfigured values file, an incompatible cluster state, an image pull error, the engine will roll back to the previous working configuration automatically. There's no manual recovery process and no separate rollback procedure to maintain.
The same rolling update behavior applies to your own Data Services and Routes. Deploying an updated version through TMC uses Kubernetes'
RollingUpdatestrategy, keeping your application available as the new version comes up and gets exposed.Monitoring What Matters
TMC is the primary operational window into running workloads. For batch Data Integration jobs, the signals that matter are execution status, duration, and log output. For always-on Data Services and Routes, the primary health indicator is replica status and the desired replica count versus those currently running, which tells you immediately whether your services are healthy.
Fitting Into Your Kubernetes Ecosystem
One of the practical advantages of a Kubernetes-native architecture is that Dynamic Engine and its Environments slot into the tooling your team already uses. Standard Kubernetes-compatible monitoring, logging, telemetry, and egress solutions work without customization. You're not adopting a proprietary observability stack or working around a closed runtime, you're running containers in namespaces, and that means Prometheus, Grafana, Fluentd, your GatewayAPI of choice, and whatever else your platform team has standardized on, all apply.
Logs and metrics can be collected and sent to your logging and operational monitoring systems.
When Dynamic Engine Is the Right Call
The architectural differences between Remote Engine and Dynamic Engine aren't superficial. Across nearly every dimension like runtime model, concurrency, cost, and job isolation, the two runtime engine types reflect fundamentally different assumptions about how data integration infrastructure should work.
Core Architectural DifferencesFeature Remote Engine Dynamic Engine Runtime Model Persistent OSGi container, long-running JVM Kubernetes-native; ephemeral pods/execution Concurrency 3 parallel tasks (default, configurable per engine) Bound by cluster capacity; no per-engine cap Resource Model Always-on; cost is continuous regardless of use On-demand; cost scales with execution time Job Isolation Shared JVM for concurrent tasks Per-pod isolation; namespace per execution Infrastructure Customer VMs (EC2, Azure VM, on-prem) Customer Kubernetes (EKS, AKS, GKE, etc.) Upgrades Manual installation per engine VM TMC-notified; rolling update, rollback on failure Impact to Running Jobs Engine restart affects running jobs Engine upgrades do not affect running jobs OSGi Artifacts Fully supported Not recommended; use Route or Data Service as microservice deployment type Routes / Data Services Supported (OSGi model) Supported (container-image model)
Those differences translate directly into the workload patterns where Dynamic Engine has a clear advantage.Variable demand workloads where nightly batch spikes, end-of-month processing, and event-driven integration, for example, benefit directly from on-demand compute. You stop paying for headroom that you only need occasionally.
Multi-tenant and regulated workloads get genuine namespace-level isolation with Kubernetes RBAC, without the overhead of separate clusters per tenant.
If Kubernetes is already part of your infrastructure, the marginal overhead of adding Dynamic Engine is low. The operational model is already familiar.
When concurrent job volume outgrows what a Remote Engine fleet can reasonably handle, Dynamic Engine's use of Kubernetes' Horizontal Pod Autoscaler is the natural path forward.
And for teams spending meaningful engineering time maintaining execution agents and managing engine upgrades, the TMC-managed upgrade model and automatic rollback free that time up for work that actually moves the needle.
Where This Is Heading
Dynamic Engine reflects a broader direction the data integration industry is moving: toward serverless, consumption-based models where infrastructure costs track actual usage rather than theoretical peak capacity. As organizations continue to mature in their cloud adoption, that kind of flexibility stops being a differentiator and becomes a baseline expectation.
Qlik Talend Cloud's Dynamic Engine gets you there without trading away the reliability and feature depth that enterprise data teams depend on. The architecture is sound, the operational model is genuinely simpler than what it replaces, and it fits into the Kubernetes-native infrastructure patterns most platform teams are already building toward.
Practical Guidance for Getting StartedA few design and deployment patterns that tend to matter in practice:
Start with modest resource configurations and scale based on actual metrics. Over-provisioning environments is easy and expensive, and that's one of the things Dynamic Engine is designed to help you avoid.
Keep environments separated by purpose. Dev, UAT, and production should be distinct DEEs. The namespace isolation is there, so use it to prevent accidental cross-environment interference.
Design Talend jobs for parallelism where it matters. Stateless job design, or managing state externally to ephemeral container storage, lets you fully exploit Dynamic Engine's ability to run multiple instances simultaneously.
Use TMC's monitoring to track execution times, resource utilization, and cost over time, and treat that data as feedback for configuration refinement. The consumption-based cost model rewards this kind of attention.
Wrapping UpThe per-pod isolation and environment-level namespace separation add up to something concrete. Execution environments stay clean and isolated without the overhead of separate clusters per tenant or business unit, and infrastructure absorbs variable demand without manual intervention. For organizations managing multi-tenant workloads or a growing portfolio of data integration pipelines, those properties translate directly into fewer incidents caused by shared runtime state and more predictable operations at scale.
The technical foundation makes that possible without trading away operational control. Your cluster, your security policies, your observability stack. Dynamic Engine fits into the infrastructure you're already running rather than replacing it.
- Cost: Eliminating idle resources can substantially reduce infrastructure costs compared to persistent engine models.
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The AI-Ready Enterprise: What Separates Leaders from Everyone Else
Nearly every enterprise has an AI strategy. Far fewer have an AI-ready enterprise. The difference matters. As AI moves from experimentation to operati... Show MoreNearly every enterprise has an AI strategy. Far fewer have an AI-ready enterprise.
The difference matters. As AI moves from experimentation to operational decision-making, the organizations pulling ahead are not necessarily those with the biggest budgets, the largest data science teams, or access to the newest models. What makes the biggest difference between a promising pilot and a system you can trust in production is Enterprise AI Readiness, and it is also the single biggest predictor of which organizations turn AI ambition into outcomes.
The numbers make the gap hard to ignore. In Qlik's 2025 Agentic AI Study, 97% of large enterprises have funding committed to agentic AI, yet only 18% have fully deployed it. Nearly four in ten are planning investments above $1 million, but 46% say meaningful scale is still three to five years away. Budget is not the constraint. Readiness is.
For data and analytics leaders, that reframes the job. AI readiness is not a one-time project that ends when a use case ships. It is an enterprise must-have capability you build, measure, and keep, in the same way you treat security or reliability. And like those capabilities, it is made of distinct, connected parts that all have to hold.
A framework for what "ready" actually means
It helps to picture readiness as a wheel. At the center sits AI readiness itself. Around it are five dimensions that must work together: Strategy, People, Technology, Data, and Data and AI Governance. Each dimension breaks down further into spokes, and a single weak spoke is usually what trips an otherwise promising program.
The value of looking at readiness this way is that it stops you from over-investing in one area while a neighboring gap quietly undermines it. A brilliant strategy fails on poor data. Pristine data fails without governance. Strong governance fails if people are not trained to act on it. Treating readiness as a connected system, rather than a list of point fixes, is what separates organizations that scale from those that keep restarting pilots. Here is what each dimension demands, and where the work actually lands.
Strategy: plan the effort and define success up front
The strategy dimension is about making informed decisions before you build. Its four spokes are opportunities, objectives, alignment, and governance of the initiative itself. In practice that means choosing the AI use cases worth pursuing, setting measurable objectives, aligning those objectives to business priorities, and deciding how initiatives get approved and funded.
This is where many enterprises look stronger than they are. In Qlik's study, 69% now report a formal AI strategy, up from 37% a year earlier, yet only 19% have a defined framework for measuring return. That gap between having a plan and being able to prove it worked is a strategy problem, and it is the one most likely to erode executive patience. Readiness here means picking opportunities you can instrument, and defining success metrics on day one rather than reverse-engineering them after launch.
For teams that want a structured starting point, the “AI & Data readiness assessment” from Qlik Advisory Services helps prioritize use cases, set objectives, and align AI initiatives to business outcomes before the build begins.
People: build the skills and the mindset
Technology does not adopt itself. The people dimension covers training, talent, ethics, and culture, the human capacity to use AI well and responsibly. Its spokes ask whether your teams have the skills to build and operate AI, whether you can attract and keep the right talent, whether people apply an ethical lens to how AI is used, and whether the culture actually trusts and acts on AI-driven insight.
The data shows why this matters. Lack of internal expertise ranked as the third-largest barrier to deployment, cited by 48% of enterprises, and only 42% express confidence in their internal skills. You can buy a platform that delivers a set of capabilities, but a culture that treats AI outputs as suspect, or worse, accepts them uncritically, will undermine even a well-governed system. Readiness here is less about headcount and more about fluency: enough literacy across the organization that trusted insight gets used, and questionable output gets questioned.
Technology: choose architecture that scales
The Technology dimension is the engine room: infrastructure, security, scalability, and software. The spokes ask whether your architecture can run AI workloads reliably, whether it is secure, whether it scales as demand grows, and whether your tools are efficient enough to operate at production volume rather than just in a demo.
This is where readiness becomes concrete for data and analytics leaders. AI workloads are unforgiving of brittle infrastructure. Pipelines that work for a quarterly report fall over when an agent queries them continuously. An open, scalable foundation matters here, because lock-in and rigid architecture are how technology debt quietly caps your AI ambitions. This is what Qlik's data foundation for AI is built to provide: trusted, open, scalable architecture, including an open lakehouse foundation, so workloads can grow without forcing a re-platforming every time demand steps up.
Data: build a pipeline you can trust
If one dimension decides the outcome, it is this one. The Data dimension covers quality, privacy, strategy, and compliance, and its job is to build a pipeline that ensures accuracy, reliability, and availability for AI. The spokes ask whether your data is accurate and complete, whether sensitive data is protected, whether you have a coherent strategy for sourcing and managing it, and whether it meets regulatory requirements.
This is also where most programs stall. Data quality, availability, and access were the number one barrier in Qlik's study at 56%, with integration second at 49%. Models are only as trustworthy as the data feeding them, and an agent that acts on bad data does not just produce a wrong answer, it takes a wrong action.
This is the core of where Qlik helps. Qlik Talend Cloud brings data integration, data quality, and governance into one environment, so you can move data from across your estate, build pipelines, and apply quality rules without stitching together disconnected tools. The goal is not just faster movement of data. It is delivering data your teams and your models can actually rely on, with accuracy, reliability, and availability built in rather than checked after the fact. Self-service access and interoperability then shorten the path from question to trusted answer, so analysts are not waiting in a central queue to put governed data to work.
Data and AI governance: the framework that holds it together
Governance is drawn as the dimension wrapping the rest, and that placement is deliberate. Its spokes are trust, privacy, ModelOps, and explainability: a framework that ensures AI is developed and used responsibly. It asks whether you can trust your data and models, protect privacy, operate and monitor models over their lifecycle, and explain how decisions get made.
Governance is shifting from a "should we" conversation to a "what did we get, and can we prove it was sound" conversation. As agentic AI starts taking action rather than just summarizing, the cost of ungoverned data rises sharply. This is where Qlik Trust Score for AI earns its place: it gives every data product a visible signal scored across dimensions such as accuracy, timeliness, completeness, and diversity, so teams can inspect readiness before a decision or an automated action depends on it. Paired with data products, anomaly detection, continuous observability, and agent-assisted stewardship, governance stops being a manual gate and becomes an operating standard built into how data is produced and consumed. That is also what lets you reduce risk while you scale, because problems surface before they reach a decision.
Readiness is a muscle, not a milestone
Look at the wheel as a whole and the lesson in the 97%-funded, 18%-deployed gap comes into focus. The funding proves the ambition is real. Closing the distance means strengthening every spoke, and refusing to let a strong strategy mask a weak data foundation or thin governance.
The organizations pulling ahead are not the ones with the largest AI budgets. They are the ones treating readiness as an enduring, measurable capability, investing in trusted data, scalable architecture, and built-in governance as deliberately as they invest in the models themselves. That work spans the organization, but it is anchored in data and analytics, which puts data leaders in the position to lead it.
Want to see where your foundation stands across the wheel? Explore why data and analytics leaders choose Qlik to build AI readiness that scales. Or start with the data dimension directly: try Qlik Talend Cloud free for 14 days and see what trusted, AI-ready data looks like in your own environment.
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Techspert Talks - Qlik Cloud Admin 101
Hi everyone, Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series where you can get first-hand ins... Show MoreHi everyone,
Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series where you can get first-hand insights from Qlik experts.The Techspert Talks session from June looked at Qlik Cloud Admin 101.
But wait, what is it exactly?
Techspert Talks is a free webinar held on a monthly basis, where you can hear directly from Qlik Techsperts on topics that are relevant to Customers and Partners today.In this session we will cover:
- Important settings
- Space and App management
- Users, Groups and Roles
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Qlik Replicate May 2026: Built to Keep Running
There's a moment in Michael Stipe's 1991 classic "Losing My Religion" where he sings "that's me in the corner, that's me in the spotlight." He was tal... Show MoreThere's a moment in Michael Stipe's 1991 classic "Losing My Religion" where he sings "that's me in the corner, that's me in the spotlight." He was talking about the agony of unrequited feeling - but he could just as easily have been describing the quiet, unsung heroism of the enterprise data engineer managing their replication infrastructure. Nobody puts CDC pipelines in the spotlight. They just expect them to work.
They do, of course. Until they don't. And when they don't, then everyone notices.
Qlik Replicate May 2026 is a release that takes that "until they don't" problem seriously. Three new endpoints, sixteen features and enhancements, and five deep-dive items that collectively add up to: less manual intervention, better resilience under real-world conditions, and meaningful expansion of where your data can land. Here's what matters - and why it's worth upgrading.
SAP HANA: two improvements that make life easier
We’re committed to making life as easy as possible for our SAP customers. These two new enhancements address well-known, frequently encountered pain points that, until now, required cumbersome workarounds.
The first is CDC artifact cleanup for Trigger-Based CDC mode. Replicate creates artifacts in the source HANA database during CDC operations - and without a clear cleanup policy outside of Replicate, they can accumulate over time, consuming disk space and adding operational drag. The new deletion policy configuration lets teams define when these artifacts are removed automatically, turning a recurring maintenance task into a set-and-forget configuration (just like our pipelines).
The second is more significant: CDC continuity on dropped triggers. SAP application upgrades frequently drop foreign triggers from HANA databases - it's a known behaviour, and it has always carried a disproportionate consequence. When Replicate detected missing triggers, it fell back to a full table reload. For large tables, that could mean lengthy delays of data outage on a production system, sometimes spanning over days.
The new "Continue CDC on Dropped Triggers" option keeps replication running when triggers are temporarily absent. Best practice is to enable it before anticipated SAP upgrade windows and disable it afterwards - but that's a minor operational step compared to the alternative should the worse happen.
To put the stakes in context: a global healthcare organisation running Trigger-Based CDC on SAP HANA uses Qlik Replicate to process around 5 billion records per quarter during peak pricing cycles, with change volumes reaching 100 million records per hour from a single large table. Qlik Replicate has helped them reduce data latency from ten hours to under 30 minutes. In an environment operating at that scale, a multi-day reload isn't a theoretical inconvenience - it's a commercial event. This enhancement was built with exactly that kind of environment in mind.
Qlik Replicate SAP HANA endpoint config screen
The mainframe gets a modern integration path
The new IBM IMS source endpoint replaces the need for our legacy ARC (Attunity Replicate Connect) extension entirely, and it arrives with the kind of improvements that matter in 2026: modern mTLS security, a minimal z/OS footprint, IMS catalog metadata support, and the ability to run alongside ARC during migration - so there's no forced cutover window, no downtime risk.
With the IMS endpoint now on the same modern footing as the rest of the mainframe connector family, organisations with IMS-resident data have a credible, enterprise-grade path to power analytics and operational use cases at a scale that demands both low latency and continuous availability.
We’d love to hear from organisations using IMS. If you would like to try the new IMS source endpoint, please reach out to Qlik support.
DB2 for LUW: a native endpoint, finally
Fun Fact: IBM DB2 was the first relational database ever built, created in the 1970s. IBM wanted to call it DB1, but the name was already taken by a database developed in Israel. So DB2 it became, and the rest is history, as they say.
DB2 for LUW has been supported by Replicate for a long time; however, not as a native Replicate target endpoint - until this release.
The new IBM DB2 for LUW target endpoint allows data to land directly into Db2 for Linux, Unix and Windows from any Replicate-supported source. Previously, the generic ODBC target endpoint was the only method available to replicate to a Db2 LUW environment. While this route was functional, performance was not optimal, and it did not support some Db2-specific capabilities. The new native endpoint closes that gap with meaningfully better performance and proper support for Db2 data types, including LOBs and XML.
The same global healthcare organisation mentioned above has already identified the DB2 for LUW target as a logical next step in their pipeline evolution - a sign of where this endpoint sits in real enterprise roadmaps.
⚠️Note:⚠️ DB2 LUW 11.1 and 11.5 are now end-of-life in this release. Customers on those versions will need to upgrade to DB2 LUW 12.1 or higher before using the new endpoint.
BigQuery streaming: lower cost, lower latency
The Google Cloud BigQuery target endpoint gains a new loading method in this release: streaming via the Storage Write API, as an alternative to the existing batch loading approach.
The practical benefits are straightforward. Streaming reduces data latency significantly compared to batch loading, lowers cost by eliminating the need to stage files before loading, removes daily quota constraints on load jobs, and guarantees once-only delivery - no duplicate rows on retries. For organisations running near-real-time analytics pipelines into BigQuery, this is a meaningful step up.
The switch is a single dropdown selection in the endpoint settings - Batch Loading or Streaming - and batch loading remains the default for those who prefer to stay with the existing behaviour. As always, it's worth reviewing the documented limitations before switching, as not all table types and workloads are compatible with Streaming mode.
More of what makes Replicate reliable
The headline features get the attention, but some of the most valuable work in any release happens in the details - the fixes and enhancements that mean fewer escalations, fewer manual interventions, and fewer late nights for you.
In this release: MS-CDC source endpoints now activate CDC only on the columns you're actually replicating, rather than all columns regardless of what's included in the task. Tables with encrypted columns or newly added DDL columns - which previously suspended replication and required manual recovery - now handle gracefully and keep running. Microsoft Fabric Mirrored Database loading is more reliable after stop/resume cycles, and now supports schema evolution via ADD COLUMN DDL. MongoDB source adds support for large change stream events and more flexible JSON handling.
A new Cloudera Iceberg target endpoint rounds out the three new additions - enabling full CDC and Full Load replication into Apache Iceberg tables on Cloudera Data Platform without custom connectors, as open table formats continue to cement their place in enterprise lakehouse architectures.
OAuth authentication is now available for Kafka and Confluent Cloud targets. Data type fidelity improves across SQL Server, Databricks, and Microsoft Fabric. And a small but welcome UX improvement: endpoint configuration fields that previously required placing certificate files at specific paths on the Replicate Server machine now include a Browse button - the file is stored securely within the endpoint settings, and the local copy can be deleted. A small change that removes a surprisingly persistent source of friction.
Qlik Replicate May 2026 is a release built on the same principle that makes replication infrastructure worth trusting: it should keep running, whatever the real world throws at it. The mainframe gets a modern IMS integration path. SAP HANA customers get resilience against the trigger-drop reload. DB2 for LUW gets the native endpoint it always deserved. And BigQuery gets faster and cheaper.
Not in the spotlight. Just keeping the data moving.
As always, each new release is fully supported for two years. To check the status of support for your currently installed version, please see the relevant product lifecycle pages.
- Qlik Replicate Product Lifecycle
- Qlik Compose Product Lifecycle
- Qlik Enterprise Manager Product Lifecycle
We hope you enjoy using Qlik Talend Data Integration & Quality products and would love to hear your feedback and success stories, especially in any improvements you achieve.
To get the latest versions, please visit the Downloads and Release Notes section on Qlik Community. To learn more about what is included in these releases, be sure to check out the Release notes, which are available here.
To obtain any of these releases, go to the Qlik Downloads Site in the Community and filter "Product Category" by "Qlik Data Integration", and then select the product and the versions you would like to download. For most products, selecting "Latest release and patch" under the "Show Releases" should be enough. If required, you can filter further by selecting the latest "Release" and/or Service Release (SR) version under "Release Number".
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Qlik Cloud webhooks: Migrate Qlik Automate workflows to CloudEvent format by Oct...
Qlik is removing legacy attributes from webhook payloads on October 6th, 2026. Unless updated to use the new attributes, this change will break webhoo... Show MoreQlik is removing legacy attributes from webhook payloads on October 6th, 2026. Unless updated to use the new attributes, this change will break webhook-triggered automations that rely on the removed fields.
During the transition, some events include both legacy and CloudEvent fields, but this is temporary. Once CloudEvent-only enforcement begins, automations that still rely on legacy fields can fail.
We have created a guide that explains:
- How to identify affected automations,
- How to update the automations,
- How to validate them,
- And return them safely to production.
Read more in Qlik Cloud webhooks: Migrate Qlik Automate workflows to CloudEvent format.
If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat. For other Qlik Automate related questions, head over to the Qlik Automate forums.
Thank you for choosing Qlik,
Qlik Support
