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  Product Innovation

  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 More

  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 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 Engines

  For 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 Is
  

  The 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 Design

  Execution 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 Cases

  Batch 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 Elasticity

  This 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 Communicate

  Two 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 Deployments

  Dynamic 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-crd

  For 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 upgrade with 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 Environment

  Dynamic 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 Pain

  Upgrade 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' RollingUpdate strategy, 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 Differences

  Feature	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 Started

  A 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 Up

  The 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.

   

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  By Rob_Arnhart 2026-06-24 / 04:18 PM

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  Product Innovation

  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 More

  Nearly 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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  By DPinto 2026-06-24 / 01:47 PM

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  Support Updates

  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 More

  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 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

  
  WATCH HERE

  
  
  

   

  

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  By Troy_Raney 2026-06-24 / 01:16 PM

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  Product Innovation

  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 More

  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 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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  By Adam_Mayer 2026-06-24 / 09:14 AM

  0 1 422
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  Support Updates

  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 More

  Qlik is removing legacy attributes from webhook payloads on October 6^th, 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

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  By Sonja_Bauernfeind 2026-06-24 / 08:00 AM

  0 0 680
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  Japan

  Qlik、日本法人の執行役員社長に今野芳弘が就任

  2026年 6月 16日、東京 - データ品質、データ分析、人工知能（AI）のグローバルリーダーであるQlik®は、2026年 6月 16日付で今野 芳弘（こんの よしひろ）が同社の日本法人であるクリックテック･ジャパン株式会社の執行役員社長に就任したことを発表しました。今野は、40 年以上にわた... Show More

  

  2026年 6月 16日、東京 - データ品質、データ分析、人工知能（AI）のグローバルリーダーであるQlik®は、2026年 6月 16日付で今野 芳弘（こんの よしひろ）が同社の日本法人であるクリックテック･ジャパン株式会社の執行役員社長に就任したことを発表しました。
  
  今野は、40 年以上にわたりテクノロジー業界で豊富なリーダーシップ経験を有し、クラウド、AI、データ分析、SaaS、セキュリティ、通信など幅広い分野において、セールス、パートナーエコシステム、システムエンジニアリング、カスタマーサポート、ビジネス開発を統括し、6億米ドルを超える事業規模の組織運営を担ってきました。
  
  Qlik 入社以前は、Twilio Japan 合同会社の代表執行役員 社長として日本法人の立ち上げを主導し、クラウドコミュニケーションプラットフォームの国内展開を推進したのち、 Fastly 株式会社のカントリー・マネージャーとして、日本市場におけるエッジクラウドプラットフォームの事業拡大を牽引しました。
  
  今回の就任にあたり、今野は次のように述べています。
  「このたび BI の老舗の企業である Qlik の一員になれたことを大変嬉しく思います。これまではクラウド市場の拡大に貢献して参りましたが、昨今は AI の応用で話題が絶えません。そして、その成果を最大化するためには、信頼できるデータを適切に活用できる基盤が不可欠です。私は、この領域において Qlik が果たす役割は今後ますます重要になると考えており、日本市場には大きな成長機会があると確信しています。BI の価値、データ統合のビジネス価値を示すとともに、AI を最適に活用することで、よりビジネスに貢献できる BI ソリューションも提供していきたいと思います。」

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  By Qlik_JP_MKT2 2026-06-21 / 10:39 PM

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  Qlik Learning

  We've got great news in Qlik Certification!

  We're introducing Webassessor, a smoother, more reliable, and user-friendly exam proctoring platform for all Qlik Certification exams.  This update wi... Show More

  We're introducing Webassessor, a smoother, more reliable, and user-friendly exam proctoring platform for all Qlik Certification exams. 

  This update will help deliver a more consistent and secure certification experience while maintaining the same trusted certification process. 

  What stays the same? 

  • You'll continue to register for and launch exams through Qlik Learning.  

  • No separate Webassessor account or login is required.  

   

  To register for an exam, log in to Qlik Learning, locate the exam you wish to take, and complete your purchase. Your exam will then appear in the My Requirements section of your Qlik Learning homepage. 

  Go to Get Started with Certifications for the full registration process. 

  What's changing? 

  Once you launch your exam, the proctoring experience will be provided and managed through Webassessor, offering improved platform performance, stability, and ease of use.    

  Go to What’s new in Qlik Certification? for more information.  

  Coming July 2026 

  Webassessor launches in July 2026. We'll share additional details as the launch date approaches. 

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  By Raheela 2026-06-18 / 12:41 PM

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  Product Innovation

  When the regulator asks, your data should have the answer

  Regulatory compliance frameworks like BCBS 239, DORA, and Solvency II don't just demand accurate data. They demand that every team in your organizatio... Show More

  Regulatory compliance frameworks like BCBS 239, DORA, and Solvency II don't just demand accurate data. They demand that every team in your organization means the same thing when they say, "net exposure," "operational risk event," or "eligible capital." Regulators want one authoritative answer. What they often find instead are multiple teams working from definitions stored in separate compliance manuals, legal documents, and glossaries that are completely disconnected from the data products those teams use.

  That gap is where regulatory reporting breaks down.

  Your compliance glossary is now tied to your data product

  With Qlik® Talend Cloud, data product managers can now attach a business glossary directly to a data product as a first-class component. The governed definitions your compliance or legal team maintains travel with the data to every consumer who accesses the product.
  
  Linked data products to glossary

   

  You bring the definitions by importing a file (or generate and auto-populate the glossary terms using Qlik’s MCP), whether those are Basel III capital terms, GDPR data classification definitions, or your own internal regulatory taxonomy, and the data product carries them. Qlik supports business glossaries with hierarchical, multi-level term structures. Glossary terms follow a governed lifecycle, moving from Draft to Verified through steward approval, and can be deprecated when no longer in use.
  
  

  Hierarchical glossary terms with governed lifecycle

   

  What does this mean for your organization?

  Data product managers can now document regulatory intent alongside the data itself. When a risk analyst opens your capital adequacy data product, the latest definitions that frame the data are right there, not decoupled from the data product and outdated from two audit cycles ago.

  For CDOs and compliance leaders, this closes an accountability gap that regulators increasingly scrutinize. You can demonstrate that the data your organization reports is bound to the same definitions your compliance team approved, streamlining the audit process.

  For analysts and data consumers, the day-to-day experience is simpler: open the product and see the glossaries they have access to. No ambiguity about whether you're using the approved definition or an old one someone passed down informally. Data product consumers can only see the glossaries they're permitted to view. Sensitive regulatory classifications don't surface to people who shouldn't see them, and there's a clear, auditable chain from definition to data product to consumer.

  Part of making data products self-describing

  Business glossary brings meaning that travels with the data in the data products, so the assets your teams and AI systems operate on carry the context needed to produce trusted, explainable outputs, including under regulatory scrutiny.

  Business glossary for data products is available in Qlik Talend Cloud and Qlik Cloud Analytics (Premium and Enterprise).

  If your organization operates under regulated reporting requirements, this is a direct path that ensures your data products say what your compliance glossary means.

  New to Qlik Talend Cloud and Data Products? Join the 14-day free trial to get started
  More feature information can be found in documentation here 

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  By DPinto 2026-06-16 / 12:33 PM

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  Qlik Academic Program

  Higher Education's 2026 Tech Trends Are Here. Is Your Campus Ready?

  Technology has moved from the edge of education to its core. As Nilesh Patel, Global Solution Head for Education at TCS, argues in Higher education in... Show More

  Technology has moved from the edge of education to its core. As Nilesh Patel, Global Solution Head for Education at TCS, argues in Higher education in 2026: Emerging tech trends, the real shift isn't just adopting new tools, it's building digital architecture that learns and scales with learners. The article also points to a wider signal: a TCS study found that 61% of universities lag in digital maturity, putting higher education at a digital crossroads.

  The piece highlights four trends shaping higher education in 2026:

  • Lifelong learning and employability. Learners now see education as a career-long continuum, focused on outcomes and employability. AI-powered adaptive systems support this by adjusting course difficulty, content, and feedback to each learner's path.
  • Stackable microcredentials and flexible pathways. Education is shifting toward skill-oriented credentials that reflect what learners can do, letting them customize pace and focus while aligning with industry needs.
  • Collaboration between universities, EdTech, and industry. Skill gaps and fast-moving technology make partnerships essential, institutions provide academic rigor, EdTech offers scale, and industry ensures relevance.
  • Experience and engagement. AI and GenAI, immersive XR, learning analytics, and stronger cybersecurity and data governance are redefining how students learn and how institutions protect trust.

  Across all four, one theme repeats: the future of higher education depends on blending technology with trust,responsible AI adoption, ethical oversight, and transparency.

  This is exactly where the Qlik Academic Program supports academic communities. The program offers Qlik solutions end to end, from data integration and Qlik Cloud Analytics to Qlik Answers and agents alongside certifications, qualifications, and courses. By putting data literacy at the center of learning, students develop the ability to read, question, and explain data while working hands-on with the full analytics stack: integrating data, building insights, and understanding how AI-driven decisions are made.

  Professors are supported with training and teaching resources that make it easier to embed analytics and modern data topics across disciplines, helping institutions move from static curricula toward the responsive, data-driven ecosystems the TCS article describes.

  As Patel puts it, the future belongs to those who build with foresight, stay transparent, and treat technology as a partner in progress, not a replacement for purpose. Preparing students to work confidently with data, alongside AI, is central to that.

  By giving students, professors, and universities free access to analytics software, learning content, and certifications, the Qlik Academic Program helps education stay aligned with the trends shaping 2026 and prepares learners for the jobs of tomorrow.

  Join our global community for free: Qlik Academic Program: Creating a Data-Literate World

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  By TalitaBM 2026-06-16 / 10:12 AM

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  Japan

  【開催報告】AI Reality Tour Tokyo 2026

  去る 6月 10日（水）、有明セントラルタワーホール & カンファレンスにて「AI Reality Tour Tokyo 2026」を開催しました。「Qlik でデータを AI の原動力へ」をテーマに、米国開催の「Qlik Connect 2026」で発表したエージェンティック AI や Servi... Show More

  去る 6月 10日（水）、有明セントラルタワーホール & カンファレンスにて「AI Reality Tour Tokyo 2026」を開催しました。
  「Qlik でデータを AI の原動力へ」をテーマに、米国開催の「Qlik Connect 2026」で発表したエージェンティック AI や ServiceNow との戦略的パートナーシップなどの最新情報をご紹介しました。

  

  13:00 の開催時刻を迎え、Chief Revenue Officer の Brian Hamel の開会の挨拶からスタート。日本のお客様に直接ご挨拶するのが初めてということもあり、日本在住経験があることや日本の食べ物が大好きだという自己紹介から始まりました。日本の顧客やスポンサーパートナー企業への感謝の意と日本市場へのさらなる強いコミットメントを表明しました。

  

  続いて、Data Business Unit, General Manager の Matthew Hayes およびクリックテック・ジャパン技術担当の中嶋によるデモを交えた基調講演をお届けしました。AIへの投資が増える一方、多くの企業が期待通りの成果を得られていない現状を指摘。Qlik は、「コンテキスト・信頼・自由」の 3 つを軸に、既存の投資を活かしながらエージェンティック AI を実現する方法を提示しました。デモパートでは、Discovery Agent / Qlik Answers® / Predict Agent / Automate Agent などの新機能をご紹介し、Qlik Open Lakehouse と データ製品による信頼できるデータ基盤の重要性を強調。Qlik MCP Server による Claude など外部 AI との連携もお見せしました。

  

  次に、Senior Vice President, Asia Pacific & Japan のMaurizio Garavello が登場し、新カントリーマネージャーの今野芳弘を紹介。今野より新任の挨拶をさせていただきました。続いて、アマゾン ウェブ サービス ジャパン合同会社の河原氏がご登壇し、Qlik との強いパートナーシップについて語っていただきました。

  

  基調講演の最後は、Qlik で画期的なイノベーションと卓越したビジネスを展開されていることを称える「リージョナルデータインパクトアワード授賞式」で締めくくりました。本年度は、株式会社クレハ様と株式会社Gakken 様が受賞されました。おめでとうございます！

  

  基調講演後のコーヒーブレイクでは、参加者がホワイエに並ぶスポンサーブースを訪れ、賑わいを見せていました。コーヒーブレイク後は、2 会場でビジネス / テクノロジーセッションへと続きます。

  

  ユーザーセッションには 3 社にご登壇いただき、Qlik の先進的な事例をご紹介いただきました。

  日清ホールディングス様は、データドリブン経営を実現する基盤の整備に取り組んでおり、Qlik Cloud® Data Integration を活用して SAP データを連携し、SAP データの価値を最大化している事例をご紹介いただきました。

  

  Gakken 様は、データ分析とアクションの促進にQlikデータ統合・分析を活用して基盤を構築。「作って終わり」ではなく、社内活用の拡大に取り組まれています。また、生成 AI や LLM の積極的な活用事例もご紹介いただきました。   

  

  BookLive 様は、Snowflake と Qlik を活用して「どんな読者が何種類いるか」を特定し、戦略的なビジネスを展開されています。また、全社的な AI エージェントの積極的な活用という新たな取り組みについてもご紹介いただきました。 

  

  さらに、本イベントのゴールドスポンサー企業であるインサイトテクノロジー からは、Qlik MCP サーバー安全な接続を確保した外部 AI の活用例とデモをご紹介いただきました。
  日鉄ソリューションズからは、Qlik を活用したAI時代に最適なデータ基盤、AIエージェントの活用 例をご紹介いただきました。
  日立社会情報サービスからは、“現場 DX” と “業務変革” をつなぐ取り組み、MCP による外部 AI エージェントの連携、Qlik と ServiceNow による分析とアクションの連携などをご紹介いただきました。

  

  テクノロジーセッションでは、クリックテック・ジャパン技術部門が、Discovery Agent  / Qlik Answers / エージェンティック AI / Qlik MCPサーバー / Qlik Open Lakehouse など、データ統合・データ分析における Qlik の最新テクノロジーをご紹介しました。

  

  最後は、新カントリーマネージャーの今野の乾杯の挨拶で懇親会がスタート。お酒とお食事でリラックスしながら、お客様・パートナー様・Qlik のつながりを深めていただきました。

  

  さらにさらに！同日の午前中には、Qlik / Talend のユーザー様のみがご参加いただける「Qlik データ統合分科会」が行われていました。サッポロホールディングス様、QTnet 様、ジャトコ様の 3 社より、ここでしか聞けないユーザー事例をご紹介いただきました。また、ユーザー同士だから共感できる課題や解決策などの意見交換が行われました。

  

  今回ご紹介した内容が、皆さまの今後の取り組みのご参考となりましたら、大変嬉しく思います。最後に、ご登壇いただいたお客様・ご参加いただいたお客様・ご協賛いただいたスポンサー各社様の支えで、本イベントを盛況のうちに終了することができました。改めて御礼申し上げます。引き続き、Qlik は皆さまのビジネスを全力でサポートいたします。ぜひ、今後のQlikにご期待ください！
  最後は Qlik 社員の「Q ポーズ」でイベントの成功を締めくくりました。

  

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

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  By Qlik_JP_MKT2 2026-06-15 / 09:41 PM

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  Product Innovation

  How Talend Routes Made the Qlik Games Live Data Playground Possible

  At Qlik Connect 2026, the Qlik Games turned a conference into a live data playground. Every golf swing, bike sprint, and hockey goal fed real-time lea... Show More

  At Qlik Connect 2026, the Qlik Games turned a conference into a live data playground. Every golf swing, bike sprint, and hockey goal fed real-time leaderboards and AI trading cards on big screens across the venue. At the heart of the solution was a deceptively simple but powerful toolkit: Qlik Talend Routes.

  

  Routing excels in a variety of rolls like API orchestration and microservices messaging, — but one of its major transformative roles is bridging “tricky” sources that analytics platforms simply can’t reach on their own. That’s exactly what we needed here. Two very different technical headaches, one flexible routing layer, and clean, real-time data flowing into Qlik Open Lakehouse so the rest of the platform could work its magic.

   

  The Bike Challenge: High-Velocity Telemetry Trapped in a Time-Series Database

  

   

  Bike sensor data from ANT+ devices streamed straight into InfluxDB — a time-series store that no analytics application speaks natively in real time. The Talend Route listened for new events, enriched each one with rider and device context on the fly, and pushed the results forward. In milliseconds, clean, analytics-ready records were landing in Qlik Open Lakehouse via Kinesis, continually populating live leaderboards. What started as raw, high-velocity telemetry became contextualized, queryable data the moment it hit the lakehouse.

   

   

  

   

  The Golf Challenge: The JSON File That Refused to Behave

  The GSPro golf simulator wrote every stroke into a single .dat file — a JSON array that was completely overwritten after each swing. It was a moving target, not a clean event stream. The Talend Route watched the file for changes, intelligently split the array into individual strokes, filtered out duplicates with idempotency, and enriched each new record with golfer context. Clean JSON records then landed in dated, timestamped S3 folders for Qlik OLH to ingest. What began as a messy, stateful file became a reliable stream of enriched events the platform could trust.

   

  Two completely different technical problems — one a high-velocity database listener, the other a file-watching, array-splitting challenge. The same routing layer handled both with ease, delivering the exact same outcome: clean, real-time, context-rich data that Qlik Open Lakehouse could immediately turn into live leaderboards, recent-attempt visuals, and AI trading cards. 

   

  The Power of Routing

  By absorbing most of the source complexity, Talend Routes let the rest of the architecture shine. No custom one-off scripts. No forcing the analytics layer to become an ETL engine. Just flexible integration that made “impossible” sources behave like well-behaved, contextualized events on standard channels.

  This matters more than it first appears. In a modern open lakehouse architecture — Apache Iceberg on S3, decoupled storage and compute, spot-instance economics — the routing layer becomes the quiet enabler that lets every other component do what it does best. The bike route turned a time-series database into a streaming source. The golf route turned a constantly-rewritten file into partitioned, idempotent events. Both fed the same downstream system without any special handling on the analytics side. That’s the real leverage: routing doesn’t just move data; it normalizes chaos so the platform can deliver speed, scale, and cost efficiency at the same time.

   

   

  Conclusion

  Key Takeaways

  • When real-time sources are inaccessible or awkwardly formatted, routing is often the cleanest bridge.
  • Different sources deserve different patterns — the right tool lets you choose without compromising the destination.
  • Enrichment at the routing layer keeps downstream systems simple and fast.
  • Small capabilities (idempotency, file streaming, dated partitioning) deliver outsized reliability and value.

   

  The Bottom Line

  Talend Routes turned sensor data that no analytics application could have consumed directly into the rhythm of Qlik Connect 2026. The same flexible approach scales far beyond conferences — it’s how modern data teams turn messy, real-world sources into governed, analytics-ready pipelines at production scale. Whether you’re dealing with device streams, legacy files, or anything else that feels just out of reach, routing could be the answer.

   

  More about the Qlik Games:

  https://www.qlik.com/blog/inside-the-qlik-games-how-we-turned-qlik-connect-2026-into-a-live-data-playground

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  By Ben_Hough_Qlik 2026-05-28 / 06:35 AM

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  Design

  Spain wins the 2026 World Cup. At least that's what the model says.

  The 2026 World Cup kicks off today! A few weeks ago, I published a post on how we built Choose Your Champion 2026, a World Cup bracket app powered by ... Show More

  The 2026 World Cup kicks off today!

  A few weeks ago, I published a post on how we built Choose Your Champion 2026, a World Cup bracket app powered by Qlik. That post went into the technical details: the data model, the ML experiment in Qlik Predict, the React front end. If you haven't read it, start there.

  So far, 90+ brackets have been submitted. One of them wasn't filled out by a person at all. It was generated by the machine learning model we trained with Qlik Predict, using FIFA rankings, player ratings, and head-to-head history. The model has Spain winning it all!

  Here's mine, and here's Qlik Predict's.

  If you haven't filled one out yet, do it here. Once you're in, use the Compare button on the leaderboard to see where your picks differ from mine, or from the model's. Brackets get scored automatically as matches are played, so the leaderboard will keep moving all the way to the final on July 19.

  We also opened up a Choose Your Champion space on Qlik Community. That's where we'll be posting leaderboard updates during the tournament, and where you can share your bracket, ask questions about the app, or make your case for why the model got it wrong.

  If you're interested in the broader question of when models beat human intuition (and when they don't), take a look at Model vs. Mastery.

  Enjoy the tournament!!

  

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  By Ouadie 2026-06-11 / 10:47 AM

  0 1 1076
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  Product Innovation

  In a world obsessed with AI, don’t discount HI

  The relationship nobody has figured out yet    We’ve been told the story so many times it starts to feel like fact: AI is coming for human judgment. I... Show More

  The relationship nobody has figured out yet 

   

  We’ve been told the story so many times it starts to feel like fact: AI is coming for human judgment. It sees patterns we miss, processes data we can’t hold, and makes predictions faster than any expert. Hand it the keys. Trust the model. 

  But spend time with the people who actually work at the intersection of AI and high-stakes decisions — not the vendors, not the keynote speakers, but the real practitioners — and a different picture emerges. AI is extraordinary. It is also incomplete. And the gap between what it can do and what it claims to do is exactly where human intelligence lives. 

  This tension isn’t a flaw in the technology. It’s a feature of reality. The world is messy, contextual, continuous, and deeply human. No model, however powerful, was trained on all of it. The question isn’t whether to use AI. The question is whether you know when to override it. 

  I saw this at Qlik Connect '26: 'By 2030, £30 billion worth of mistakes will be made by AI without human intervention.' Yet everybody thinks AI is magic and it's the answer to everything. It's an amazing tool and does amazing things. But without the human intervention sense check, you'll be very cautious of it.

  — Steve Palmer, Head of Data Solutions, Premier League

   

  Steve Palmer spent 18 years playing in the Premier League, then spent 15 more building the data infrastructure that now tracks every movement of the players on the pitch today. He is, in other words, one of the few people alive who has not only taken a corner kick at Old Trafford but also written the code that measures them. 

  His take on the AI revolution isn’t skepticism. It’s lived-in experience from his past and his current lives. And it starts with two letters the industry keeps forgetting: HI. 

   

  HI and AI in action: What the Premier League has figured out 

  Football data is harder than moneyball. When people think of sports analytics, their minds go to moneyball: the Oakland A’s, Billy Beane, sabermetrics. Steve has a ready answer for why football doesn’t translate cleanly. 

  It's much more real time and much more continuous. It's not stop-starting. There's 22 players, 11 in each team, operating independently all the time — and really difficult to get the metrics together to work out which ones are the key ones.

  — Steve Palmer, Head of Data Solutions, Premier League

   

  Baseball gives analysts clean, discrete events. A pitch. A hit. A run. Football gives them 90 minutes of continuous, multivariate chaos — too much to fully capture and model. Without context, even the best models work with incomplete information. The smartest football minds know it. 

  That’s not a knock on the model. Steve uses it every day. It’s a precise diagnosis of what AI can and can’t do, and exactly where human judgment should step in. 

   

  The technology is extraordinary. So is the complexity. 

  The scale of what the Premier League tracks today is genuinely staggering. Steve walked us through it: 32 cameras in every stadium, operating at up to 200 frames per second, capturing 23 body points per player. The latest “mesh data” system generates 10,000 data points per player, 100 times every second — enough to reconstruct a full-body digital twin of every player in real time. 

  	Data then (8 years ago)	Data now
  Tracking	Centre of mass: one point per player, tracked 25 times per second.	Mesh data: 10,000 points, 100 times per second. Full-body digital twins in real time.

   

  That’s AI and machine learning doing something remarkable. But Steve is careful about the conclusions it invites. The model generates insights from the data. It cannot necessarily generate the wisdom to interpret it. 

   

  HI: Those two letters changing the conversation 

  In every AI discussion, Steve comes back to one concept: HI, human intelligence. Not as nostalgia for the pre-data era. Not as resistance to machine learning. But as the essential counterpart without which AI develops dangerous blind spots.

  HI and AI go hand in hand. Don't forget human intelligence — the softer side that a machine can't give you, which is key to a lot of the decision making.

  — Steve Palmer, Head of Data Solutions, Premier League

   

  What does HI look like in practice? In football, Steve is specific: you can isolate player profiles from global databases, model their metrics, rank them against a recruitment brief. But you can’t model whether a player will thrive in your dressing room, whether they’ll adapt to a new manager’s philosophy, or whether they have the psychological make-up to perform under pressure in a new country. 

  The managers who get this right visit players, meet their families, look them in the eye. Data builds the shortlist. Human judgment makes the call. 

  This isn’t a sports-specific insight. It’s a business insight wearing a football kit. 

   

  Where Qlik fits: tools built for the tension 

  Steve’s relationship with Qlik in the Premier League context is specific and revealing. The Premier League uses Qlik as a validation layer for its skeletal tracking data — checking that the measurements being generated are anatomically feasible before they feed into anything downstream. Are those legs two meters long? Is that skeleton geometrically possible? 

  It’s a perfect metaphor for what good analytics tooling does. It doesn’t just output numbers; it validates the foundations those numbers are built on. Not generate-and-trust. Generate, interrogate, decide. 

  That’s exactly what Qlik’s solutions are designed for: not just surfacing predictions but surfacing the reasoning behind them so humans can do that challenging, interrogating, and ultimately, decisioning. 

   

  Qlik capabilities built for HI + AI decision-making 

  Qlik Analytics^®

  See what happened. Associative analysis and visual exploration.

  Qlik Predict^®

  Model what will happen. ML-powered predictions and scenario simulation.

  Qlik Answers^®

  Ask why. Agentic AI that answers natural-language questions and reasons through the evidence.

   

  The tension: Model vs Mastery during the World Cup 

  This summer, Qlik is putting the HI vs AI question to the test in the most watched sporting arena on earth. Model vs Mastery is a new three-episode video podcast series launching around the 2026 World Cup, hosted by Adam Mayer, Technical Data Marketer for Qlik. 

  Adam moderates as Steve Palmer, Head of Data Solutions for the Premier League with a combined 33 years of football player and data engineering experience, goes head-to-head with Nick Magnuson, Qlik’s Head of AI. The World Cup is the arena. The question running through all three episodes is the one Steve has been answering his whole career: 

   

  Can AI predict the unpredictable? 

  Model Vs. Mastery, Watch here

   

  01

  Pre-tournament · Qlik Predict + Qlik Analytics

  The Model vs The Man

  Predicting the Path to the Final:
  Join Adam, Steve and Nick as they discuss the highs and lows of predicting group stage and play-offs outcomes from the upcoming championship and then put that to the test with the ‘Choose Your Champion app’ powered by Qlik

  Drops at tournament kickoff June 11

   

  02

  Mid-tournament · Qlik Answers · Agentic AI

  Inside the Tournament

  Half-Time Dressing Room Review:
  Adam, Steve and Nick retire to the dressing room to discuss the group stages so far, how have their predictions faired? What happens when the model is wrong and why HI is just as important as AI.

  Group stage

   

  03

  Post-tournament · Full suite · The verdict

  The Final Reckoning

  “They think it’s all over, it is now” – as the final whistle still reverberates around the stadium, we recap on the results, expect deep data discussions on models, what ifs and why AI and HI go hand in hand

  After the final

   

  The business parallel is direct. The AI modeling World Cup tournament outcomes is the same class of technology modeling your pipeline, your churn risk, and your supply chain. Knowing when to trust it — and when to override it — is the question every leader is navigating right now. Qlik is built for that moment. 

   

  The culture, not just the tool 

  Steve ended our conversation with something that felt less like a soundbite and more like a manifesto — not just for football analytics, but for every organization figuring out where AI ends and human judgment begins. 

  The technology is there. You've just got to get the culture to embrace it.

  — Steve Palmer, Head of Data Solutions, Premier League

   

  That’s the real work. Not buying the tool. Not running the model. Getting your culture to understand what the model is for — and what it’s not. AI without HI is a number without a narrative. HI without AI is intuition without evidence. The organizations winning right now are the ones holding both. 

  The tension between AI and HI isn’t a problem to be solved. It’s a dynamic to be managed: in every recruitment decision, every forecast, every strategy call. The question is whether you’re equipped to manage it well. 

  We built this series to make that argument through the most compelling arena we know. We’re also giving you a chance to experience the action yourself — see the box below! 

   

  Model vs Mastery: Choose Your Champion

  Feel the tension. Make your picks.

  Test your instincts against the model. Can you out-predict the AI?

  https://engage.qlik.com/model-vs-mastery?co=%7B%7BCompany.Company

  Want to see more of what Qlik can do? Start a trial at qlik.com.

  Qlik Insider · World Cup 2026 Series · Steve Palmer interview conducted May 2026 · Data vs Instinct: 3 episodes, June–July 2026

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  By Katie_Abbott 2026-06-11 / 10:17 AM

  0 4 875
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  Qlik Academic Program

  Qlik Academic Program offers complete training from data ingest to insight disco...

  Qlik, the leaders in user-driven business intelligence, has developed the industry’s only end-to-end data platform combining data sourcing, data prepa... Show More

  Qlik, the leaders in user-driven business intelligence, has developed the industry’s only end-to-end data platform combining data sourcing, data preparation, and data analysis tools in one robust toolset.

  With support for real-time data integration, modern data management, smart data cataloging, and AI-fueled analytics, Qlik helps you bring distributed, diverse data together fast and makes it available to users in a governed environment to transform all your data into real value.

  Qlik’s Data Integration layer helps you turn your raw data into analytics-ready information assets by enabling you to:

  • Move data at scale in real-time through streaming data pipelines with change data capture

  • Automate the design, deployment, and management of data warehouses and data lakes

  • Make trusted data easily accessible to users via a secure, enterprise data catalog

  Qlik’s Data Analytics layer allows you to turn your data into valuable insights by empowering users with intuitive, AI-powered tools that let them explore your data without boundaries.

  Source: qlik.com 

  Once students and professors sign up for the Qlik Academic Program, they receive training, qualifications and certifications on aspects of data including data integration, analytics, predictive analytics etc. Its a lot of value for the academic community and the best part? Its all free.
  
  So if you are a student or professor looking to propel your career, you should check out this program and discover all the benefits of the industry's only end-to-end data platform. 

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  By Pankaj_Muthe 2026-06-11 / 04:13 AM

  0 2 225
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  Explore Qlik Gallery

  Advanced Sankey

  Advanced Sankey AnyChart A Sankey for flows that outgrow the native chart: up to ten stages, richer selection, and extended visual context. See ... Show More

  

  

  Advanced Sankey

  AnyChart

  

  A Sankey for flows that outgrow the native chart: up to ten stages, richer selection, and extended visual context. See it all in action on a global supply chain demo.

  

  

  Discoveries

  Trace a global supply chain through multiple stages: source regions, materials, product lines, and end markets. Each stage is a column of nodes; the bands between them scale to the size of each flow. Click a link between two stages and the chart selects both source and target dimension values at once, filtering the entire sheet to that specific flow path.

  

  Impact

  A hands-on walkthrough of all major features at work: multi-stage flows up to 10 dimensions, dual-end flow selection, gradient links, level headers, label modes, detailed tooltips, layout controls, and more.

  

  Audience

  Qlik developers and BI consultants visualizing multi-stage flows in their dashboards — budget allocation, supply chains, customer journeys, energy flows, website traffic, and conversion funnels.

  

  Data and advanced analytics

  Built with the Sankey Chart extension for Qlik Sense, released in 2026. Based on fictional global supply chain data.

  🔗 >> VIEW LIVE OR DOWNLOAD .QVF <<

  🔗 >> SEE MORE QLIK APPS BY ANYCHART <<

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  By anychart 2026-06-10 / 08:48 AM

  0 3 549
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  Product Innovation

  Unlocking Qlik Open Lakehouse Access from Talend Studio

  Native Qlik Open Lakehouse interoperability for Talend Studio With the March release, Talend Studio introduces native support for querying Qlik Open L... Show More

  
  Native Qlik Open Lakehouse interoperability for Talend Studio

  With the March release, Talend Studio introduces native support for querying Qlik Open Lakehouse datasets through Amazon Athena — available in both Standard Data Integration jobs and Spark-based Big Data workflows.

  This means developers can now connect to Qlik Open Lakehouse data, execute SQL queries, and integrate results downstream the Talend job without manual JDBC configuration or custom setup.

  Connecting Talend Studio to Qlik Open Lakehouse

  Talend Studio now connects natively to Qlik Open Lakehouse through Amazon Athena — a SQL query engine that runs directly on top of cloud storage, enabling access to Iceberg-managed data without data movement or duplication. Developers can:

  • Access Qlik Open Lakehouse data with an out-of-the-box configuration,  no manual JDBC setup required
  • Execute SQL queries directly within Talend jobs (Standard and Big Data)
  • Integrate Qlik Open Lakehouse data into existing Talend jobs without disrupting current workflows

  Reliable by Design

  Connecting to Qlik Open Lakehouse from Talend Studio is straightforward by design. The integration ships with dedicated Athena configuration and input components, eliminating manual setup. Runtime validation, improved error handling, and secure credential management ensure the connection remains stable and trustworthy in production environments.

  How Data is Organized in Qlik Open Lakehouse

  In Qlik Open Lakehouse, data is ingested incrementally and accumulated in Apache Iceberg tables. A logical abstraction layer — implemented as Trino views — resolves those changes into a consolidated latest-state representation, which different engines can query without handling change consolidation logic directly.

  This model supports two complementary data patterns:

  • Current-state access (SCD Type 1): query the latest-state view through Athena for operational and integration use cases
  • Full history access (SCD Type 2): query the underlying Iceberg tables directly for time-aware and audit analysis

  Both patterns are available across Standard Data Integration and Big Data jobs in Talend Studio, enabling teams to work with Qlik Open Lakehouse data in the way that best suits their use case.

   

  

  Looking Ahead

  This integration enables Talend Studio users to access Qlik Open Lakehouse data without changing their existing workflows — while aligning with modern, open-format architectures that support multiple query engines.

  Athena is the first fully supported access path in this model, with a roadmap to extend support to additional engines over time. For organizations moving away from traditional data warehouses or adopting multi-engine strategies, this represents a concrete step toward a more flexible data architecture.

   

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  By RMartins 2026-05-07 / 04:12 PM

  3 4 1266
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  Design

  Data Products - take a tour

  Our Data Products Storylane has been given a revamp! TAKE THE TOUR   Data Products are highly trusted, re-usable, and consumable data assets. Data el... Show More

  Our Data Products Storylane has been given a revamp!
  
  

  

  TAKE THE TOUR

   

  Data Products are highly trusted, re-usable, and consumable data assets. Data elements such as raw data, transformations, data quality rules, contracts, access patterns, and infrastructure have been organized into a single cohesive unit to align with specific requirements and objectives of a business to create a Data Product.

  Data Products come with the Qlik Trust Score, a score given to data products based on seven factors, Validity, Completeness, Discoverability, Usage, Timeliness, Accuracy, and Diversity. The Qlik Trust Score ™ gives you confidence in the quality and health of your Data Products so you can be empowered when using them throughout your business. You can also view the Data Product Lineage, which is a flow chart that shows you the origin of the Data Product, so you can track it down to its source. Available Data Products can be found in the Data Marketplace, a collection of Data Products that are ready for use.

   

  If you want to learn even more about Data Products, Datasets, Data Quality and Data Validation Rules check out Mike Tarallo's video series here: Data Products for Qlik Analytics

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  By MattSmart 2026-06-05 / 01:01 PM

  0 0 675
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  Support Updates

  Upcoming Maintenance for Talend Cloud and Talend Management Console: March, Apri...

  Update March 4th, 2026: added link to How to get Talend Management Console task schedules and pause and resume during a maintenance window using the A... Show More

  Update March 4th, 2026: added link to How to get Talend Management Console task schedules and pause and resume during a maintenance window using the API article
  Updated April 24th, 2026: added impact on APIs (all down) and additional clarification on why tasks must be stopped and the impact on remote engines
  Updated May 7th, 2026: added additional information on how to address Remote Engine impact
  Updated May 12th, 2026: the anticipated impact for the remaining maintenance window has increased from 30 minutes to 90 minutes
  

  Talend Cloud and Talend Management Console will undergo scheduled maintenance in March, April, and May. This infrastructure modernization is a key step in unifying the Talend ecosystem with Qlik.

  The alignment paves the way for a more seamless experience across both platforms. Over the coming months, you will gain access to integrated features that bridge data integration and analytics, enabling unified governance and a streamlined management experience across your entire data lifecycle.

  The maintenance windows will occur per region, during off-peak hours, and are expected to have a maximum of 90 minutes of effective downtime. 

   

  What is the expected impact?

  A full outage of Talend Cloud and Talend Management Console for a duration of up to 90 minutes within a preplanned 4-hour window.

  The following applications will not be accessible: 

  • Talend Management Console (TMC)
  • Talend Data Stewardship (TDS)
  • Talend Data Preparation (TDP)
  • Talend Data Inventory (TDC)
  • Talend Pipeline Designer (TPD)
  • Talend API Designer and Tester
  • Talend Studio
  • Talend Cloud Engines

  All APIs for Talend Cloud will not be available during the outage. APIs impacted:

  • Talend Cloud API
  • Legacy Swagger Talend Management Console API

   

  In detail:

  • Cloud engines will not be available, and executions running on Cloud Engines will be terminated.
  • Talend Studio users may be disconnected from their session, and it will not be possible to open a new Talend Studio session except in local mode.
  • Executions that are already in progress during the outage will terminate correctly except on cloud engines, but all tasks or plans scheduled to start during those periods will be skipped.
  • Skipped executions will not be tagged as failed, since they were never started. For this reason, check the execution status of your tasks and plans to ensure that all important ones are not skipped, or start them manually if necessary. See What do I need to do to prepare? further down in this blog post.
  • Static IP addresses for Cloud Engines corresponding to Disaster Recovery regions will change during maintenance. See What follow-up actions are required? further down in this blog post.

   

  What do I need to do to prepare?

  • It is recommended to pause existing task runs during the maintenance window. Talend Remote Engines will continue processing tasks during the outage if they started before the maintenance window, but as they may have inconsistent statuses, we recommend pausing all tasks beforehand.
    
    This concerns all jobs and plans scheduled to start or run during the maintenance window.
    
    See Checking scheduled task runs against your maintenance timetable on how to identify these plans and jobs.

    Looking for information on how to identify, pause, and resume your tasks? See How to get Talend Management Console task schedules and pause and resume during a maintenance window using the API.

  • If you are running Remote Engine Gen2, upgrade to the latest 2026-04. This is to prevent RE Gen2 from becoming unavailable after the maintenance. To do so:
    
    
    1. Upgrade your Remote Engine Gen2 to the latest 2026-04 release: Updating the Remote Engine Gen2 when installed using the execution script)
    2. Then re-establish the pairing: Re-establish the pairing of Remote Engine Gen2

   

  What follow-up actions are required?

  • After the maintenance window, check and monitor the execution status of tasks and plans, as well as the status of your Remote Engines.

    In some instances, Remote Engines might require a restart if marked as unavailable in the Talend Management Console or if tasks cannot be executed as expected.

    If restarting the Remote Engine does not resolve the complication, follow the pairing instructions in Pairing Remote Engines using a dedicated web service to reset the key and re-pair the Remote Engine.

    If your Remote Engine Gen2 is unavailable or cannot execute tasks, then:

    1. Upgrade your Remote Engine Gen2 to the latest 2026-04 release: Updating the Remote Engine Gen2 when installed using the execution script)
    2. And re-establish the pairing: Re-establish the pairing of Remote Engine Gen2
       
       
  • If you use a predefined static IP on Cloud Engine, you will need to allow the new Disaster Recovery Region's IP addresses, which will have changed at this point. While this does not immediately affect production, it will impact any potential Disaster Recovery process.
    
    After the maintenance window, check your static IPs (Disaster Recovery) as documented in Using predefined static IP addresses for execution containers and update your firewalls accordingly.
    
    No change is required for the active region's IP addresses. They will be migrated and will work as of today, ensuring no production interruption.

   

  When will the maintenance take place?

  Each region will undergo maintenance for 4 hours during off-peak hours, with a maximum of 90 minutes of effective downtime.

   

  Region	Maintenance Start	Maintenance End
  Talend Cloud - AWS - Asia Pacific (Sydney) au.cloud.talend.com	Wednesday 25 March 2026  22:00 AEDT (Sydney) UTC: 25/03/26 - 11:00	Thursday 26 March 2026  02:00 AEDT (Sydney) UTC: 25/03/26 - 15:00
  Talend Cloud - AWS - Asia Pacific (Tokyo) ap.cloud.talend.com	Monday 20 April 2026  22:00 JST (Tokyo) UTC: 20/04/26 - 13:00	Tuesday 21 April 2026  02:00 JST (Tokyo) UTC: 20/04/26 - 17:00
  Talend Cloud - AWS - US East (N. Virginia) us.cloud.talend.com	Monday 27 April 2026  02:00 EDT UTC: 27/04/26 - 6:00	Monday 27 April 2026  06:00 EDT UTC: 27/04/26 - 10:00
  Talend Cloud - AWS - Europe (Frankfurt) eu.cloud.talend.com	Tuesday 26 May 2026  21:00 CEST UTC: 26/05/26 - 19:00	Wednesday 27 May 2026  01:00 CEST UTC: 26/05/26 - 23:00

  To identify which region your tenant is affected by, cross-reference Accessing Talend Cloud applications.

  To track further updates during the scheduled Qlik Cloud Maintenance, please visit our Qlik Cloud Status page. This blog post will be updated with additional information where necessary. 

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-03-02 / 08:10 AM

  35 1 16959
• 
  

  Support Updates

  Qlik NPrinting - New Security Patches Available Now

  A security issue has been identified in Qlik NPrinting, and patches have been made available. Details can be found in the Security Bulletin High Secur... Show More

  A security issue has been identified in Qlik NPrinting, and patches have been made available. Details can be found in the Security Bulletin High Security fix for Qlik NPrinting (CVE-pending).

  We've released two releases across the latest versions of Qlik NPrinting to patch the reported issues. All versions of Qlik NPrinting prior to and including these releases are impacted:

  • Qlik NPrinting February 2025 SR5

  
  No workarounds can be provided. Customers should upgrade Qlik NPrinting to a version containing fixes for these issues.

  • Qlik NPrinting May 2026 IR
  • Qlik NPrinting February 2025 SR 6

  Before applying patches or upgrading Qlik NPrinting, always review the system requirements for the new version.

  This issue only impacts Qlik NPrinting. Other Qlik products are NOT impacted.

  All Qlik software can be downloaded from our official Qlik Download page (customer login required). Follow best practices when upgrading Qlik Sense.

  Qlik provides patches for major releases until the next Initial or Service Release is generally available. See Release Management Policy for Qlik Software. Notwithstanding, additional patches for earlier releases may be made available at Qlik’s discretion.

  The information in this post and Security Bulletin High Security fix for Qlik NPrinting (CVE-pending) is disclosed in accordance with our published Security and Vulnerability Policy.

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-06-02 / 08:01 AM

  7 5 3683
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  Product Innovation

  Not All Catalogs Are Governance. Not All Governance Is a Catalog

  One question I get asked a lot is "We're evaluating Qlik alongside a standalone data governance catalog. How do you compare?" It's a reasonable questi... Show More

  One question I get asked a lot is "We're evaluating Qlik alongside a standalone data governance catalog. How do you compare?"

  It's a reasonable question. But the framing inside it, that you're choosing between Qlik and a standalone governance catalog, misses something important about what governance requires. So let me try to untangle it.

  The question behind the question

  Data intelligence platforms are built to balance two distinct problems, and most organizations have both. The first is context capture: building a unified metadata view to understand and optimize your data integration landscape. That means technical context, operational context, and governance context, all in one place. The second is context delivery: getting trusted context, with clear ownership and accountability, to the analytics, APIs, and AI agents that need it to produce real business outcomes.

  

   

   

   

  Both problems matter. They're related. However, they aren't the same problem, and they don't call for the same solution.

  Two Catalogs, One Trusted Foundation

  Context capture is where most metadata catalogs live, and the focus is heavily technical: inventorying schemas, tables, and columns across the estate; harvesting metadata from cross-platform sources; tracing column-level lineage from origin to destination; and powering search and discovery so data engineers and platform teams can find the right asset and understand its dependencies. Beyond those core technical jobs, the same catalogs also support regulatory and audit needs with cross-domain lineage, enterprise-wide policy enforcement, and classification. Qlik's Talend Data Catalog is one example of such a catalog solution.

  Context delivery requires a knowledge catalog that makes trusted data easy to discover, understand, and consume. In Qlik Talend Cloud, this includes a data product marketplace where users can find and request trusted data products, domain-driven ownership that enables teams to publish and manage data with clear accountability, rich business and technical context that helps both people and AI agents understand data, and usage intelligence that provides visibility into adoption and value. Together, these capabilities transform governed data into a consumable asset for analytics, AI, and business operations.
  The two aren't redundant. They serve different roles in the same data organization. Having both is what moves you from "we have a governance program" to "our governance actually produces something useful."

  But here's where I think the market conversation often stops too early.

  Where Governance Has to Run

  A standalone governance catalog typically answers a question about inventory : what data do we have, where did it come from, and what rules apply to it? It documents. It organizes. It certifies. What it can't answer is the question that matters most data practitioners - which is whether the data is trustworthy or not for a business use-case?

  That's where Qlik's approach is different. The knowledge catalog capabilities in Qlik Talend Cloud don't just surface what data products are available. They carry live trust signals with them. The Qlik Trust Score on a data product reflects what happened in the pipeline: accuracy, completeness, and timeliness, computed at runtime. Anomalies get flagged before bad data reaches a consumer. Trustworthiness isn't a label someone applied. It's a measurement the product makes continuously.

  The full sequence looks like this. Discover and understand your data through active metadata, lineage, and semantic search. Trust and validate it with Qlik Trust Score for AI, which measures accuracy, completeness, consistency, and timeliness as a live computed signal. Govern and secure it through PII detection and classification, role-based access, and policies enforced at runtime. Steward and operationalize it through quality improvement cycles and continuous remediation. Then activate and deliver it: data products integrated with analytics and AI tools, with usage tracking so you can measure impact.

  Context capture builds the foundation. Context delivery makes it useful. A standalone catalog only handles part of that sequence. 

  The Practical Takeaway

  When someone asks whether to use Qlik's knowlege catalog capabilities alongside a standalone governance catalog, my honest answer is: it depends on whether you are solving for context capture or context delivery or both.

  If you're a data engineer or architect and solving for context capture — inventorying the estate, tracing lineage across your entire organizational estate, classifying assets, supporting audit and compliance — a metadata catalog can handle that work. If you're a business data steward or business user solving for context delivery — publishing trusted data products, enforcing contracts and SLAs, routing governed inputs to analytics and AI agents — you need a solution like Qlik Talend Cloud.

  And if the real question is whether governance holds when data actually moves — whether SLAs are met, whether the Trust Score reflects what just ran in the pipeline — that's delivery work. No standalone catalog can answer it, and that has to come from the underlying integration and quality substrate that Qlik Talend Cloud provides.

  In summary, context capture gives you the picture of your data. Context delivery puts that picture to work so that analytics, APIs, and AI agents that depend on it. Governance should not exist as a separate layer above the data stack. It must be natively integrated into every stage of the data lifecycle. Only then can organizations move beyond governance processes to deliver governance outcomes.
  

  Don't get capture context, deliver it to drive your AI and Agentic outcomes. Try Qlik Talend Cloud® free to see this in action.

   

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  By DPinto 2026-06-02 / 04:02 PM

  2 4 1283