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Recent Blog Posts
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Qlik Talend Studio 2026-06 will move from Java 17 to Java 21
To all Talend customers, This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studi... Show MoreTo all Talend customers,
This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studio starting with the 2026-06 release.
This change only concerns the Talend Studio desktop application. It has no impact on the Jobs and Services you build with it, which will continue being compliant with Java 17 runtime environments (and Java 8 for Big Data Jobs).
You have multiple options to make a smooth transition:
- The Talend Studio embedded upgrade mechanism (Feature Manager) will offer handling the JDK version upgrade
- Should you prefer, a new installer of Talend Studio 2026-06 with JDK 21 embedded will also be provided
- You will also be free to manually configure Talend Studio to start with JDK 21, provided you are on Talend Studio 2025-09 or higher
Additional details:
Along with this change, the June release will come with many additions from a Java version perspective:
- Talend Studio will allow building Jobs and Services for Java 21 runtimes, in addition to Java 17 runtimes. Java 17 will remain the default setup for runtimes for the time being
- Remote Engines, Cloud Engines, Dynamic Engines, Job Server, and Talend Runtime will support running Jobs and Services in Java 21, in addition to the Java versions they already support
- Talend Administration Center (TAC), Data Preparation (client-managed), Data Stewardship (client-managed), Talend SAP RFC Server, Talend Semantic Dictionary, and Talend Identity and Access Management will also support running with Java 21 in addition to Java 17
For further questions, please start a chat with us to contact Qlik Support and subscribe to the Support Blog for future updates.
Thank you for choosing Qlik,
Qlik Support -
Knowledge Nuggets-2026Q2
Knowledge Nuggets-2026Q2Insight ConsultingA collection of Qlik and Data & AI Literacy related videos and articles, updated to Jun 2026DiscoveriesContr... Show MoreKnowledge Nuggets-2026Q2Insight Consulting
A collection of Qlik and Data & AI Literacy related videos and articles, updated to Jun 2026
Discoveries
Contributors and Keywords
Impact
Learning resource
Audience
All Qlik stakeholders
Data and advanced analytics
Learning resource
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The Human Skills AI Can't Replace: Why Students Should Embrace AI, Not Fear It
The answer isn't as simple as yes or no. While AI is changing the way we work, it isn't replacing the need for human talent. Instead, it's changing th... Show MoreThe answer isn't as simple as yes or no. While AI is changing the way we work, it isn't replacing the need for human talent. Instead, it's changing the skills employers value most. The students who succeed in the future won't be the ones who avoid AI—they'll be the ones who learn how to use it effectively.The Skills AI Can't ReplaceAs AI becomes more capable, human skills become even more valuable.Critical thinking helps professionals determine whether AI-generated information is accurate or biased. Creativity allows people to develop innovative ideas that go beyond patterns AI has already learned. Ethical decision-making ensures technology is used responsibly and fairly.Perhaps most importantly, AI cannot replace human judgment. Businesses still need people who can understand context, collaborate with others, communicate effectively, and make decisions that consider both data and real-world consequences.These are the skills employers consistently seek—and they're becoming even more important in an AI-powered workplace.Learn AI Now—Before You GraduateStudents don't need to wait until they enter the workforce to start building AI skills. In fact, college is the perfect time to experiment with new technologies.Use AI to brainstorm ideas, summarize research, explore coding concepts, or analyze datasets. At the same time, challenge yourself to verify its responses, ask better questions, and think critically about the results. Learning how to use AI responsibly today will prepare you for the tools you'll encounter in nearly every profession tomorrow.Why Analytics Matters More Than EverAI depends on data. If the data is incomplete, inaccurate, or biased, the results can be misleading.That's why understanding analytics and data literacy is becoming one of the most valuable career skills students can develop. Professionals who know how to interpret data, identify trends, and evaluate AI-generated insights will be in high demand across industries.Rather than replacing analytics, AI makes analytics even more essential.Building Future-Ready Skills with the Qlik Academic ProgramThe Qlik Academic Program helps students prepare for this new era of work by providing free access to analytics and AI learning resources, Qlik products and qualifications.Students gain hands-on experience working with real data while learning how analytics and AI work together to solve business challenges. These practical skills help graduates build confidence and demonstrate to employers that they're ready to contribute in an AI-driven workplace.Don't Fear the Future—Help Shape ItThe graduates who thrive won't be those competing against AI—they'll be the ones who know how to partner with it. By developing strong analytical thinking, embracing new technology, and continuing to learn, today's students can prepare for careers that are not only resilient but full of opportunity. -
Model vs. Mastery – Episode 2: Inside the Tournament
In this special mid-tournament edition, host Adam is joined by Steve Palmer from the Premier League and Qlik's Head of AI, Nick Magnuson, to take stoc... Show MoreIn this special mid-tournament edition, host Adam is joined by Steve Palmer from the Premier League and Qlik's Head of AI, Nick Magnuson, to take stock of the 2026 FIFA World Cup so far.
The trio revisits the bracket predictions made in Episode 1, reflecting on early surprises and examining how red cards, tactical adjustments, and the expanded 48-team format have challenged the accuracy of Qlik's AI prediction model. They explore the balance between data-driven forecasting and football's inherent unpredictability, while also discussing how real-time data integration could reshape in-match analysis and decision-making.
The episode also highlights the collective wisdom of more than 330 user-submitted brackets in Qlik's "Choose Your Champion" app, where Spain, France, and Brazil have emerged as the leading fan favorites. As the knockout stage approaches, listeners are encouraged to revisit their own predictions and make their picks before the next round begins.
Watch Episode 2 “Inside the Tournament” and create or update your bracket today!
Meet the Panel
Joining host Adam for this tournament check-in are two guests who bring very different — and very complementary — perspectives to the table:
- Steve Palmer, a football industry veteran with over 30 years of experience, including his Data Solutions Engineering work with the Premier League
- Nick Magnuson, Qlik's Head of AI, who brings the data science lens to everything football throws at us
Together, they make for a fascinating collision of human intuition and machine intelligence.
JOIN THE CONVERSATION: with the panel, and others, on our dedicated community forum!
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You Spoke, We Listened Hub
Each month, we publish new updates showing how YOUR feedback is directly reflected in product changes, enhancements, and roadmap decisions. 🔦 J... Show MoreEach month, we publish new updates showing how YOUR feedback is directly reflected in product changes, enhancements, and roadmap decisions.
🔦 June's Spotlight Feature: Configurable Session Timeouts
What We've Heard You Say
We heard customers say they want more flexibility around session management in Qlik Cloud and admins wanted a way to configure authentication session timeout settings.
What Qlik Did About It
We heard you and delivered. Admins now have the controls to configure it under Settings -> Tenant -> Session timeouts.
To give you more clarity and understanding of session handling in Qlik Cloud, it's helpful to know that different types of sessions exist across the platform:
Authentication Session Settings:
These settings determine when users need to sign in again through their identity provider (IdP):
Inactivity Timeout
- How long Qlik Cloud waits before requiring re-authentication after inactivity
- Default is 30 minutes, adjustable from 15 minutes up to 24 hours.
Maximum Session Duration
- The maximum lifetime of an authenticated session before re-authentication is required, regardless of activity.
- Default is 24 hours, adjustable up to 7 days.
Qlik Sense Engine Session:
This is separate from authentication and relates to engine memory management, not user sign-in.
Engine Inactivity Timeout
- How long a Qlik Sense app remains loaded in memory during inactivity.
- Default is 30 minutes, currently not configurable.
If you'd like to explore the feature in more detail, find details in Qlik Help.
What's In It for You
Admins, this one's for you. This enhancement gives organizations more flexibility to balance user experience and security requirements based on their own policies and workflows.
Changes apply to new sessions only, so existing users do not get interrupted mid-work.
This update was featured in our LinkedIn 'You Spoke, We Listened' carousel.
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Qlik アプリで「FIFA ワールドカップ 2026」をもっと楽しく観戦！
史上最大 48 ヶ国が熱い戦いを広げている「FIFA ワールドカップ 2026」の勝敗の行方を Qlik の Web アプリ「Choose Your Champion 2026」でお楽しみください！Qlik Predict® を使用した AI による試合予測、過去のワールドカップデータの探索ができる... Show More史上最大 48 ヶ国が熱い戦いを広げている「FIFA ワールドカップ 2026」の勝敗の行方を Qlik の Web アプリ「Choose Your Champion 2026」でお楽しみください！Qlik Predict® を使用した AI による試合予測、過去のワールドカップデータの探索ができるだけでなく、大会の進行に合わせてリーダーボードで他のユーザーと競い合うことができます。アプリのデータモデルと過去データの分析には Qlik Cloud Analytics®、試合予測には Qlik Predict、そして React のフロントエンドとの連携にはさまざまな Qlik API を活用しています。
アプリの4つの機能
「Choose Your Champion 2026」は 4 つのパートに分かれています。
- ブラケットを作る
グループステージの勝者を選び、ノックアウトラウンドでチームを勝ち上がらせ、最終的な優勝チームを決定します。 -
予測を確認する
大会で起こりうるすべての対戦カードについて、Qlik Predict が生成した各チームの勝率と引き分け率を表示します。どちらのチームを選ぶか迷ったときは、予測を参考にして判断できます。 - 過去のワールドカップデータを探索する
歴代大会のデータをさまざまなビジュアライゼーションで深掘りできます。得点、得点王、開催国のパフォーマンス、最大の番狂わせなど、すべて Qlik の連想エンジンで動いています。
- リーダーボード
実際の試合が行われると、提出されたブラケットは自動的に採点され、プレイヤーはリーダーボードにランキングされます。
Web アプリについて
Web アプリは React のフロントエンドで、@qlik/api を使って Qlik テナントに匿名アクセスで接続しています。ユーザーはログインやテナントへの認証も不要です。ブラケット UI は Qlik Sense® のデータモデルから予測データを取得しているため、ユーザーが対戦カードを開くたびに、Qlik から直接データが表示されます。
過去のワールドカップセクションでは、手軽に使えるチャートが必要な場合は @qlik/embed コンポーネントを、アプリのデザインに合わせてスタイルをより細かくコントロールしたい場合はカスタムの nebula.js + picasso.js ビジュアライゼーションを使い分けました。どちらのアプローチも同じ Qlik Cloud Analytics アプリを基盤としているため、すべてが 1 ヶ所で一貫して管理されています。
詳しい予測の仕組みはこちらをご参考ください。決勝トーナメントにコマを進めるのはどのチームなのか？先日のチュニジア戦では、W 杯 史上最速先制ゴール ＆ 史上最多得点という圧勝で、日本中が歓喜に包まれました。がんばれ、サムライジャパン！
今すぐ Web アプリにアクセス - ブラケットを作る
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Qlik Talend Cloud Dynamic Engine: A technical overview
Who is this For? If you're a data engineer, platform architect, or on a DevOps team evaluating whether Qlik Talend Cloud's Dynamic Engine is the right... Show MoreWho is this For?
If you're a data engineer, platform architect, or on a DevOps team evaluating whether Qlik Talend Cloud's Dynamic Engine is the right move, or you're already committed and working through implementation, then this article is written for you. It covers architecture, configuration, and the operational patterns that matter in practice. Business value gets called out where it's relevant, but this isn't a sales pitch.
Kubernetes familiarity helps with some sections. It's not required to follow the concepts, but you'll get more out of the architecture details if you're comfortable with namespaces, operators, and Helm.
The Challenge with Always-On EnginesFor most of Talend's history, data integration infrastructure followed a familiar and predictable pattern: provision a server or VM, install the Remote Engine runtime, and keep it running. The engine polls Talend Management Console (TMC) for work, processes jobs with limited concurrency, and runs whether it has tasks to execute or not.
For stable, predictable workloads that model is dependable. But for organizations dealing with variable demand, multi-tenant requirements, or teams trying to align their data infrastructure with modern DevOps practices, it creates real friction. You end up paying for compute you're not using, managing a fleet of VMs that need individual attention for upgrades, and hitting concurrency ceilings at exactly the wrong moment.
Dynamic Engine is Qlik's answer to those constraints.
What Dynamic Engine IsThe Dynamic Engine is a Kubernetes-native processing platform that executes Talend workloads like Data Integration jobs, Data Services, and Routes, on customer-controlled infrastructure. TMC and the still handle all management, scheduling, and orchestration. What changes is the execution layer. Instead of a persistent JVM waiting for work, Dynamic Engine provisions isolated containers on demand, runs the job, and releases those resources when it's done.
Three Principles Drive the DesignExecution is on-demand. There are no persistent runtimes to babysit. Each Talend Job, Data Service, or Route gets its own container for the duration of its execution. When the work is done, the container is gone.
Scalability is elastic. During peak processing windows, multiple containers run in parallel. During quiet periods, you're not burning budget on idle infrastructure. The ceiling is your cluster capacity, not a per-engine configuration value.
The design is cloud-agnostic and Dynamic Engine runs on Amazon EKS (including Auto Mode), Azure AKS, Google GKE (including Autopilot), and on-premises Kubernetes today, with Red Hat OpenShift support planned. Your cloud strategy stays yours.
What This Means in Practice:- Cost: Eliminating idle resources can substantially reduce infrastructure costs compared to persistent engine models.
- Velocity: Developers spin up environments for testing without resource conflicts or provisioning delays.
- Reliability: Each job starts with a clean container, eliminating state-related failures common in long-running processes.
- Simplified operations: Infrastructure management is abstracted away, letting engineers focus on pipelines rather than execution environments.
Real-World Use CasesBatch at scale: A retailer processing millions of nightly transactions scales from a handful of containers during the day to hundreds at peak, automatically.
Multi-tenant SaaS: A SaaS provider uses separate environments per customer, ensuring isolation while maintaining centralized monitoring, governance, and FinOps.
Hybrid cloud integration: An enterprise connects on-premises data warehouses to cloud analytics using environments configured with VPN access, maintaining security while leveraging cloud scalability.
Architecture: Two Environments, One Control Plane
Dynamic Engine spans two distinct environments: the Qlik Talend Cloud layer managed by Qlik, and your Kubernetes cluster where execution happens. They communicate over HTTPS, with your cluster always initiating the outbound connection. No inbound firewall rules are required on your cluster for Dynamic Engine.
On the Qlik side, TMC is the user-facing control plane. Through it you define Dynamic Engine entities, manage Dynamic Engine Environments (DEEs), configure Run Profiles, deploy and monitor tasks, and trigger promotions across environments. The Talend Cloud API exposes the same operations programmatically via REST, and it's worth noting this is the same API available for all Talend Cloud interactions and not something specific to Dynamic Engine.
Inside your cluster, two namespaces do the work.
The Two-Namespace Design: Stability Meets ElasticityThis is one of the more important architectural details to understand, because it explains how Dynamic Engine achieves two things that typically work against each other: a stable, persistent control layer and an execution layer that scales up and down freely.
The infrastructure namespace is the fixed foundation. It contains the engine-operator, which is the Kubernetes operator responsible for managing the lifecycle of all Dynamic Engine resources in the cluster, along with the CRDs that extend the Kubernetes API with Dynamic Engine-specific resource types, and the associated RBAC primitives, so the ClusterRoles, RoleBindings, ServiceAccounts, Secrets, and ConfigMaps. This namespace is deployed once per Dynamic Engine instance and its footprint doesn't change with workload.
It also hosts a local container image registry used for Data Service and Route images. When a Data Service or Route task is deployed, an image-builder job builds the task-specific container image and pushes it to this registry, where execution pods pull from it. Batch Data Integration jobs use a different artifact delivery path.
If your organization requires internally managed and secured registries, your own container image registry can be substituted for the in-cluster repository.
The Dynamic Engine Environment namespace is where execution actually happens. Each DEE you create in TMC maps to its own namespace in the cluster, named
qlik-processing-env-<env-id>by default, though this is customizable via Helm. This namespace contains the service pods that enable task execution, the ephemeral job pods running your workloads, and the persistent storage resources supporting them.One Dynamic Engine instance can serve multiple Environment namespaces simultaneously. That one-to-many relationship is what makes it practical to maintain separate dev, test, and UAT environments, or to organize by business unit, like accounting-dev, marketing-uat, all without needing separate clusters or engine deployments.
It's also worth being explicit about something; Dynamic Engine does not require a dedicated Kubernetes cluster. It's also compatible with ARM-based Kubernetes nodes and autopilot flavors. It's designed to run within your existing infrastructure, alongside your existing workloads, using your existing security controls, network configuration, and monitoring setup.
How the Two Layers CommunicateTwo channels connect Qlik Talend Cloud to your cluster.
HTTPS handles provisioning and management. The engine-operator establishes an outbound connection to Talend Cloud services, through which the TMC issues deployment instructions, configuration changes, and version upgrades. All traffic is TLS-encrypted.
ActiveMQ over HTTPS handles Talend job dispatch. Environment namespace service pods use this channel to receive task execution requests and report status back to TMC. The reliable message delivery model means a job submission isn't lost if your cluster is temporarily unreachable. Like the HTTPS channel, this is outbound from your cluster, so no special inbound firewall rules needed.
Air-gapped and Restricted-Network DeploymentsDynamic Engine has been designed with security and risk mitigation in mind. Both channels support air-gapped deployments. Infrastructure Helm charts and required images can be sourced from a local registry, such as Harbor, though cloud provider registries and other commercially supported options are used as well. In this setup, your cluster nodes never pull images from the Internet.
In this example Helm command, you’ll see the domain references the internal registry.
helm install dynamic-engine-crd \
oci://your.internal.registry/talend/helm/dynamic-engine-crdFor TMC communication in restricted environments, an HTTPS proxy can be placed between your cluster and Qlik Talend Cloud services. You can find additional information for configuring the proxy in your Dynamic Engine setup in the documentation.
Dynamic Engine also provides support for Kubernetes Network Policies, allowing you to enable default network policies that are set during your installation or upgrade, and further, you're able to further define custom egress rules that restrict your outbound traffic.
Deploying Dynamic Engine
Once you've created a Dynamic Engine in TMC, deploying it to your cluster uses familiar Helm install, and it exposes the full range of configuration values, supports
helm upgradewith rollback capability, and lets you validate configurations before applying with--dry-run=server.Prerequisites include needing a cluster to be running Kubernetes v1.30–v1.36, and you'll need cluster admin permissions for CRD registration and namespace creation.
One thing worth highlighting is that once Dynamic Engine and its Environments are up and running, data engineers don't need cluster access for day-to-day work. All task deployment, monitoring, and management happens through TMC and the Talend Cloud API. Direct cluster interaction is only needed when adding, removing, or upgrading Dynamic Engine components themselves.
Customizing the Execution EnvironmentDynamic Engine environments support a range of customization through standard Kubernetes resource mounting. ConfigMaps work well for non-sensitive configuration like API endpoints or feature flags. Secrets can be injected as environment variables for credentials, or mounted as volumes when jobs need access to certificates or keystores. PersistentVolumeClaims cover shared storage scenarios where data needs to survive beyond a single pod's lifetime. These patterns can be combined, and they apply independently to Data Integration job pods and Data Service or Route pods, so you have separate control over each execution type.
Run Profiles give data engineers a TMC-native way to tune how jobs run without touching cluster configuration directly. A Run Profile defines the JVM arguments and Kubernetes resource parameters for a given task: CPU and memory requests and limits, replica counts for Data Services and Routes, and autoscaling thresholds if needed. A team running a memory-intensive transformation job can have its own profile with higher pod limits, while lighter workloads share a more constrained default.
Because Run Profiles are managed in TMC, data engineers can adjust execution parameters within the boundaries the platform team has established, without needing cluster access.Talend Run Profiles defined in Talend Management Console allow you to define resource configuration, JVM arguments, and Kubernetes Pod resources, allowing for finer declaration of your configuration.
Upgrades Without the Usual PainUpgrade management is one of the clearest operational advantages Dynamic Engine has over the Remote Engine model, and it's worth walking through how it works.
When a new version is available, TMC surfaces the notification directly in the console. Applying it uses the same manifest or Helm commands as the original deployment. The critical difference from Remote Engine is that running jobs and tasks are not interrupted. Kubernetes' rolling update mechanism brings up new engine pods, validates them, and only then terminates the old ones. The engine's control capabilities remain available throughout.
If something goes wrong during an upgrade, such as a misconfigured values file, an incompatible cluster state, an image pull error, the engine will roll back to the previous working configuration automatically. There's no manual recovery process and no separate rollback procedure to maintain.
The same rolling update behavior applies to your own Data Services and Routes. Deploying an updated version through TMC uses Kubernetes'
RollingUpdatestrategy, keeping your application available as the new version comes up and gets exposed.Monitoring What Matters
TMC is the primary operational window into running workloads. For batch Data Integration jobs, the signals that matter are execution status, duration, and log output. For always-on Data Services and Routes, the primary health indicator is replica status and the desired replica count versus those currently running, which tells you immediately whether your services are healthy.
Fitting Into Your Kubernetes Ecosystem
One of the practical advantages of a Kubernetes-native architecture is that Dynamic Engine and its Environments slot into the tooling your team already uses. Standard Kubernetes-compatible monitoring, logging, telemetry, and egress solutions work without customization. You're not adopting a proprietary observability stack or working around a closed runtime, you're running containers in namespaces, and that means Prometheus, Grafana, Fluentd, your GatewayAPI of choice, and whatever else your platform team has standardized on, all apply.
Logs and metrics can be collected and sent to your logging and operational monitoring systems.
When Dynamic Engine Is the Right Call
The architectural differences between Remote Engine and Dynamic Engine aren't superficial. Across nearly every dimension like runtime model, concurrency, cost, and job isolation, the two runtime engine types reflect fundamentally different assumptions about how data integration infrastructure should work.
Core Architectural DifferencesFeature Remote Engine Dynamic Engine Runtime Model Persistent OSGi container, long-running JVM Kubernetes-native; ephemeral pods/execution Concurrency 3 parallel tasks (default, configurable per engine) Bound by cluster capacity; no per-engine cap Resource Model Always-on; cost is continuous regardless of use On-demand; cost scales with execution time Job Isolation Shared JVM for concurrent tasks Per-pod isolation; namespace per execution Infrastructure Customer VMs (EC2, Azure VM, on-prem) Customer Kubernetes (EKS, AKS, GKE, etc.) Upgrades Manual installation per engine VM TMC-notified; rolling update, rollback on failure Impact to Running Jobs Engine restart affects running jobs Engine upgrades do not affect running jobs OSGi Artifacts Fully supported Not recommended; use Route or Data Service as microservice deployment type Routes / Data Services Supported (OSGi model) Supported (container-image model)
Those differences translate directly into the workload patterns where Dynamic Engine has a clear advantage.Variable demand workloads where nightly batch spikes, end-of-month processing, and event-driven integration, for example, benefit directly from on-demand compute. You stop paying for headroom that you only need occasionally.
Multi-tenant and regulated workloads get genuine namespace-level isolation with Kubernetes RBAC, without the overhead of separate clusters per tenant.
If Kubernetes is already part of your infrastructure, the marginal overhead of adding Dynamic Engine is low. The operational model is already familiar.
When concurrent job volume outgrows what a Remote Engine fleet can reasonably handle, Dynamic Engine's use of Kubernetes' Horizontal Pod Autoscaler is the natural path forward.
And for teams spending meaningful engineering time maintaining execution agents and managing engine upgrades, the TMC-managed upgrade model and automatic rollback free that time up for work that actually moves the needle.
Where This Is Heading
Dynamic Engine reflects a broader direction the data integration industry is moving: toward serverless, consumption-based models where infrastructure costs track actual usage rather than theoretical peak capacity. As organizations continue to mature in their cloud adoption, that kind of flexibility stops being a differentiator and becomes a baseline expectation.
Qlik Talend Cloud's Dynamic Engine gets you there without trading away the reliability and feature depth that enterprise data teams depend on. The architecture is sound, the operational model is genuinely simpler than what it replaces, and it fits into the Kubernetes-native infrastructure patterns most platform teams are already building toward.
Practical Guidance for Getting StartedA few design and deployment patterns that tend to matter in practice:
Start with modest resource configurations and scale based on actual metrics. Over-provisioning environments is easy and expensive, and that's one of the things Dynamic Engine is designed to help you avoid.
Keep environments separated by purpose. Dev, UAT, and production should be distinct DEEs. The namespace isolation is there, so use it to prevent accidental cross-environment interference.
Design Talend jobs for parallelism where it matters. Stateless job design, or managing state externally to ephemeral container storage, lets you fully exploit Dynamic Engine's ability to run multiple instances simultaneously.
Use TMC's monitoring to track execution times, resource utilization, and cost over time, and treat that data as feedback for configuration refinement. The consumption-based cost model rewards this kind of attention.
Wrapping UpThe per-pod isolation and environment-level namespace separation add up to something concrete. Execution environments stay clean and isolated without the overhead of separate clusters per tenant or business unit, and infrastructure absorbs variable demand without manual intervention. For organizations managing multi-tenant workloads or a growing portfolio of data integration pipelines, those properties translate directly into fewer incidents caused by shared runtime state and more predictable operations at scale.
The technical foundation makes that possible without trading away operational control. Your cluster, your security policies, your observability stack. Dynamic Engine fits into the infrastructure you're already running rather than replacing it.
- Cost: Eliminating idle resources can substantially reduce infrastructure costs compared to persistent engine models.
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The AI-Ready Enterprise: What Separates Leaders from Everyone Else
Nearly every enterprise has an AI strategy. Far fewer have an AI-ready enterprise. The difference matters. As AI moves from experimentation to operati... Show MoreNearly every enterprise has an AI strategy. Far fewer have an AI-ready enterprise.
The difference matters. As AI moves from experimentation to operational decision-making, the organizations pulling ahead are not necessarily those with the biggest budgets, the largest data science teams, or access to the newest models. What makes the biggest difference between a promising pilot and a system you can trust in production is Enterprise AI Readiness, and it is also the single biggest predictor of which organizations turn AI ambition into outcomes.
The numbers make the gap hard to ignore. In Qlik's 2025 Agentic AI Study, 97% of large enterprises have funding committed to agentic AI, yet only 18% have fully deployed it. Nearly four in ten are planning investments above $1 million, but 46% say meaningful scale is still three to five years away. Budget is not the constraint. Readiness is.
For data and analytics leaders, that reframes the job. AI readiness is not a one-time project that ends when a use case ships. It is an enterprise must-have capability you build, measure, and keep, in the same way you treat security or reliability. And like those capabilities, it is made of distinct, connected parts that all have to hold.
A framework for what "ready" actually means
It helps to picture readiness as a wheel. At the center sits AI readiness itself. Around it are five dimensions that must work together: Strategy, People, Technology, Data, and Data and AI Governance. Each dimension breaks down further into spokes, and a single weak spoke is usually what trips an otherwise promising program.
The value of looking at readiness this way is that it stops you from over-investing in one area while a neighboring gap quietly undermines it. A brilliant strategy fails on poor data. Pristine data fails without governance. Strong governance fails if people are not trained to act on it. Treating readiness as a connected system, rather than a list of point fixes, is what separates organizations that scale from those that keep restarting pilots. Here is what each dimension demands, and where the work actually lands.
Strategy: plan the effort and define success up front
The strategy dimension is about making informed decisions before you build. Its four spokes are opportunities, objectives, alignment, and governance of the initiative itself. In practice that means choosing the AI use cases worth pursuing, setting measurable objectives, aligning those objectives to business priorities, and deciding how initiatives get approved and funded.
This is where many enterprises look stronger than they are. In Qlik's study, 69% now report a formal AI strategy, up from 37% a year earlier, yet only 19% have a defined framework for measuring return. That gap between having a plan and being able to prove it worked is a strategy problem, and it is the one most likely to erode executive patience. Readiness here means picking opportunities you can instrument, and defining success metrics on day one rather than reverse-engineering them after launch.
For teams that want a structured starting point, the “AI & Data readiness assessment” from Qlik Advisory Services helps prioritize use cases, set objectives, and align AI initiatives to business outcomes before the build begins.
People: build the skills and the mindset
Technology does not adopt itself. The people dimension covers training, talent, ethics, and culture, the human capacity to use AI well and responsibly. Its spokes ask whether your teams have the skills to build and operate AI, whether you can attract and keep the right talent, whether people apply an ethical lens to how AI is used, and whether the culture actually trusts and acts on AI-driven insight.
The data shows why this matters. Lack of internal expertise ranked as the third-largest barrier to deployment, cited by 48% of enterprises, and only 42% express confidence in their internal skills. You can buy a platform that delivers a set of capabilities, but a culture that treats AI outputs as suspect, or worse, accepts them uncritically, will undermine even a well-governed system. Readiness here is less about headcount and more about fluency: enough literacy across the organization that trusted insight gets used, and questionable output gets questioned.
Technology: choose architecture that scales
The Technology dimension is the engine room: infrastructure, security, scalability, and software. The spokes ask whether your architecture can run AI workloads reliably, whether it is secure, whether it scales as demand grows, and whether your tools are efficient enough to operate at production volume rather than just in a demo.
This is where readiness becomes concrete for data and analytics leaders. AI workloads are unforgiving of brittle infrastructure. Pipelines that work for a quarterly report fall over when an agent queries them continuously. An open, scalable foundation matters here, because lock-in and rigid architecture are how technology debt quietly caps your AI ambitions. This is what Qlik's data foundation for AI is built to provide: trusted, open, scalable architecture, including an open lakehouse foundation, so workloads can grow without forcing a re-platforming every time demand steps up.
Data: build a pipeline you can trust
If one dimension decides the outcome, it is this one. The Data dimension covers quality, privacy, strategy, and compliance, and its job is to build a pipeline that ensures accuracy, reliability, and availability for AI. The spokes ask whether your data is accurate and complete, whether sensitive data is protected, whether you have a coherent strategy for sourcing and managing it, and whether it meets regulatory requirements.
This is also where most programs stall. Data quality, availability, and access were the number one barrier in Qlik's study at 56%, with integration second at 49%. Models are only as trustworthy as the data feeding them, and an agent that acts on bad data does not just produce a wrong answer, it takes a wrong action.
This is the core of where Qlik helps. Qlik Talend Cloud brings data integration, data quality, and governance into one environment, so you can move data from across your estate, build pipelines, and apply quality rules without stitching together disconnected tools. The goal is not just faster movement of data. It is delivering data your teams and your models can actually rely on, with accuracy, reliability, and availability built in rather than checked after the fact. Self-service access and interoperability then shorten the path from question to trusted answer, so analysts are not waiting in a central queue to put governed data to work.
Data and AI governance: the framework that holds it together
Governance is drawn as the dimension wrapping the rest, and that placement is deliberate. Its spokes are trust, privacy, ModelOps, and explainability: a framework that ensures AI is developed and used responsibly. It asks whether you can trust your data and models, protect privacy, operate and monitor models over their lifecycle, and explain how decisions get made.
Governance is shifting from a "should we" conversation to a "what did we get, and can we prove it was sound" conversation. As agentic AI starts taking action rather than just summarizing, the cost of ungoverned data rises sharply. This is where Qlik Trust Score for AI earns its place: it gives every data product a visible signal scored across dimensions such as accuracy, timeliness, completeness, and diversity, so teams can inspect readiness before a decision or an automated action depends on it. Paired with data products, anomaly detection, continuous observability, and agent-assisted stewardship, governance stops being a manual gate and becomes an operating standard built into how data is produced and consumed. That is also what lets you reduce risk while you scale, because problems surface before they reach a decision.
Readiness is a muscle, not a milestone
Look at the wheel as a whole and the lesson in the 97%-funded, 18%-deployed gap comes into focus. The funding proves the ambition is real. Closing the distance means strengthening every spoke, and refusing to let a strong strategy mask a weak data foundation or thin governance.
The organizations pulling ahead are not the ones with the largest AI budgets. They are the ones treating readiness as an enduring, measurable capability, investing in trusted data, scalable architecture, and built-in governance as deliberately as they invest in the models themselves. That work spans the organization, but it is anchored in data and analytics, which puts data leaders in the position to lead it.
Want to see where your foundation stands across the wheel? Explore why data and analytics leaders choose Qlik to build AI readiness that scales. Or start with the data dimension directly: try Qlik Talend Cloud free for 14 days and see what trusted, AI-ready data looks like in your own environment.
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Techspert Talks - Qlik Cloud Admin 101
Hi everyone, Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series where you can get first-hand ins... Show MoreHi everyone,
Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series where you can get first-hand insights from Qlik experts.The Techspert Talks session from June looked at Qlik Cloud Admin 101.
But wait, what is it exactly?
Techspert Talks is a free webinar held on a monthly basis, where you can hear directly from Qlik Techsperts on topics that are relevant to Customers and Partners today.In this session we will cover:
- Important settings
- Space and App management
- Users, Groups and Roles
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Qlik Replicate May 2026: Built to Keep Running
There's a moment in Michael Stipe's 1991 classic "Losing My Religion" where he sings "that's me in the corner, that's me in the spotlight." He was tal... Show MoreThere's a moment in Michael Stipe's 1991 classic "Losing My Religion" where he sings "that's me in the corner, that's me in the spotlight." He was talking about the agony of unrequited feeling - but he could just as easily have been describing the quiet, unsung heroism of the enterprise data engineer managing their replication infrastructure. Nobody puts CDC pipelines in the spotlight. They just expect them to work.
They do, of course. Until they don't. And when they don't, then everyone notices.
Qlik Replicate May 2026 is a release that takes that "until they don't" problem seriously. Three new endpoints, sixteen features and enhancements, and five deep-dive items that collectively add up to: less manual intervention, better resilience under real-world conditions, and meaningful expansion of where your data can land. Here's what matters - and why it's worth upgrading.
SAP HANA: two improvements that make life easier
We’re committed to making life as easy as possible for our SAP customers. These two new enhancements address well-known, frequently encountered pain points that, until now, required cumbersome workarounds.
The first is CDC artifact cleanup for Trigger-Based CDC mode. Replicate creates artifacts in the source HANA database during CDC operations - and without a clear cleanup policy outside of Replicate, they can accumulate over time, consuming disk space and adding operational drag. The new deletion policy configuration lets teams define when these artifacts are removed automatically, turning a recurring maintenance task into a set-and-forget configuration (just like our pipelines).
The second is more significant: CDC continuity on dropped triggers. SAP application upgrades frequently drop foreign triggers from HANA databases - it's a known behaviour, and it has always carried a disproportionate consequence. When Replicate detected missing triggers, it fell back to a full table reload. For large tables, that could mean lengthy delays of data outage on a production system, sometimes spanning over days.
The new "Continue CDC on Dropped Triggers" option keeps replication running when triggers are temporarily absent. Best practice is to enable it before anticipated SAP upgrade windows and disable it afterwards - but that's a minor operational step compared to the alternative should the worse happen.
To put the stakes in context: a global healthcare organisation running Trigger-Based CDC on SAP HANA uses Qlik Replicate to process around 5 billion records per quarter during peak pricing cycles, with change volumes reaching 100 million records per hour from a single large table. Qlik Replicate has helped them reduce data latency from ten hours to under 30 minutes. In an environment operating at that scale, a multi-day reload isn't a theoretical inconvenience - it's a commercial event. This enhancement was built with exactly that kind of environment in mind.
Qlik Replicate SAP HANA endpoint config screen
The mainframe gets a modern integration path
The new IBM IMS source endpoint replaces the need for our legacy ARC (Attunity Replicate Connect) extension entirely, and it arrives with the kind of improvements that matter in 2026: modern mTLS security, a minimal z/OS footprint, IMS catalog metadata support, and the ability to run alongside ARC during migration - so there's no forced cutover window, no downtime risk.
With the IMS endpoint now on the same modern footing as the rest of the mainframe connector family, organisations with IMS-resident data have a credible, enterprise-grade path to power analytics and operational use cases at a scale that demands both low latency and continuous availability.
We’d love to hear from organisations using IMS. If you would like to try the new IMS source endpoint, please reach out to Qlik support.
DB2 for LUW: a native endpoint, finally
Fun Fact: IBM DB2 was the first relational database ever built, created in the 1970s. IBM wanted to call it DB1, but the name was already taken by a database developed in Israel. So DB2 it became, and the rest is history, as they say.
DB2 for LUW has been supported by Replicate for a long time; however, not as a native Replicate target endpoint - until this release.
The new IBM DB2 for LUW target endpoint allows data to land directly into Db2 for Linux, Unix and Windows from any Replicate-supported source. Previously, the generic ODBC target endpoint was the only method available to replicate to a Db2 LUW environment. While this route was functional, performance was not optimal, and it did not support some Db2-specific capabilities. The new native endpoint closes that gap with meaningfully better performance and proper support for Db2 data types, including LOBs and XML.
The same global healthcare organisation mentioned above has already identified the DB2 for LUW target as a logical next step in their pipeline evolution - a sign of where this endpoint sits in real enterprise roadmaps.
⚠️Note:⚠️ DB2 LUW 11.1 and 11.5 are now end-of-life in this release. Customers on those versions will need to upgrade to DB2 LUW 12.1 or higher before using the new endpoint.
BigQuery streaming: lower cost, lower latency
The Google Cloud BigQuery target endpoint gains a new loading method in this release: streaming via the Storage Write API, as an alternative to the existing batch loading approach.
The practical benefits are straightforward. Streaming reduces data latency significantly compared to batch loading, lowers cost by eliminating the need to stage files before loading, removes daily quota constraints on load jobs, and guarantees once-only delivery - no duplicate rows on retries. For organisations running near-real-time analytics pipelines into BigQuery, this is a meaningful step up.
The switch is a single dropdown selection in the endpoint settings - Batch Loading or Streaming - and batch loading remains the default for those who prefer to stay with the existing behaviour. As always, it's worth reviewing the documented limitations before switching, as not all table types and workloads are compatible with Streaming mode.
More of what makes Replicate reliable
The headline features get the attention, but some of the most valuable work in any release happens in the details - the fixes and enhancements that mean fewer escalations, fewer manual interventions, and fewer late nights for you.
In this release: MS-CDC source endpoints now activate CDC only on the columns you're actually replicating, rather than all columns regardless of what's included in the task. Tables with encrypted columns or newly added DDL columns - which previously suspended replication and required manual recovery - now handle gracefully and keep running. Microsoft Fabric Mirrored Database loading is more reliable after stop/resume cycles, and now supports schema evolution via ADD COLUMN DDL. MongoDB source adds support for large change stream events and more flexible JSON handling.
A new Cloudera Iceberg target endpoint rounds out the three new additions - enabling full CDC and Full Load replication into Apache Iceberg tables on Cloudera Data Platform without custom connectors, as open table formats continue to cement their place in enterprise lakehouse architectures.
OAuth authentication is now available for Kafka and Confluent Cloud targets. Data type fidelity improves across SQL Server, Databricks, and Microsoft Fabric. And a small but welcome UX improvement: endpoint configuration fields that previously required placing certificate files at specific paths on the Replicate Server machine now include a Browse button - the file is stored securely within the endpoint settings, and the local copy can be deleted. A small change that removes a surprisingly persistent source of friction.
Qlik Replicate May 2026 is a release built on the same principle that makes replication infrastructure worth trusting: it should keep running, whatever the real world throws at it. The mainframe gets a modern IMS integration path. SAP HANA customers get resilience against the trigger-drop reload. DB2 for LUW gets the native endpoint it always deserved. And BigQuery gets faster and cheaper.
Not in the spotlight. Just keeping the data moving.
As always, each new release is fully supported for two years. To check the status of support for your currently installed version, please see the relevant product lifecycle pages.
- Qlik Replicate Product Lifecycle
- Qlik Compose Product Lifecycle
- Qlik Enterprise Manager Product Lifecycle
We hope you enjoy using Qlik Talend Data Integration & Quality products and would love to hear your feedback and success stories, especially in any improvements you achieve.
To get the latest versions, please visit the Downloads and Release Notes section on Qlik Community. To learn more about what is included in these releases, be sure to check out the Release notes, which are available here.
To obtain any of these releases, go to the Qlik Downloads Site in the Community and filter "Product Category" by "Qlik Data Integration", and then select the product and the versions you would like to download. For most products, selecting "Latest release and patch" under the "Show Releases" should be enough. If required, you can filter further by selecting the latest "Release" and/or Service Release (SR) version under "Release Number".
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Qlik Cloud webhooks: Migrate Qlik Automate workflows to CloudEvent format by Oct...
Qlik is removing legacy attributes from webhook payloads on October 6th, 2026. Unless updated to use the new attributes, this change will break webhoo... Show MoreQlik is removing legacy attributes from webhook payloads on October 6th, 2026. Unless updated to use the new attributes, this change will break webhook-triggered automations that rely on the removed fields.
During the transition, some events include both legacy and CloudEvent fields, but this is temporary. Once CloudEvent-only enforcement begins, automations that still rely on legacy fields can fail.
We have created a guide that explains:
- How to identify affected automations,
- How to update the automations,
- How to validate them,
- And return them safely to production.
Read more in Qlik Cloud webhooks: Migrate Qlik Automate workflows to CloudEvent format.
If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat. For other Qlik Automate related questions, head over to the Qlik Automate forums.
Thank you for choosing Qlik,
Qlik Support -
Qlik DataTransfer Deprecation Notice for Q1 2026
Qlik DataTransfer will be officially End-of-Life by the end of Q1 2026. It will be removed from the Product Downloads site later this year and will no... Show MoreQlik DataTransfer will be officially End-of-Life by the end of Q1 2026.
It will be removed from the Product Downloads site later this year and will no longer be available for new installations or upgrades. Qlik will provide support until April 30, 2026.
While Qlik Data Transfer is deprecated and will no longer receive fixes or support starting April 30th, 2026, we expect it to function until November 29th, 2026, at which point it will have reached EOL (End of Life).
To ensure a smooth transition, we recommend you begin utilizing Qlik Data Gateway – Direct Access, the supported alternative.
Note that the initial release of Qlik DataTransfer (November 2024, version 10.4.0) will not work after June 24th, 2025. If you still need to use Qlik DataTransfer beyond June, upgrade to the Service Release version 10.4.4.
How do I get started with Qlik Data Gateway - Direct Access?
We have compiled a list of resources to assist you in adopting Qlik Data Gateway - Direct Access:
- Setup Guide: Qlik Data Gateway – Direct Access
- Walkthrough Video: Qlik Data Gateway – Direct Access
- Troubleshooting Video: STT - Troubleshooting Qlik Data Gateway - Direct Access
Additionally, for those needing feature parity with Qlik DataTransfer, we recommend pairing with the File Connector via Direct Access, REST Connector via Direct Access, and the generic ODBC Connector.
Further Resources:
- Connector Factory Blog: Connector Factory January and February 2025 Releases
- SaaS in 60: File Connector via Direct Access Data Gateway - SaaS in 60
We will share an update later this year with the exact deprecation and end-of-support dates.
For assistance, please contact Qlik Support. Questions on how to contact Qlik Support.
Thank you for choosing Qlik,
Qlik Support -
Important update on SAP Data Access Restrictions and your Qlik Integration: SAP ...
Dear Qlik Customers, In April 2026, SAP published a series of updates that will restrict your ability to extract certain SAP data using Qlik products.... Show MoreDear Qlik Customers,
In April 2026, SAP published a series of updates that will restrict your ability to extract certain SAP data using Qlik products. We are writing to explain what is changing, what it means for your Qlik integration, what you can do next, and how Qlik is responding.SAP Announcement
SAP has recently updated SAP Note 3255746 and its API Policy, which together prevent customers from using the ODP-RFC interface to perform bulk data extractions to non-SAP systems. Here is what you need to know.
Impacted Qlik Products:
The CDC connectivity via ODP will be at most risk by the implementation of the June 9th SAP security update, as it will be blocked. The main products affected are:
- Qlik Talend Cloud SAP ODP Connector
- Qlik Replicate SAP ODP Endpoint
Other impacted products that also offer ODP functionality are:
- Qlik Analytics (Qlik Sense / QlikView) SAP ODP Connector
- Talend Studio Component tSAPODPInput (see SAP ODP-RFC Changes: What Talend Studio Customers Need to Know)
- Talend Studio Component tSAPADSOInput using RFC-based access to Advanced DataStore Objects (ADSO), relying on the ODP API (see SAP ODP-RFC Changes: What Talend Studio Customers Need to Know)
The following Qlik Products do not use the ODP-RFC interface and therefore will not be impacted by the June SAP Security update:
- Qlik Gold Client
What's Changing and When:
- June 9, 2026: SAP released a security patch through SAP Note 3635619 blocking “unrestricted” calls from non-SAP applications, including affected Qlik connectors, that rely on the ODP-RFC interface. Depending on your SAP system’s component levels, your SAP System Administrator may apply the SAP Note independently or as part of an SAP Support Package (refer to SAP Note 3635619).
- Temporary revert option: According to SAP Note 3731818, there is an option to allow restricted ODP-RFC calls on systems with the security patch installed to proceed without interruption. Implementing SAP Note 3635619 will add an opt-out program which allows disables the ODP-RFC restrictions. SAP Note 3731818 also states that another SAP Note will be provided at the end of 2026 to permanently disable the opt-out program and reimplement the block on unrestricted ODP-RFC calls from non-SAP applications.
Recommended Next Steps
- Review the SAP Notes: Independently review SAP Note 3255746 (reclassification notice), SAP Note 3439624 (self-assessment tool), SAP Notes 3748819 and 3635619 (security patch for ODP-RFC), and SAP Note 3731818 (security patch opt-out program) via the SAP Support Portal at support.sap.com. Address your concerns and feedback through the appropriate SAP channels.
- Contract Review: Review your customer agreement and the API Policy, updated in April, 2026, to evaluate SAP’s ability to make unilateral changes to your data access rights, and to require additional purchases or charge egress fees.
- Audit your environment: SAP has released a self-assessment tool (SAP Note 3439624, available April 13, 2026) to help you identify all current ODP-RFC usage across your systems.
- The Qlik SAP ODP Connector will show as “unpermitted”, which means it will be blocked by the SAP June Security patch
- The Qlik SAP OData Connector will show as “permitted”, which implies it will continue to work with the SAP June Security
patch
- Evaluate Qlik’s Alternative SAP Connectors: Qlik supports several methods for SAP connectors, including OData and SAP Extractors. We continue to enhance our connectors and are looking to release a new SAP connector due later this year, which may prove a viable alternative.
- Work with Qlik: Reach out to your Qlik representative before implementing the SAP June Security patch, so we can partner with you on alternate Qlik solutions and ways to address these SAP restrictions. Together, we can evaluate your specific environment, understand your connectivity needs, and align on the best path forward before the December 2026 deadline.
Our Commitment
You should be in control of your business-critical data, wherever it creates value. Without interoperable architectures, SAP customers like you face higher costs, performance issues, and less freedom to choose, including loss of flexibility in implementing AI.
Qlik already supports alternatives to ODP and is actively developing additional migration paths and solutions to preserve your flexibility through this transition and beyond. We will continue to work on viable alternatives to our ODP endpoints and help you navigate this change on your terms, not SAP's. To learn more about how Qlik can support your specific environment, please reach out to your Qlik representative.
Additional Resources
- SAP’s Operational Data Provisioning (ODP) Update Public FAQ
- SAP Note 3255746 (reclassification notice) - Accessible via the SAP Support Portal at support.sap.com
- SAP Note 3439624 (self-assessment tool) - Accessible via the SAP Support Portal at support.sap.com
- SAP Note 3748819 and SAP Note 3635619 (security patch for ODP-RFC) - Accessible via the SAP Support Portal at support.sap.com
- SAP Note 3731818 (security patch opt-out program) - Accessible via the SAP Support Portal at
- support.sap.com
- “Open” on Whose Terms? What SAP Sapphire Won't Say. by Matt Hayes – Qlik GM Data Business Unit
- SAP ODP-RFC Changes: What Talend Studio Customers Need to Know Qlik Support article by Rodrigo Martins
This communication reflects Qlik's current interpretation of SAP's restrictions, which are outside our control and subject to change. Customers should consult SAP's official communications and seek independent guidance on operational and legal impact. Roadmap statements are forward-looking and not commitments; disruptions arising from SAP's decisions do not constitute a defect in Qlik's products or services.
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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 More2026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。」 -
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 MoreWe'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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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 MoreRegulatory 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 -
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 MoreTechnology 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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【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。 -
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 MoreAt 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:
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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 MoreThe 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!!
