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  Support Updates

  Qlik Reporting Service metering update for Word and PowerPoint beginning August ...

  At the beginning of 2026, the Qlik Reporting Service expanded its offering with support for Microsoft Word and Microsoft PowerPoint report capabilitie... Show More

  At the beginning of 2026, the Qlik Reporting Service expanded its offering with support for Microsoft Word and Microsoft PowerPoint report capabilities. For the duration of the first half of the year, these report formats have been executing in an unmetered state.

  As of August 1st, 2026, these report formats will be metered. Customers using Word and PowerPoint reports will notice an increase in the report execution metrics.

   

  What does this mean for me?

  If you are a customer making use of these report formats, you should review your consumption and ensure that your Qlik Reporting Service quota is adequate for your aggregate report consumption at this time.

   

  How do I know how many Word and PowerPoint reports are being sent?

  • Talk to your report developers, they would have a good understanding of which report task are in production and how many reports are being executed
  • For an aggregate view across your tenant, use the Report Analyzer app as per Qlik Cloud Monitoring Apps Workflow Guide 

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-07-14 / 08:00 AM

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  Support Updates

  Upcoming deprecation of Qlik Analytics charts in May 2027

  Qlik constantly refines its Analytics, over time replacing old charts with new, modernized alternatives. These deprecations are announced well in adva... Show More

  Qlik constantly refines its Analytics, over time replacing old charts with new, modernized alternatives. These deprecations are announced well in advance and come with instructions on how to best replace these old charts, whether that is to use a new one, several new ones, or make use of new settings.

  This blog post covers the deprecation of charts in May 2027 and offers you guidance on how to replace them.

   

  What charts are being deprecated and when?

  The following seven visualization bundle charts are up for deprecation in May 2027. Most have already been removed from the asset panel and are no longer a part of recent applications.

  • The Bar and Area chart
  • The old Bullet chart
  • The old Heatmap chart
  • The Button for navigation
  • The Share button
  • The Show/Hide container
  • The old Tabbed container
  • The Multi KPI

   

  What do I use instead?

  Since these are old charts, most are no longer in use. If you happen to still have a very old application and need to replace deprecated charts, see Visualization bundle > Deprecated charts for more information on what to use instead.

   

  How do I find out if my apps are using the deprecated charts?

  Qlik recommends reviewing your apps for old charts. Depending on your platform (Qlik Cloud or Client-managed), there are different methods you can deploy.

   

  Qlik Cloud

  Qlik Cloud administrators should use the Qlik Cloud Monitoring Apps to track the usage. The App Analyzer has a sheet dedicated to where deprecated charts are being used on a tenant in Qlik Cloud. The App Analyzer is based on usage events rather than scanning every app. Use the App Analyzer to find which apps and sheets have charts that need to be updated to newer and more modern alternatives. The easiest way to install and update the Qlik Cloud Monitoring Apps is to use the automation template. If you already have the App Analyzer, just remove the automation and install a new one to get the latest version of the App Analyzer. 

   

  Client-managed

  For client-managed installations, use the Content Monitor app, included in every Qlik Sense Enterprise on Windows installation beginning with the May 2026 release.

  The Content Monitor has a sheet for tracking deprecated charts. At reload, the Content Monitor app scans every app in the installation to list all applications and sheets that are using charts that are being deprecated. It also lists the installed extensions and their deprecation status.

  If you want to track usage in prior versions, download the Qlik Sense Content Monitor from the Qlik download page. It comes bundled with the required Object Scanner. 

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-03-03 / 08:00 AM

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  Subprocessors List

  Qlik and Talend Subprocessors - Version 7.0 - 17th March 2026

  Version 7.0 Current as of: 17th March 2026 Qlik and Talend, a Qlik company, may from time to time use the following Qlik and Talend group companies an... Show More

  Version 7.0 Current as of: 17th March 2026

  Qlik and Talend, a Qlik company, may from time to time use the following Qlik and Talend group companies and/or third parties (collectively, “Subprocessors”) to process personal data on customers’ behalf (“Customer Personal Data”) for purposes of providing Qlik and/or Talend Cloud, Support Services and/or Consulting Services.

  Qlik and Talend have relevant data transfer agreements in place with the Subprocessors (including group companies) to enable the lawful and secure transfer of Customer Personal Data.

  You can receive updates to this Subprocessor list by subscribing to this blog or by enabling RSS feed notifications.

  Third party subprocessors for Qlik Cloud 

  Third Party	Location of processing (e.g., tenant location)	Service Provided/Details of processing	Address of contracting party	Contact
  Amazon Web Services (AWS)	See Qlik Cloud locations at: https://www.qlik.com/us/regions	Qlik Cloud is hosted through AWS	Amazon Web Services, Inc.  410 Terry Avenue North, Seattle, WA 98109-5210, U.S.A	AWS AWS Privacy
  MongoDB	See Qlik Cloud locations at: https://www.qlik.com/us/regions	Any data inputted into the Notes feature in Qlik Cloud	Mongo DB, Inc. 229 West 43rd St., New York, NY 10036 USA	MongoDB
  Third party subprocessors for Qlik mobile device apps
  Google Firebase	United States	Push notifications	Google LLC 1600 Amphitheatre Parkway Mountain View California United States	Google

   

   

  Third party subprocessors for Talend Cloud

  Third Party	Location of processing   (e.g., tenant location)	Service Provided/Details of Processing	Address of contracting party	Contact
  Amazon Web Services (AWS)	See Talend Cloud locations at: https://www.qlik.com/us/regions	These Talend Cloud locations are hosted through AWS	Amazon Web Services, Inc. 410 Terry Avenue North, Seattle, WA 98109-5210, U.S.A.	AWS AWS Privacy
  Microsoft Azure	See Talend Cloud locations at: https://www.qlik.com/us/regions	These Talend Cloud locations are hosted through Microsoft Azure	Microsoft Corporation 1 Microsoft Way, Redmond, WA 98052, USA	Microsoft Enterprise Service Privacy Microsoft Corporation 1 Microsoft Way Redmond, Washington 98052 USA
  MongoDB	See Talend Cloud locations at: https://www.qlik.com/us/regions	Any data inputted into the DataPrep and Stewardship modules of Talend Cloud. Hosted in the same region as the customer’s Talend Cloud environment on AWS or Microsoft Azure, as selected by the customer.	Mongo DB, Inc. 229 West 43rd St., New York, NY 10036 USA	MongoDB

   

  Third party subprocessors for Qlik and Talend Support Services and/or Consulting Services

  The vast majority of Qlik’s support data that it processes on behalf of customers is stored in Germany (AWS). However, in order to resolve and facilitate the support case, such support data may also temporarily reside on the other systems/tools below.
  Third Party	Location of processing (e.g., tenant location)	Service Provided/Details of processing	Address of contracting party	Contact
  Amazon Web Services (AWS)	Germany	Support case management tools	Amazon Web Services, Inc.  410 Terry Avenue North, Seattle, WA 98109-5210, U.S.A.	AWS AWS Privacy
  Salesforce	UK	Support case management tools	Salesforce UK Limited Village 9 Floor 26 Salesforce Tower 110 Bishopsgate London, UK EC2N 4AY	Salesforce
  Microsoft	United States	Customer may send data through Office 365	Microsoft Corporation One Microsoft Way, Redmond, WA 98052 USA	Chief Privacy Officer One Microsoft Way, Redmond, WA 98052 USA
  Ada	Germany	Support Chatbot	Ada Support 371 Front St W, Unit 314 Toronto, Ontario M5V 3S8 CANADA	Ada
  Persistent	India	R&D Support Services	2055 Laurelwood Road Suite 201 Santa Clara, California 95054 USA	Persistent
  Atlassian (Jira Cloud)	Germany, Ireland (Back-up)	R&D support management tool	350 Bush Street Floor 13 San Francisco, CA 94104 United States	Atlassian
  Stretch Qonnect APs	Denmark	2nd line support for the Qlik Analytics Migration Tool	Kompagnistræde 21 1208 Copenhagen Denmark	Martin Sahlin
  GlobalLogic	India	Customers who utilize Cloud migration services support	2535 Augustine Drive, Suite 500 Santa Clara, CA 95054, USA	GlobalLogic

   

   

  Affiliate Subprocessors for Qlik and Talend

  Affiliate Subprocessors     These affiliates may provide services, such as Consulting or Support, depending on your location and agreement(s) with us. Our Support Services are predominantly performed in the customer’s region:    EMEA – France, Sweden, Spain, Israel; Americas – USA; APAC – Japan, Australia, India.
  Subsidiary Affiliate	Location of processing (e.g., tenant location)	Service Provided/Details of Processing	Address of contracting party	Contact
  QlikTech International AB	Sweden	These affiliates may provide services, such as Consulting or Support, depending on your location and agreement(s) with us. Our Support Services are predominantly performed in the customer’s region: EMEA – France, Sweden, Spain, Israel; Americas – USA; APAC – Japan, Australia, India.	Scheelevägen 26 223 63 Lund Sweden	DPO
  QlikTech Nordic AB	Sweden			DPO
  QlikTech Latam AB	Sweden
  QlikTech Denmark ApS	Denmark		Dampfaergevej 27-29, 5th Floor 2100 København Ø Denmark
  QlikTech Finland OY	Finland		Simonkatu 6 B 5th Floor FI-00100 Helsingfors Finland
  QlikTech France SARL, Talend SAS	France		93 Ave Charles de Gaulle 92200 Neuilly Sur Seine France
  QlikTech Iberica SL (Spain)	Spain		"Blue Building", 3rd Floor Avinguda Litoral nº 12-14 08005 Barcelona Spain
  QlikTech Iberica SL (Portugal liaison office), Talend Sucursal Em Portugal	Portugal
  QlikTech GmbH	Germany		Joseph-Wild-Str. 23 81829 München Germany
  QlikTech GmbH (Austria branch)	Austria		Am Euro Platz 2, Gebäude G A-1120, Wien, Austria
  QlikTech GmbH (Swiss branch)	Switzerland		c/o Küchler Treuhand Brünigstrasse 25, CH-6055 Alpnach Dorf Switzerland
  QlikTech Italy S.r.l.	Italy		Piazzale Luigi Cadorna 4 20123 Milano (MI)
  QlikTech Netherlands BV	Netherlands		Evert van de Beekstraat 1-122 Building B, 6th Floor 1118 CL Schiphol
  QlikTech Netherlands BV (Belgian branch)	Belgium		Culliganlaan 2D 1831 Diegem
  Blendr NV	Belgium		Bellevue Tower Bellevue 5, 4th Floor, Ledeberg 9050 Ghent Belgium
  QlikTech UK Limited	United Kingdom		1020 Eskdale Road, Winnersh, Wokingham, RG41 5TS United Kingdom
  Qlik Analytics (ISR) Ltd.	Israel		1 Atir Yeda St, Building 2 7th floor 4464301, Kfar Saba Israel
  QlikTech International Markets AB (DMCC Branch)	United Arab Emirates		AB (DMCC Branch) JBC 3 Building, Cluster Y 3rd Floor, Office 301 P.O. Box 120115 Jumeirah Lake Towers Dubai
  Qlik Business Solutions Company	Kingdom of Saudi Arabia		6629 King Abdul Aziz, District King Salman, Riyadh, Kingdom of Saudi Arabia
  QlikTech Inc.	United States		211 South Gulph Road Suite 500 King of Prussia, Pennsylvania 19406
  QlikTech Corporation (Canada)	Canada		1133 Melville Street Suite 3500, The Stack Vancouver, BC V6E 4E5 Canada
  QlikTech México S. de R.L. de C.V.	Mexico		c/o IT&CS International Tax and Consulting Service San Borja 1208 Int. 8 Col. Narvate Poniente, Alc Benito Juarez 03020 Ciudad de Mexico Mexico
  QlikTech Brasil Comercialização de Software Ltda.	Brazil		51 – 2o andar - conjunto 201 Vila Olímpia – São Paulo – SP Brazil
  QlikTech Japan K.K.	Japan		105-0001 Tokyo Toranomon Global Square 13F, 1-3-1. Toranomon, Minato-ku, Tokyo, Japan
  QlikTech Singapore Pte. Ltd.	Singapore		9 Temasek Boulevard Suntec Tower Two Unit 27-01/03 Singapore 038989
  QlikTech Hong Kong Limited	Hong Kong		Unit 19 E Neich Tower 128 Glouchester Road Wanchai, Hong Kong
  Qlik Technology (Beijing) Limited Liability Company, Talend China Beijing Technology Co. Ltd.	China		51-52, 26F, Fortune Financial Center, No. 5 Dongsan Huanzhong Road, Chaoyang district, Pekin / Beijing, 100020 China
  QlikTech India Private Limited, Talend Data Integration Services Private Limited	India		“Kalyani Solitaire” Ground Floor & First Floor 165/2 Krishna Raju Layout Doraisanipalya Off Bannerghatta Road, JP Nagar, Bangalore 560076
  QlikTech Australia Pty Ltd	Australia		McBurney & Partners Level 10 68 Pitt Street Sydney NSW 2000  Australia
  QlikTech New Zealand Limited	New Zealand		Kensington Swan 40 Bowen Street Wellington 6011 New Zealand

   

  In addition to the above, other professional service providers may be engaged to provide you with professional services related to the implementation of your particular Qlik and/or Talend offerings; please contact your Qlik account manager or refer to your SOW on whether these apply to your engagement.

  Qlik and Talend reserve the right to amend its products and services from time to time. For more information, please see www.qlik.com/us/trust/privacy and/or https://www.talend.com/privacy/.

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  By QlikUpdate 2019-04-24 / 06:26 PM

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  Qlik Academic Program

  What 1,000 job postings reveal about the data analyst career in 2026

  Every year, thousands of students ask the same question: is data analytics still a good career bet? The 2026 numbers say yes. In Data Analyst Job Outl... Show More

  Every year, thousands of students ask the same question: is data analytics still a good career bet? The 2026 numbers say yes. In Data Analyst Job Outlook 2026, 365 Data Science analyzed over 1,000 real job postings to map what employers actually want from data analysts today. The report adds a wider signal, citing a US Bureau of Labor Statistics projection of 23% job growth by 2032.

  The research highlights four trends shaping the data analyst career in 2026:

  • Specialists are winning. Nearly 70% of job postings focus on a core set of skills such as SQL, statistics, and visualization rather than long lists of requirements. The takeaway from the research: master the core analytics toolkit deeply before expanding into adjacent areas.
  • Data visualization is the most in-demand skill. It appears in more postings than any other analysis skill, and dashboards remain the bridge between raw data and business decisions. Employers want analysts who can turn complex datasets into stories stakeholders understand.
  • AI is transforming the role, not replacing it. Machine learning mentions in job postings doubled year over year, and the report cites that 70% of analysts say AI automation makes their work more effective. The analysts who thrive are the ones who understand how AI-driven insights are produced and how to question them.
  • Communication is a technical skill now. Stakeholder communication appears in nearly 60% of postings, ahead of most hard skills. Analysis that no one understands is analysis that changes nothing.

  Across all four, one theme repeats: the market rewards professionals who combine hands-on analytics skills with the ability to explain, question, and communicate data. That is data literacy, and it's exactly what the Qlik Academic Program helps universities build.

  The program gives students free access to the full Qlik stack, from data integration and Qlik Cloud Analytics to Qlik Answers and agents, alongside courses, qualifications, and certifications. Students don't just read about visualization and AI-assisted analytics; they build dashboards, integrate real data, and learn how AI-driven decisions are made. Those are precisely the skills leading the 2026 hiring data.

  Professors are supported too, with training and ready-to-use teaching resources that make it easier to bring analytics into the classroom, so curricula keep pace with a job market that is evolving faster than any syllabus revision cycle.

  The report's conclusion is encouraging: the field isn't oversaturated for those with the right combination of skills. For students, the best time to start building that combination is before graduation, with real tools, real data, and credentials employers recognize.

  By giving students, professors, and universities free access to analytics software, learning content, and certifications, the Qlik Academic Program helps learners graduate ready for a career field that the data shows is still growing.

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

   

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  By TalitaBM 2026-07-14 / 12:16 PM

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  Support Updates

  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 More

  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 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

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  By Sonja_Bauernfeind 2026-02-02 / 08:00 AM

  15 3 6000
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  Product Innovation

  Shift Left: What DevOps can teach us about catching bad data

  There’s a moment every data team knows. An executive questions a number in a dashboard. An AI model returns a recommendation that doesn’t make sense. ... Show More

  There’s a moment every data team knows. An executive questions a number in a dashboard. An AI model returns a recommendation that doesn’t make sense. A downstream report shows a discrepancy no one can explain. The root cause turns out to be a data quality issue that has been sitting in the pipeline for weeks.

  The frustrating part is that the issue rarely originated where it was found. It perhaps started upstream, from an incorrect transformation, a schema change that wasn’t handled, or a source system that began emitting invalid values two sprints ago. Every unidentified data issue compounds downstream, driving rework, slowing decisions, and eroding trust in analytics and AI. Gartner research estimates that the cost of poor data quality averages $12.9 million per organization annually.

  By the time an AI app or a business user surfaces the problem, it has aged, compounded, and become harder to trace. There is a better containment model, and software engineering figured it out years ago.

  Borrowing from DevOps: shift quality and governance left

  In software development, “shifting left” means moving quality and governance checks earlier in the lifecycle. Barry Boehm’s research quantified why it matters for code development. A defect caught during development costs the least to fix, a defect found during testing costs considerably more, and a defect that reaches production costs the most by far. The multiplier compounds at every stage you move downstream, because context degrades, blast radius widens, and the fix becomes harder to isolate. Software engineering recognized the problem years ago, internalized it and reorganized the whole discipline around it. This gave rise to  DevOps Research and Assessment (DORA) framework for continuously improving software delivery.

  Furthermore, the DORA concept can be applied to other domains beyond just software engineering. Indeed, we can apply it to data engineering as well, specifically data quality. Thinking through the lens of DORA metrics, KPIs such as deployment frequency, lead time for changes, change failure rate, and mean time to recovery can be mapped to equivalents within data quality engineering.

  DORA Metric	Software Engineering	Data Quality Equivalent
  Deployment Frequency	How often code is deployed	How often quality rules and controls are deployed into live pipelines
  Lead Time for Changes	Time from commit to production	Time from identifying a DQ requirement to enforcing the rule in production
  Change Failure Rate	% of deployments causing incidents	% of pipeline or schema changes that introduce data defects
  Mean Time to Recovery	Time to restore service	Time to detect, remediate, and validate a data quality issue

   

  

   

  Just like code, data follows the same curve. A quality issue caught at ingestion costs a fraction of what it costs when it surfaces in a dashboard, a regulatory report, or an AI model.

  What shifting quality left means for data engineers

  Shifting quality left has a specific meaning for data engineers. Validation logic runs at the point where data is created and transformed, not the point where it is consumed. When a new source is onboarded, completeness and schema checks run immediately. When a transformation is written, it carries assertions about what the output should look like. When a pipeline runs, anomaly detection fires in real time, not three weeks later when someone notices a metric looks off. This repositions the data engineer from pipeline builder to quality-accountable producer.

  The data engineering role itself is changing in the process. Hand-crafting rule expressions and wiring checks into every job used to fill the day. Qlik’s Agentic Data Engineering capabilities, now generally available in Qlik Talend Cloud changes what that work looks like. With the Data Quality Agent, an engineer describes what good data looks like in plain language; the agent finds the target dataset, checks existing fields and rules to avoid redundancy, generates the rule expressions, and lets the engineer refine them before anything is written. The engineer directs and reviews; the agent does the mechanical work. Building pipelines and fixing data quality issues becomes a higher-leverage discipline allowing the quality coverage to widen well beyond the specialists who used to own it.

  Observability is the key to practicality

  Shifting quality left only works if you can see what is happening across your pipelines. This is where data observability comes in, and it is worth being precise because it is often conflated with monitoring.

  Monitoring tells you that something failed. Observability tells you why, when it started, and where it originated. A monitoring check might flag that a table has unexpected nulls. An observability signal tells you that null values in a specific column rose 40% over the last two hours, that the spike correlates with a transformation updated yesterday, and that this pattern has appeared twice before in the past month. That is a signal you can act on.

  Effective observability means instrumenting pipelines continuously for freshness (is data arriving when it should?), completeness (are expected volumes showing up?), schema consistency (are structure changes propagating correctly?), distribution drift (are values behaving as they historically have?), and rule conformance (are business-defined constraints holding?). These run as ongoing signals that are continuously woven into the operational fabric.

  The Qlik Trust Score for AI condenses the trustworthiness of a dataset or data product into a single, intuitive measure. Because Trust Score is tracked over time, an engineer can compare it before and after the Data Quality Agent acts — seeing exactly which signals moved the score and by how much. The evolution of Trust Score becomes the feedback loop: every new rule, every remediation, and every schema change shows up as a measurable shift, so the impact of each intervention is visible rather than inferred. When you combine that visibility with earlier enforcement, you get a compounding effect. Rules embedded in pipelines generate signals upstream. These signals surface issues while they are still close to the source. Resolution happens faster because the context is fresh. Over time, you build a map of where your data is fragile and where it is reliable, and you address quality proactively rather than reactively.

  Ownership shifts as quality and governance become an engineering discipline

  The technical model only works if the organizational model supports it. Traditional data quality programs concentrate ownership in a small governance team operating at a remove from the engineering work. That team defines policy. Engineers implement pipelines. Governance audits outputs. The feedback loop is long, and by the time an issue surfaces through that channel, it has already caused downstream harm.

  The old stereotype was governance as the data police: no, you can’t use that field, please attend next month’s council. Even well-run enablement teams operated from the outside, persuading and reminding everyone else to comply. That does not scale. Quality and governance that survives AI shows up where the work happens, in pull requests, in CI/CD, and in the pipelines themselves.

  A left-shifted model distributes accountability differently. Data engineers own quality at the point of production. Domain teams are responsible for the data they generate. Business stewards contribute the contextual judgment automated systems cannot provide, because they know what “correct” means for their domain and can tell a real problem from expected variance.

  This is a change in how quality work is structured and who participates, not a reorganization. Automated checks handle the mechanical layer. Human judgment handles the interpretation layer. Two more of the new agents in Qlik Talend Cloud make that split workable. Data products are how the gap between data producers and data consumers finally closes — producers publish a governed, contract-bound asset; consumers get something discoverable, documented, and safe to trust. The Data Product Agent manages the full lifecycle of a data product through conversation, with pre-activation validation, dependency awareness, and explicit confirmation before anything is created, deactivated, or deleted. Producers evolve the product without breaking downstream consumers, and governance is enforced at every step without adding process overhead, yet allowing human judgment to be in the loop at each decision point. The Catalog and Business Glossary Agent then makes those products understandable in business terms. It derives glossary terms from business domain context, data product structure, field metadata, and app measures and dimensions, and links each term to the assets it describes. Documentation stays in sync with the data instead of rotting the moment someone renames a column, and other agents inherit correct, governed metadata to reason over.

  Where to start

  The Data Quality, Catalog and Business Glossary, and Data Product Agents, along with Declarative Pipelines, are all generally available today in Qlik Talend Cloud. Clive Bearman walks through each one, with demos, in the full launch announcement: From Intent to Trusted Data: Agentic Data Engineering is Now Generally Available. If you are ready to move quality upstream, start here:

  🔗 Release notes: here
  
  🔗 Want a demo, join the session : here

   

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  By DPinto 2026-07-10 / 12:10 PM

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  Qlik Academic Program

  REVA University students get qualified

  By Pankaj_Muthe 2026-07-10 / 08:27 AM

  0 2 93
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  Support Updates

  Join Us Live! Q&A with Qlik: New to Qlik Cloud

  Don't miss our next Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience.       REG... Show More

  Don't miss our next Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience.

   

   

  

   

  REGISTER HERE

   

  Not able to make it live? Don't worry! All registrants will get a copy of the recording the following week.

  See you there!

  Qlik Global Support

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  By Troy_Raney 2026-07-09 / 03:20 AM

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  Product Innovation

  Model vs. Mastery, Episode 2: When the Beautiful Game Meets AI

  Meet the Panel Joining host Adam for this tournament check-in are two guests who bring very different — and very complementary — perspectives to the t... Show More

  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 

  JOIN THE CONVERSAION with the panel, and others, on our community forum 

  Together, they make for a fascinating collision of human intuition and machine intelligence. 

   

  So… How's the Model Holding Up? 

  Spoiler alert: football is doing what football does best — keeping everyone on their toes. 

  The early group stage threw up some genuine surprises, including a record-breaking number of red cards in a single game that no model could have seen coming. As Steve puts it, those early outlier results tested the predictions, but the group stage format — with three games per team — gives the model room to self-correct and normalize over time. 

  Nick adds important context around this year's expanded 48-team format. With two-thirds of teams now qualifying for the knockout stage (up from half in previous tournaments), team strategies are shifting in ways that add yet another layer of complexity for any prediction model to navigate. 

   

  The Real Challenge: The Knockout Stage 

  Both Steve and Nick agree — the real test for AI prediction is still to come. In the group stage, there's room for recovery. In the knockout rounds, it's winner takes all. One upset, one red card, one moment of magic from an unexpected player, and the model is back to square one. 

  That's what makes this experiment so compelling. It's not just about whether the AI gets it right — it's about understanding where and why it gets it wrong, and what that tells us about the limits of data-driven forecasting in a sport defined by human unpredictability. 

   

  The Wisdom of the Crowd 

  One of the highlights of the episode is a peek behind the curtain at Qlik's Choose Your Champion app, where over 330 people have submitted their own World Cup brackets. The crowd's favorites? Spain, France, and Brazil — a fairly conventional set of picks that will be fascinating to measure against the model as the tournament progresses. 

  There's also some fun analysis around national bias — how likely are fans to pick their own country to win? (England fans, it turns out, are more realistic than you might expect. The French, less so.) 

   

  Why This Matters Beyond Football 

  At its heart, Model vs. Mastery is about something bigger than the World Cup. It's about the relationship between AI and human expertise — when to trust the data, when to trust your gut, and how the two can work together to make smarter decisions. 

  Whether you're a football fan, a data professional, or just someone curious about how AI performs under pressure, this episode has something for you. 

   

  Don't Miss Out — Update Your Bracket! 

  If you haven't joined the Choose Your Champion competition yet, it's not too late. Head to the app, plot your path to the final, and see how your predictions stack up against both the AI model and the wisdom of the crowd. A team jersey from Adidas is up for grabs for our top prediction champion — not a bad prize for getting your football picks right. 

  And if you've already submitted your bracket, now is the time to revisit your knockout stage picks and make sure you're backing teams that have actually made it through. 

   

  Episode 2 of Model vs. Mastery is available now. Catch up on Episode 1 if you haven't already, and stay tuned for our final episode where we'll find out once and for all — did the model win, or did human mastery prevail? 

  The beautiful game. The power of data. Let's see who comes out on top. 

  Watch Episode 2 “Inside the Tournament” and create or update your bracket today!   

  Show Less

  By Katie_Abbott 2026-06-29 / 11:32 AM

  1 2 240
• 
  

  Support Updates

  Introducing Fast Mode: A New Gear for Qlik Answers

  Qlik Answers was built to do real analytical work: investigate root causes, reason across dimensions and time, and explain not just what happened but ... Show More

  Qlik Answers was built to do real analytical work: investigate root causes, reason across dimensions and time, and explain not just what happened but why. That depth is what our customers value most, and it isn't going anywhere.

  But not every question needs an analyst. Sometimes you only need a number. What was the revenue last month? Top 10 products by margin? Open orders in EMEA right now? These are lookups, not investigations, and they deserve their own gear.

  On the 7th of June 2026, Qlik added one: Fast Mode is now live in Qlik Answers, providing one assistant with the effort matched to the question. To read more about the release, see What's new in Qlik Cloud.

   

  

   

  Fast Mode gives you near-instant answers to simple questions on your structured data, so quick checks feel as natural as glancing at a KPI. Thinking Mode remains your deep analyst for everything else, with all the reasoning power you rely on today.

  Together, they work the way a great analyst does. Ask what sales were last month, and you get the number immediately. Ask why sales dropped in the North region, and the assistant rolls up its sleeves and does the work properly. Same assistant, same governed data, two gears.

   

  Fast, and still your numbers

  Plenty of AI tools can answer quickly. The question is whether you can trust what comes back.

  Fast Mode is grounded in your app's governed semantics from the first query: master measures, master dimensions, your calendar logic, and your section access. When Fast Mode says "sales," it means sales exactly as your organization defines them, rather than AI math or a plausible-looking guess. You will get the same governed definitions your dashboards run on, at conversational speed.

  And when a question deserves more than a fast answer, Fast Mode won't pretend otherwise. It tells you the question calls for deeper analysis and offers a one-click "Think harder" that hands the same question, full context intact, to Thinking Mode.

  No need for rephrasing or starting over.

   

  What each mode covers today

  Fast Mode is currently available for structured data: questions answered from your apps, master measures, and master dimensions.

  The rest of what makes Qlik Answers powerful stays with Thinking Mode. Questions over unstructured content like documents and knowledge bases, answers that blend structured and unstructured sources, and requests that trigger automations all need real reasoning, and Thinking Mode is built for exactly that.

  That's a deliberate split: fast lookups where speed matters, full reasoning where the work demands it.

  We know where the appetite goes from here, and we're watching how you use both modes closely. For now, if your question touches documents or kicks off an automation, Thinking Mode has you covered.

   

  When to use which mode

  A simple rule: if a colleague could answer from memory using your apps, use Fast Mode. If they'd need to open the app, read the documents, or take action, that's a job for Thinking Mode.

  Fast Mode: "What was revenue last quarter?" "Top 5 regions by margin." "Open orders in EMEA right now?"

  Thinking Mode: "Why did margin compress in Q2?" "What does our returns policy say, and how many orders does it affect?" "Flag these accounts and notify the owners."

  Pick wrong? One click fixes it.

   

  Try it today! Fast Mode is available now in Qlik Answers. Ask the question on your mind and see how fast a trusted answer can be. Then ask a hard one, hit "Think harder," and watch the assistant do what it does best.

   

  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-07-08 / 08:02 AM

  0 1 468
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  Support Updates

  Action required: Qlik Automate ServiceNow connector migration to OAuth 2.0, June...

  Qlik Automate's ServiceNow connector is moving from username and password authentication to OAuth 2.0. This change aligns with security best practices... Show More

  Qlik Automate's ServiceNow connector is moving from username and password authentication to OAuth 2.0. This change aligns with security best practices and ServiceNow's own recommendations for third-party integrations, eliminating the need to store credentials in plain text and providing more granular, revocable access control.

   

  When will this change be made?

  The change will go into effect on the 23rd of June, 2026.

   

  What action do I need to take?

  Existing connections that use username and password authentication will need to be re-authenticated using OAuth 2.0. Complete the re-authentication steps as outlined in this blog post on the 23rd of June to prevent possible service interruptions. 

   

  How do I switch my ServiceNow connection to OAuth 2.0?

  ServiceNow provides guidance on each stage of the OAuth 2.0 setup. See the following for details:

  • OAuth 2.0 Overview
  • Set up OAuth 
  • OAuth Inbound 
  • OAuth Inbound and Outbound authentication 
  • Enable OAuth with inbound REST

   

  To migrate your Qlik Automate connector, first prepare ServiceNow by configuring OAuth 2.0, then re-authenticate Qlik Automate on the 23rd of June.

  Before you begin, make sure you have Admin access to your ServiceNow instance and Admin or Owner access to your Qlik Cloud tenant and the relevant Qlik Automate automation.

  1. Open the Application Manager in ServiceNow:

     The OAuth 2.0 plugin must be available in your ServiceNow instance.

     1. Log in to your ServiceNow instance.
     2. Click the All button in the top-left navigation bar and search for Application Manager.
     3. Select Application Manager from the results under Admin Center.
  2. In the Application Manager, use the Search your licensed applications and plugins field to search for OAuth. The ServiceNow products OAuth tile will appear in the results.
  3. Click the OAuth tile to expand it, then install the OAuth 2.0 plugin (Plugin ID: com.snc.platform.security.oauth). Wait for the installation to complete before proceeding.
  4. Navigate to Inbound Integrations:
     1. Click the All button again and search for Inbound Integrations.
     2. Select it from under System OAuth in the results.
  5. Create a new OAuth application endpoint:
     1. Click the New Integration button on the right.
     2. On the connection type screen, select OAuth – Authorization code grant.
     3. Configure the application by providing a Name, the Redirect URL, and the required scopes.

        When prompted for a Redirect URL, use the callback URL provided by Qlik Automate during the connection setup. This URL must exactly match what is registered in ServiceNow.

     4. Save the Client ID and Client Secret; they are required for the next step. 
     5. Click Save.
  6. Re-authenticate the connector in Qlik Automate

     This step can only be carried out after the change on the 23rd of June, 2026.

     1. In Qlik Automate, add a new ServiceNow connection.
     2. Enter your ServiceNow instance domain (format: https://[domain].service-now.com/).
     3. Provide the Client ID and Client Secret previously recorded.
     4. Follow the on-screen authentication flow to complete the OAuth authorisation.

   

  If you encounter issues during the migration, please contact Qlik Support or visit the Qlik Community for assistance. When raising a support case, include your ServiceNow instance domain and the error message you are seeing.

   

  Thank you for choosing Qlik,
  Qlik Support

   

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  By Sonja_Bauernfeind 2026-05-25 / 08:00 AM

  1 0 1206
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  Support Updates

  Qlik Data Gateway - Direct Access version 1.6.x reaching End of Life on Septembe...

  Qlik Data Gateway - Direct Access version 1.6.x will be officially End of Life (EOL) on September 30, 2026. Version 1.6 was initially released in Dece... Show More

  Qlik Data Gateway - Direct Access version 1.6.x will be officially End of Life (EOL) on September 30, 2026.

  Version 1.6 was initially released in December 2023 and reached End of Support on June 14, 2025. It is now confirmed for End of Life on September 30, 2026. Upgrade to a supported version as soon as possible.

   

  How will this affect me?

  Once version 1.6.x reaches End of Life, Direct Access Gateway instances still running 1.6.x after September 30 may lose connection to Qlik Cloud, disrupting data loads and any workloads that depend on it.

   

  What should I do?

  Upgrade to the latest available version of Qlik Data Gateway – Direct Access (currently 1.7.x, with 1.7.16 expected soon) before the End of Life date for 1.6.x. The new versions bring:
  • A redesigned gateway configuration UI, making setup and troubleshooting easier
  • Ongoing security fixes and cloud-side compatibility, which are not backported to older versions
  • Ongoing performance and reliability improvements

  From version 1.6.6 onward, .NET 8.x is required. This is installed automatically as part of the upgrade process, so no manual action is required.

  The upgrade path from 1.6.x to the latest 1.7.x release has been tested and is expected to run smoothly. As always, please review the upgrade steps carefully and take a backup before upgrading.

   

  How do I upgrade?

  Upgrading Qlik Data Gateway - Direct Access walks through the upgrade procedure and lists the significant changes introduced in each release.

  Configuring and troubleshooting Qlik Data Gateway - Direct Access covers configuration options introduced in later versions that may apply to your deployment. Most gateway settings can also be managed directly in the Qlik Cloud Administration activity center (from v1.7.2).

   

  What else should I keep in mind?

  We recommend always running the latest available version of the Direct Access Gateway. Cloud-side fixes are deployed on a near-weekly basis, and these most often do not apply to older gateway versions — so staying current is the best way to avoid running into avoidable issues.

   

  Questions?

  If you have questions or need assistance planning your upgrade, our forums are open to you, and Support is only a chat away.

  
  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-07-07 / 08:00 AM

  0 1 441
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  Support Updates

  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 More

  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. 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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  By Sonja_Bauernfeind 2026-05-27 / 08:10 AM

  14 0 6166
• 
  

  Design

  Qlik Cloud Features & Improvements

  In this blog post, I will cover three new features in Qlik Cloud: Subtotals in the straight table Exit error Creating text and image objects from pas... Show More

  In this blog post, I will cover three new features in Qlik Cloud:

  1. Subtotals in the straight table
  2. Exit error
  3. Creating text and image objects from pasted images

  These are small features, but useful improvements that can be used when building your analytics apps.

  Subtotals in the Straight Table

  The first is the ability to add a subtotal row to a straight table. This is something we are used to seeing in a pivot table but not a straight table. Developers can now toggle on Show subtotals in a dimension field of their straight table to add subtotal rows.

  

  Once Show subtotals is toggled on, the Label property becomes available allowing the developer to rename the label. In the table, a subtotal row is added for each asset class.

  

   

  Exit Error

  The second new feature is Exit Error, which is used in the script to end the execution of the script with a user defined error message. The syntax as found in Qlik Help looks like this:

  Exit Error expression [(when | unless condition)]

  For example, assume you are loading data in a table, and you want to exit the script execution if no data has been loaded. Using Exit Error, a variable can be checked (or some other condition) to determine if there are rows in a table that was previously loaded. If there are, continue with the script; if not, exit the script with a message. In this example, the vOpenOrders variable will be 0 if there are no rows in the table.

  

   

  If the variable vOpenOrders is equal to 0, then exit the script and show the message “No open orders were loaded.” This is how the error will appear.

  

   

   

   

  Creating Text and Image Objects from Pasted Images

  The last new feature is a cool shortcut to create a text object or an image object. Simply paste text or an image onto an empty area on a sheet and a new text object or image object will be created. In the case of images, the pasted image will be automatically saved to the media library – a time saver. On the sheet below, I pasted this text: “I am going to create an image with this text.” and this image:

  

   

   

   

   

   

   

  And a text object and image object were created without any additional work from me.

  

  Note that the developer will need upload permissions for the image to be added to the media library.

  That's three new ways Qlik Cloud is making app development easier — smarter subtotals, cleaner error handling, and a slick shortcut for pasted content. Small features, big time savings. Try them out in your next app and stay tuned for more updates coming soon.

  Thanks,

  Jennell

   

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  By Jennell_Yorkman 2026-07-03 / 09:27 AM

  1 9 500
• 
  

  Support Updates

  Qlik Strava connector: upcoming removal of Club Activities and Club Members tabl...

  Effective September 1, 2026, Strava will deprecate three Club endpoints: Club Activities, Club Administrators, and Club Members. Strava has stated tha... Show More

  Effective September 1, 2026, Strava will deprecate three Club endpoints: Club Activities, Club Administrators, and Club Members. Strava has stated that the small number of developers using these endpoints does not support their continued maintenance.

  As a result, the ClubActivities and ClubMembers tables in the Qlik Strava connector will no longer return data after this date and will be deprecated.

  The following Qlik products and their related Strave connectors are affected:

  • Qlik Cloud
  • QlikView
  • Qlik Sense Enterprise on Windows

   

  Why is this happening?

  This change originates with Strava, not Qlik. Strava is deprecating these endpoints at the API level, so any application relying on them, including the Qlik Strava connector, is affected.

   

  What action do I need to take?

  Review your Qlik apps, scripts, and automations for any use of the ClubActivities or ClubMembers tables in the Strava connector. If you rely on this data, plan to remove or replace these data sources ahead of September 1, 2026, as they will stop returning data once Strava's deprecation takes effect.

  No action is needed if you don't use these tables.

   

  What happens if I don't act?

  Any loads or automations using the ClubActivities or ClubMembers tables will stop returning data after September 1, 2026.

   

   

  If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat.

  
  Thank you for choosing Qlik,
  Qlik Support

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  By Sonja_Bauernfeind 2026-07-03 / 08:00 AM

  0 0 252
• 
  

  Japan

  【新着レポート】Qlik、16 年連続で BI 市場のリーダーの 1 社に！

  AI がビジネス上の意思決定により深く組み込まれていくと、単に何が起きたかを報告するだけではない高度な分析機能が必要になってきます。信頼できるデータ、詳細なビジネス状況に基づいた分析、人間と AI エージェントとの複雑な対話からも推論を導き出す高度な体験が求められているのです。 Gartner®... Show More

  

  AI がビジネス上の意思決定により深く組み込まれていくと、単に何が起きたかを報告するだけではない高度な分析機能が必要になってきます。信頼できるデータ、詳細なビジネス状況に基づいた分析、人間と AI エージェントとの複雑な対話からも推論を導き出す高度な体験が求められているのです。

  • Gartner® 社によるアナリティクス / BI プラットフォーム市場に関するインサイト
  • Qlik がリーダーの 1 社に評価された理由
  • Gartner® 社が「ビジョンの完全性」と「実行能力」に基づいてベンダーを評価する方法
  • 自社の AI 戦略に適したアナリティクス / BI プラットフォームを選定する際に考慮すべき点

  「Gartner® アナリティクス / BI プラットフォームの Magic Quadrant」で、Qlik が 16 年連続でリーダーの 1 社に評価された理由とは？無料のレポートで、その理由と BI 市場の全容をご確認ください。
  
  無料のレポートを見る
  

  

   

  無料のレポートを見る

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  By Qlik_JP_MKT2 2026-07-03 / 12:07 AM

  0 0 41
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  Qlik Academic Program

  Why Hands-On Learning Matters More Than Ever in the Age of AI

  This shift presents an exciting opportunity for higher education. Universities are uniquely positioned to equip students with the skills they'll need ... Show More

  This shift presents an exciting opportunity for higher education. Universities are uniquely positioned to equip students with the skills they'll need in an AI-driven world, but doing so requires more than traditional lectures and textbooks. Students learn best when they can apply their knowledge in practical, real-world scenarios.

  Hands-on analytics experiences allow students to explore real datasets, identify trends, answer business questions, and communicate their findings with confidence. Instead of simply learning about data analytics, they experience what it's like to solve problems using the same types of tools and approaches they'll encounter in the workplace.

  The benefits extend well beyond technical skills. By working on practical projects, students strengthen their critical thinking, collaboration, communication, and problem-solving abilities—skills that remain essential regardless of how technology continues to evolve. These experiences also help students build confidence, making the transition from university to the workplace much smoother.

  Across EMEA, we're seeing more educators embrace this approach by incorporating analytics into their teaching in innovative ways. Whether through classroom projects, workshops, guest lectures, or interactive learning activities, educators are helping students connect academic knowledge with real business challenges. It's inspiring to see students become more engaged as they discover how data and AI can be used to answer meaningful questions and support informed decision-making.

  As AI continues to reshape industries, one thing is becoming increasingly clear: data literacy is no longer a niche skill. It's a core capability that will benefit students across disciplines, from business and finance to healthcare, engineering, and the social sciences. By giving students opportunities to learn through experience, universities are helping prepare graduates who are ready to thrive in a data-driven future.

  The Qlik Academic Program is proud to support both educators and university students by providing free access to Qlik's analytics and AI technologies, curriculum resources, self-paced learning, and industry-recognized qualifications. Whether you're an educator looking to enrich your courses with hands-on analytics or a student eager to build in-demand data and AI skills, the program offers valuable resources to help you succeed. Learn more about the Qlik Academic Program and sign up here: https://qlik.com/academicprogram 

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  By ElizCayirli 2026-07-02 / 10:30 AM

  0 3 124
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  Support Updates

  Watch! Q&A with Qlik: Qlik Cloud Administration

  Don't miss our previous Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience. Let ou... Show More

  Don't miss our previous Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience.

  Let our Qlik experts offer solutions and best practices for Qlik Cloud administration and answer your analytics questions.

   

  WATCH HERE

   

   

  

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  By Troy_Raney 2026-07-02 / 04:33 AM

  0 0 381
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  Product Innovation

  Write Table now available in Qlik Cloud Analytics

  Turn insights into action... right inside your analytics!  As we wrap up the year and before everyone starts tying bows on projects, dashboards and wi... Show More

  Turn insights into action... right inside your analytics! 

  As we wrap up the year and before everyone starts tying bows on projects, dashboards and wish lists, we wanted to add one more gift to the pile. Something that helps you move from insights to action faster, collaborate more easily, and keep your workflows flowing straight into the new year. 

  Unwrap Write Table, now available in Qlik Cloud Analytics. 

  Write Table is included with Premium, Enterprise, and Enterprise SaaS subscriptions. No add-ons or extra licensing required. You can find it in the Chart Library now. 

   

  A More Interactive Analytics Experience 

  Write Table introduces a new editable table chart that lets you update data, add context, validate decisions, and collaborate directly within your analytics apps. No reloads. No switching systems. 

  Just click, update, and watch your changes sync instantly across active sessions. 

  And because it’s built natively into Qlik Cloud Analytics, it seamlessly integrates into your existing workflows, transforming your apps into actionable workspaces.

   

  "Probably the most awaited feature for customers who want to turn insights into action. Write table closes the gap between analytics and transaction applications, offering a compelling solution for those who always want to take it a step further. " 
  
  — Henri Rufin, Head of Data & Analytics, Radial 

   

  From Decisions to Automation 

  Now you can capture decisions and act on them.  

  With real-time syncing and change tracking, every update you make in a Write Table is stored in a Qlik-managed database called a “change store” that can be exported and connected into Qlik Automate workflows. That means approvals, changes, or comments made in an app can instantly trigger downstream processes.  

  From updating operational records... to routing approvals... to notifying teams... to pushing changes into your operational systems.... 

  Write Table helps you move smoothly from insight -> decision -> action in one place.  

   

  “The new Write Table is a game-changing addition to Qlik's data and analytics solution. It allows our users to interact with the data, add context to their insights and collaborate effortlessly within the same trusted end-to-end platform. We're going to use it for everything, from updating data easily to seamlessly integrating with third-party tools using Qlik Automate workflows.” 

  — Sebastian Björkqvist, Solution Lead, Fellowmind 

   

  Key Highlights 

  • Editable table chart for updating values, adding comments, and capturing decisions in the Chart Library 
  • Real-time sync across active sessions, without reloads 
  • Qlik-managed change store for secure update handling 
  • Integration with Qlik Automate to trigger downstream workflows and orchestrate actions 
  • Native to Qlik Cloud Analytics, included for Premium, Enterprise and Enterprise SaaS subscriptions 

   

  Learn more 

  • Write Table Product Tour 
  • What’s New in Qlik Cloud | Qlik Help 
  • Write Table | Qlik Help
  • Write Table - SaaS in 60 | Video  
  • Write Table FAQ | Support Article

  Show Less

  By QlikProductUpdates 2025-12-09 / 09:41 AM

  40 21 21976
• 
  

  Product Innovation

  Agentic Data Engineering: Building the Nervous System of the Intelligent Enterpr...

  For two decades, the job of a data engineer was deceptively simple to describe: move data from system A to system B so that someone downstream could a... Show More

  For two decades, the job of a data engineer was deceptively simple to describe: move data from system A to system B so that someone downstream could analyze it. That era is ending. As agentic AI moves out of the lab and into core operations, a new and far more demanding consumer has arrived at the table — the autonomous agent. And agents are unforgiving about the data they are fed.

  Welcome to agentic data engineering: the practice of both using and building agentic AI capabilities to deliver trusted data for AI workloads. It marks a shift from building pipelines by hand to orchestrating self-sustaining systems — and it changes what the modern data team is measured on.

  The Great Reset in Data Engineering

  In 2026, agentic AI is crossing the line from experimental pilots to operational infrastructure, and that crossing is redefining the data engineering role itself. Several forces are converging at once.

  • Coding agents have swung the pendulum back to code. Tools like Anthropic's Claude Code make pipeline creation, data-quality cleansing, and workload automation dramatically faster — enough that the productivity gain outweighs the cost of managing a codebase again.
  • The complexity wall has stalled manual workflows. Fragmented stacks and multi-SaaS sprawl have made hand-mapping every process prohibitively expensive. Agents are being adopted precisely because they can navigate that complexity where rules-based systems stall.
  • Standards like MCP have made context portable. The Model Context Protocol gives teams a standardized way to feed enterprise data and context into agents and homegrown agentic systems — turning integration from bespoke plumbing into a repeatable pattern.

  The net effect is a market shifting from pipeline building to system orchestration. Data engineers are no longer just moving data from A to B; they are building the semantic layers and feedback loops that agents depend on. The value of the role is no longer measured by the ability to deliver data, but by the architecture, context, and guardrails that let swarms of agents operate reliably and securely. In short, the data engineer is becoming the conductor of an agent orchestra.

  This is not a threat to the profession — it is an expansion of it. The mundane, repetitive work that consumed so many engineering hours is exactly what agents handle well, freeing skilled engineers to focus on the design decisions only humans can make. Qlik has spent years at the forefront of data engineering productivity, recognized as a longstanding leader across the Gartner Magic Quadrants for Data Integration Tools and Augmented Data Quality Solutions. We see this shift not as a disruption to defend against, but as the natural next chapter.

  What changes for the data engineer From hands-on-keyboard pipeline work to designing context, architecture, and guardrails. Less gritty manual toil, more time on the work that actually drives business value.

   

  

  Figure 1. The agentic data engineering stack — from raw enterprise sources through build, trust, and serving layers that feed both human and AI consumers.

  What “Trusted Data” Means When the Consumer Is an Agent

  When a human reads a dashboard, they bring judgment — they can sense when a number looks wrong. An agent has no such instinct. It acts on what it is given, at machine speed. That raises the bar for what trusted data has to mean. Success in the agentic era looks like:

  • Real-time freshness. Event streams replace batch jobs so agents reason on current state, not yesterday's snapshot.
  • Agent-ready semantic context. Living metadata — provenance, business logic, quality scores — delivered directly to the consuming agent. Agents don't need tables; they need context.
  • Verifiable provenance. Fully governed lineage that lets the business audit exactly how a piece of data shaped an agentic decision.

  Get this wrong and the failure modes are equally specific: latency-induced inaccuracy, where agents decide on stale data; the context gap, where clean-looking data lacks the semantics agents need and produces “informed hallucinations”; and agent sprawl, where unoptimized, agent-triggered queries quietly run up the cloud bill. Trusted data, governed context, and cost guardrails are no longer nice-to-haves — they are the cost of entry.

  How Qlik Talend Cloud Delivers It

  Qlik's approach rests on three ideas working together: intent-driven engineering, autonomous operations, and AI-ready data. Each maps to a real capability shipping as part of our agentic data engineering release.

  Intent-driven engineering

  Rather than dragging components on a canvas or dropping out to scripts to join dozens of tables, engineers describe the outcome they want and let agents do the work. With declarative data pipelines, you can build and update Qlik pipelines in natural language from the IDE and coding agent of your choice — Claude Code, GitHub Copilot, and more. Five new data agents — Data Product, Data Quality, Catalog, and Glossary, with a Pipeline Agent in preview for H2 2026 — extend Qlik's agentic experience from analytics teams to the data engineering team itself.

  Autonomous operations

  Building the pipeline is only half the battle; keeping it alive is the other. Consider a familiar scenario at a large financial services firm. A finance director flags that the P&L report shows unexpected numbers. In the old world, that triggered hours — sometimes days — of manual investigation: tracing queries, combing logs, hunting down dataset owners.

  With integrated, continuously updated metadata, that picture changes. The on-call engineer uses natural language to see end-to-end lineage immediately, tracing the issue from the report back to its source. Observability alerts enriched with historical context flag the anomaly, and governance teams instantly see which other reports are affected. What once took days can be resolved in under an hour.

  

  Figure 2. Lineage-driven incident response — active metadata provides the lineage to find the problem, the context to understand it, and the clarity to fix it fast.

  AI-ready data

  In the age of AI, “garbage in, garbage out” is no longer a warning — it is a business risk. Data must be systematically prepared, scored, and governed to meet the needs of every AI project. Qlik treats data quality and governance as an integral part of the platform, not an afterthought: profiling, quality rules, lineage, and the Qlik Trust Score are built in. Agentic data stewardship (preview) adds a human-in-the-loop layer in which agents proactively identify, profile, and recommend remediations, while human stewards approve or refine the action — the first step toward an agentic data quality, governance, and observability ecosystem.

  Where to Start

  The fastest way to see this in practice is to pick one painful, well-understood pipeline and rebuild it the agentic way. A focused pilot makes the value concrete:

  1. Choose a high-friction pipeline. One your team already knows is brittle, slow to debug, or expensive to maintain.
  2. Rebuild it with declarative pipelines and CDC. Describe the outcome in natural language; ingest in real time rather than batch.
  3. Turn on active metadata and a Trust Score. Measure mean-time-to-recovery on a deliberately injected issue, before and after.
  4. Expose the result through the Qlik MCP server. Let an agent consume the curated data product and verify it acts on trusted, governed inputs.

  The future of data isn't about having more of it; it's about how intelligently it moves and presents itself for agentic consumption. Agentic data engineering is how you scale human expertise to match the speed of modern business. With Qlik, you don't just build data pipelines — you architect the nervous system of the intelligent enterprise.

   

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  By QTC_Manuel 2026-07-01 / 01:20 PM

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