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Recent Blog Posts
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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 MoreQlik 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.
- Open the Application Manager in ServiceNow:
The OAuth 2.0 plugin must be available in your ServiceNow instance.
- Log in to your ServiceNow instance.
- Click the All button in the top-left navigation bar and search for Application Manager.
- Select Application Manager from the results under Admin Center.
- 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.
- 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.
- Navigate to Inbound Integrations:
- Click the All button again and search for Inbound Integrations.
- Select it from under System OAuth in the results.
- Create a new OAuth application endpoint:
- Click the New Integration button on the right.
- On the connection type screen, select OAuth – Authorization code grant.
- 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.
- Save the Client ID and Client Secret; they are required for the next step.
- Click Save.
- Re-authenticate the connector in Qlik Automate
This step can only be carried out after the change on the 23rd of June, 2026.
- In Qlik Automate, add a new ServiceNow connection.
- Enter your ServiceNow instance domain (format: https://[domain].service-now.com/).
- Provide the Client ID and Client Secret previously recorded.
- 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 -
Qlik Sense May 2026 (Client-Managed) now available!
Key Highlights More Powerful & Efficient Data Scripting This release introduces new scripting capabilities that simplify development while improvin... Show MoreKey Highlights
- More Powerful & Efficient Data Scripting
This release introduces new scripting capabilities that simplify development while improving performance and governance.
- New functions like Quarter and a suite of geo-validation functions (MakeGeoPoint, MakeGeoLine, MakeGeoPolygon, MakeGeoRegion) expand what you can do directly in scripts and expressions
- Script-only variables and the ability to explicitly drop variables and mapping tables improve memory efficiency and keep apps clean
- Variable constraints allow you to enforce rules on inputs, protecting apps from malformed or unsafe values
Why this matters:
- Faster, cleaner script development
- Better performance during reloads (especially for large apps)
- Improved governance and protection against errors or misuse
- Greater Flexibility with On-Demand Apps (ODAG)
Developers can now allow users to reload ODAG-generated apps, with optional inclusion of the load script
Why this matters:
- Enables users to refresh data while maintaining their selections
- Gives developers more control over balancing flexibility and security
- Improves usability for large, dynamically generated datasets
- A More Streamlined and Customizable User Experience
Several enhancements make it easier for both creators and consumers to interact with apps:
- Pin key fields to the selection bar for quick access to commonly used filters like Year or Quarter
- Resizable properties panel makes working with long field names significantly easier
- Top bar styling options allow customization of colors to align with your organization’s branding
Why this matters:
- Reduces friction for end users navigating dashboards
- Improves productivity for developers and analysts
- Enables more polished, branded analytics experiences
- Reorganized Charts & Faster Access to Visualizations
The assets panel has been reorganized into clearer categories:
- Visualizations
- Dashboard objects
- Legacy
Additionally, visualization and dashboard bundles are now integrated directly into the charts panel for easier access
Why this matters:
- Simplifies how users find and use visualizations
- Reduces confusion between legacy and modern objects
- Speeds up dashboard development
- Major Enhancements to Tables & Data Exploration
Tables continue to evolve into more interactive, user-driven experiences:
Straight Table Enhancements:
- Separate styling for headers and content cells
- One-click sorting and in-column search
- Drag-and-drop column reordering in view mode
- Fewer steps to add expressions
- Improved data panel for managing columns
Pivot Table Exploration:
- End users can now dynamically move dimensions and measures between rows and columns
- Support for alternative dimensions/measures for self-service exploration
Why this matters:
- Empowers business users to explore data without editing apps
- Reduces dependency on developers for ad hoc analysis
- Improves speed and flexibility of insight generation
- Richer Visualizations & Dashboard Storytelling
This release continues to expand visual capabilities:
- Custom bar widths for continuous axis charts
- Inline images in Text objects for richer storytelling
- Images in line charts for better context and readability
- Shapes in scatter plots (including areas) for advanced visual use cases like quadrant analysis
Why this matters:
- Enables more engaging, intuitive dashboards
- Helps users interpret data faster with visual cues
- Supports more advanced and custom analytical storytelling
- Improved Embedding Capabilities
The updated Share > Embed menu now integrates qlik-embed, making it easier to embed Qlik visualizations into external applications
Why this matters:
- Simplifies integration with portals, apps, and web experiences
- Accelerates embedded analytics use cases
- Provides easier access to metadata like app and object IDs
In Closing
The May 2026 release of Qlik Sense Enterprise on Windows delivers a strong combination of power, flexibility, and usability. From more efficient scripting and improved governance to richer visualizations and enhanced self-service capabilities, this release helps your teams do more with data — faster and with greater confidence.
We encourage you to explore these new features and consider how they can support your ongoing analytics modernization efforts.
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Table Recipe
A table recipe is an easy, no-code way to prepare a data set for an app, data flow, script or ML experiment. It is a great way for new users to prepar... Show MoreA table recipe is an easy, no-code way to prepare a data set for an app, data flow, script or ML experiment. It is a great way for new users to prepare their data and get started building an app without having to write the script to transform the data. It can also be used by experienced users for quick data prep. Table recipe is available in Qlik Cloud Analytics from the Analytics activity center > Prepare data. You can also access it from the + Create menu option or + Create New button in the catalog. To begin using it, open table recipe, give it a name and select the space you would like to create it in. Once that is done, you will be prompted to select the data set. Note - the table recipe processes one data set at a time.
Once a data file is selected, you can view the fields to confirm it is the correct data set and then you can load it into table recipe. Once it is loaded, you will see a pop up indicating the number of rows and columns in the dataset. Note that all the rows may not be loaded into the table recipe editor, but the recipe will be applied to the entire data set when it is run. Below is a look at the table recipe. On the left is the functions panel that includes several functions that can be used to prepare the data. Functions are grouped as general, columns, strings, dates, numbers and math. On the right is where the steps in our recipe will be added.
Let’s begin with a simple step like deleting a column. I have a Fax column that I do not need so to delete the column; I can click on the column menu (three vertical dots) and select Delete column.
The step will appear on the right in the table recipe, and I can click Apply to remove the Fax column.
Next, let’s split a column into two new columns. I have a Contact Name field with the contact’s full name. I would like to split the name into two fields – one for first name and one for last name. To do this, I will select the Split column function from the list of string functions on the left. Then I can update the details and apply the step. I will change the separator to a space since that is what separates the first and last name in the ContactName column.
Once I apply that step, I can see the two new fields it generated.
I will open the column menu and select Rename column to change the names of the new columns. Once I apply those steps, the columns and the recipe look like the image below. Keep in mind that the table recipe has steps that flow in a specific order. If those steps were to change, you may not get the expected results. For example, if I moved the rename column step above the split column step it would not work because the column I am renaming does not exist yet.
It is also easy to reorder the columns with the table recipe. The Reorder column function is found in the function group named Columns. Simply, select the column you would like to move and click on the Reorder column function. To move the Address column to the left of the City column, I will select the City column from the Anchor column drop down and then click Apply.
We are also able to apply filters to the data if we want to focus on specific rows. In this example, Country has two different values for the United States – USA and US. I would like to change the US values to USA.
After applying the filter, I can see that I have 6 rows with US as the Country. I can use the string Search and replace function to replace US with USA.
This step included my filter, so it only applied it to the rows I have filtered on.
Let’s perform one more step that I use all the time when loading data. We will extract date parts from the OrderDate field. This will allow us to create other date fields such as month and year fields. If I select the OrderDate field and then select the date function Extract date parts, I am presented with all the possible date part fields I can add as a column to my data set. I simply can toggle on any new fields I would like to add.
Once this step is applied, I can see the 4 new date fields I added.
When all the steps have been added to the table recipe, the next steps are to set the target and run the recipe. Setting the target is nothing more than giving the target file a name and file type (qvd, parquet, txt or csv) and selecting the space to create it in.
What is nice about the table recipe is the steps are applied and stored in a new file, separate from the original data set, so you do not have to worry about incorrectly modifying the data set. Once the target is set, the Incomplete recipe message at the top of the page will change to Valid recipe and the Run recipe button will be enabled. Once the recipe is run, you can view your new data set and use it accordingly. The target data set will be stored in the desired space and can be accessed anytime via the catalog.
Notice how all these steps we are creating are very easy and intuitive to build. You do not have to be an expert to perform these steps, and the editor is very user friendly and has a clean flow. What is also nice is that you can see that each step is working as expected when you apply it. Take this product tour to get a feel for the table recipe and then try it for yourself. To learn more, check out Qlik Help.
Thanks,
Jennell
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Level Up Your Qlik Experience: Community Resource Spotlight
Hey guys join @Jennell_Yorkman and I for a quick community resource update as we highlight some of the latest ideas, enhancements, and best practices ... Show MoreHey guys join @Jennell_Yorkman and I for a quick community resource update as we highlight some of the latest ideas, enhancements, and best practices being shared across the Qlik Design Blog and Qlik Community.
From dashboard design and developer capabilities to visualization techniques and platform improvements, this session is a fast-paced roundup of useful content and community-driven insights to help you get more out of Qlik. Whether you’re building apps, designing dashboards, or exploring new capabilities, this is a great way to stay connected with what’s happening across the Qlik ecosystem.
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QlikView server communication interruptions following Microsoft Windows Domain C...
Edited May 4th, 2026: Added mitigation article, published on the 4th of May. Beginning on April 14, 2026, multiple QlikView customers experienced outa... Show MoreEdited May 4th, 2026: Added mitigation article, published on the 4th of May.
Beginning on April 14, 2026, multiple QlikView customers experienced outages and intermittent disruptions within their QlikView environments. These incidents coincided with the deployment of Microsoft’s April 2026 security patches to Domain Controllers, which affected QlikView Server Service (QVS) communications over port 4747.
The Microsoft patches introduced changes targeting Kerberos authentication and RC4 encryption. See Addendum for a list of patches. As a result, QlikView environments where RC4 remained enabled (such as at the domain account or Windows server level) became unstable or non-functional.
The impact on QlikView may include, but is not limited to:
- Failed QlikView Distribution Server distribution tasks with an error code indicating an Authentication Failure.
- "No Server" message on the QlikView AccessPoint with an error message in the Web server log indicating that the Web server cannot connect to the QlikView Server.
- Failed Qlik NPrinting distributions to QlikView using a QVP Connection.
- Problems accessing the QlikView Server settings from the QlikView Management Console, or failures for the QlikView Server to come online after a restart.
What can be done to mitigate this issue?
We've published an article to help address the issue. See QlikView server communication interruptions following Microsoft Windows RC4 cipher suite deprecation.
Information in the article is based on Microsoft's remediation steps and has been adjusted and expanded to include QlikView-specific instructions. For the original, see Detect and remediate RC4 usage in Kerberos | learn.microfot.com.
What action is Qlik taking?
RC4 support was deprecated starting with the May 2024 release of QlikView. The root cause in these cases stems from legacy configurations where RC4 remained enabled in the environment, rather than a defect in QlikView itself. No code changes are planned at this time, though improvements to diagnostic logging are under consideration.
Addendum
Microsoft Patches:
- Windows Server 2022 (DC): KB5082142
- Windows Server 2019 (DC): KB5082123
- Windows Server 2016 (DC): KB508208
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 -
Introducing enhanced subscription options for the Qlik Cloud Status Page (per re...
Hello Qlik Cloud users and admins, We’ve enhanced the Qlik Cloud Status page subscription experience to give you more control over the notifications y... Show MoreHello Qlik Cloud users and admins,
We’ve enhanced the Qlik Cloud Status page subscription experience to give you more control over the notifications you receive.
Previously, subscribing meant you received incident and maintenance notifications for all Qlik Cloud regions. With the updated Statuspage experience, you can now choose specific Qlik Cloud regions you want to follow.
This helps reduce unnecessary notifications in your inbox and puts the focus on the Qlik Cloud deployment regions you have active tenants in.
How do I subscribe or modify my current subscription?
To subscribe:
- Visit status.qlikcloud.com
- Click Log In in the top right of the page and authenticate with your Qlik ID
- Click Subscribe to Updates
- Select your preferred notification method (Email or RSS feed)
- Confirm the method
- Choose which deployment region you wish to be notified for
- Click Save
To update your preferences:
- Visit status.qlikcloud.com
- Click Log In in the top right of the page and authenticate with your Qlik ID
- Click Subscribe to Updates
- Select your preferred notification method (Email)
- Enter the email address associated with your subscription and click Subscribe via Email; this will send a One-Time Passcode (OTP) to your registered email address and may prompt a CAPTCHA
- Complete the CAPTCHA (if applicable) and click Subscribe to Update
- Retrieve the One-Time Passcode (OTP), then enter your email address and your OTP
- Click Verify OTP and Subscribe, and complete the CAPTCHA
- Select or deselect the Qlik Cloud regions you want to follow
- Save your updated preferences
For a full guide, see Qlik Cloud and Qlik Talend Cloud Status.
Thank you for choosing Qlik,
Qlik Support -
Techspert Talks - Monitoring Qlik Cloud Capacity
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 May looked at Monitoring Qlik Cloud Capacity.
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:
- Understanding capacity usage
- Explore the reporting app
- Tips for managing consumption
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Qlik Automate: Execution tokens will become header parameters on February 1st, 2...
Hello Qlik Users, As announced previously (see Qlik Automate execution token changes), execution tokens will become header parameters on February 1st,... Show MoreHello Qlik Users,
As announced previously (see Qlik Automate execution token changes), execution tokens will become header parameters on February 1st, 2026.
When triggering a triggered automation through the trigger URL (see the endpoint below), the execution token must be sent as a header parameter. Currently, it is possible to send the execution token as a query parameter. Starting February 1st, 2026, sending execution tokens as header parameters will be enforced.
api/v1/automations/{id}/actions/executeAny Button objects using the built-in Run Automation feature are not affected by this change.
Don't hesitate to reach out if you have any questions or address our experts directly in the Qlik Automate forum.
Thank you for choosing Qlik,
Qlik Support -
【BookLive / Gakken / 日清食品のユーザー事例講演が決定！】AI Reality Tour Tokyo 2026
6/10 （水）開催 AI Reality Tour Tokyo 2026BookLive / Gakken / 日清食品のユーザー事例講演が決定！ 来たる 6/10（水）、「AI Reality Tour Tokyo 2026」を開催いたします。 本イベントでは、Qlik のエキスパートによる基調... Show More6/10 （水）開催 AI Reality Tour Tokyo 2026
BookLive / Gakken / 日清食品のユーザー事例講演が決定！来たる 6/10（水）、「AI Reality Tour Tokyo 2026」を開催いたします。
本イベントでは、Qlik のエキスパートによる基調講演、Qlik ユーザーの先進的な事例、Qlik 技術部門による最新の製品情報、Qlik のパートナー企業による最新のソリューションや展示ブースなど、AI がもたらす価値と現実とのギャップを解消し、AI を実現・加速・適応する最先端のソリューションをご紹介します。
- Qlik の AI ビジョンと戦略 - 企業全体の AI 活用を成功に導く実践的な手法
- 最新のエージェンティック - 今すぐ活用できる最新のイノベーションが切り拓く新たな可能性
- 先進的な顧客事例 - Qlik を活用してビジネス変革を実現している企業の事例
- 新たなつながり - 新たな知見をもたらす Qlik のエキスパートや同業他社との交流
お申し込みの締め切りは、6月 2日（火）17:00 までです。お早めにお申し込みください。【開催概要】
日時：2026年 6月 10日（水）13:00 - 18:30（受付開始 12:00）
懇親会 18:30 - 19:30
会場：有明セントラルタワーホール＆カンファレンス
東京都江東区有明3-7-18 有明セントラルタワー3F・4F参加費：無料
お問い合わせ：Marketingjp@qlik.com までお問い合わせください。 -
Decomposition Tree with AI Splits
Decomposition Tree with AI Splits AnyChart Users drill into any metric across any dimensions, in any order — and find what drives the numbers. A... Show MoreDecomposition Tree with AI SplitsAnyChart
Users drill into any metric across any dimensions, in any order — and find what drives the numbers. All on a single sheet, all on their own. With AI Splits revealing the key impacts automatically.
Discoveries
Well-known from Power BI, decomposition trees aren't available in Qlik natively. This extension fills that gap — letting users drill down across multiple dimensions in any order, with AI Splits automatically surfacing the highest and lowest impact factors on any measure.
Impact
A hands-on feature walkthrough: AI Splits, flex dimension ordering, multiple measures, conditional coloring, negative value handling, three bar scaling modes, zooming, and paging. Everything configured and ready to inspect in edit mode.
Audience
Qlik developers and BI consultants looking to add root cause analysis and ad hoc exploration to their Sense apps.
Data and advanced analytics
Built with AnyChart's Decomposition Tree extension for Qlik Sense / Qlik Cloud, based on fictional business data.
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New Centers of Excellence in India
This year, in April, we inaugurated two new Centers of Excellence ( CoE) under the Qlik Academic Program, in the Silicon Valley of India, i.e Bangalor... Show MoreThis year, in April, we inaugurated two new Centers of Excellence ( CoE) under the Qlik Academic Program, in the Silicon Valley of India, i.e Bangalore. The new CoEs mark a new beginning for training and skilling students in Qlik technologies along with other activities like datathons.
Reva University is one of the leading Universities in the State of Karnataka and is ranked among the top universities in the region. The School of Computer Science and Engineering took the lead in this initiative and has established the CoE. Strategic Partner of the Qlik Academic Program, ICT Academy established the connection with Reva and ensured that arrangements were made as per the requirements of the CoE.
The second CoE was established in Sai Vidya Institute of Technology ( SVIT) which is a well known institution for engineering students. Many students have earned their degree qualification from here. The Department of Computer Science Engineering have been coordinating to establish the CoE. Many initiatives are planned by SVIT to take this engagement ahead.
The previous CoEs are functioning successfully in VJIT Hyderabad, Anurag University Hyderabad and Kristu Jayanti University Bangalore. Many students have got trained and qualified from the CoEs here. Along with this, they have hosted various events including datathons successfully.
We hope to establish more CoEs this year and create a physical space for students to get trained under the Qlik Academic Program.To learn more about the academic program, please visit: qlik.com/academicprogram
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May the Data Flow: Qlik Replicate's May 2026 Technical Preview is Live
It's May — and just like a certain galaxy far, far away, things are heating up. The Qlik Replicate May 2026 Technical Preview has landed, and it's rea... Show MoreIt's May — and just like a certain galaxy far, far away, things are heating up. The Qlik Replicate May 2026 Technical Preview has landed, and it's ready for you to put through its paces.
For those of you who live and breathe data replication, this is your moment to get ahead of the curve before general availability. The Technical Preview is available to download now
[Select Product Category: Qlik Data Integration, Product: Qlik Replicate, Release Number: Technical Preview]
Now let's get into what's new.
What's in the May 2026 Technical Preview?
This release brings a couple of notable additions worth paying attention to:
- IMS – R&D Monitored and Fabric Mirror – Improvements are now available, actively tracked by our R&D team as we refine the experience.
- SAP Sybase ASE – Preview: making its debut in this Technical Preview, we are pleased to give you an early look at the improvements we've made for SAP Sybase ASE support.
As always with a Technical Preview, the clue is in the name — this is your opportunity to explore, test, and feed back before the full release. Think of it as the dress rehearsal, not opening night.
Before You Upgrade — A Quick But Important Note
Please take a few minutes to review the known issues before proceeding with any upgrade. No one enjoys a surprise mid-pipeline, even in test environments.
The full documentation and release notes for both Qlik Replicate and Qlik Enterprise Manager are available here:
- Qlik Replicate May 2026 Documentation & Release Notes
- Qlik Enterprise Manager May 2026 Documentation & Release Notes
The docs are your friend here — treat them as such.
Get Involved
Technical Previews are only as good as the people who test them. If you hit something unexpected or spot something worth improving, drop your feedback in the comments below or raise it through the Community. Your input directly shapes what ships.
May the data flow — and may your upgrades go smoothly. Happy testing.
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Qlik Talend Administration Center - Security Patches Available
The following two Qlik Talend Administration Center security issues have been identified and subsequently resolved. Patches are already available. U... Show MoreThe following two Qlik Talend Administration Center security issues have been identified and subsequently resolved. Patches are already available.
URL access control vulnerability (CVE-2026-9057)
A broken access control issue has been identified in Qlik Talend Administration Center, which allows a user with View permission to modify the Qlik Talend Studio update URL.
Affected Software
- All versions of Qlik Talend Administration Center before Patch_20251121_QTAC-1471_R2025-11_v1-8.0.1.
See Security fix for Qlik Talend Administration Center URL access control vulnerability (CVE-2026-9057) for details.
Cross-site scripting vulnerability (CVE-2026-9056)
A stored cross-site scripting security issue in the Qlik Talend Administration Center has been identified.
Affected Software
- All versions of Qlik Talend Administration Center before Patch_20260123_QTAC-1883 (cumulative patch)_R2026-01_v1-8.0.1 are affected.
See Security fix for Qlik Talend Administration Center cross-site scripting vulnerability (CVE-2026-9056) for details.
Recommendation
Upgrade at the earliest. The following table lists the patch versions addressing the vulnerabilities.
Always update to the latest version. Before you upgrade, check if a more recent release is available.
Product Patch Release Date Qlik Talend Administration Center
URL access control vulnerabilityQTAC-1471 November 21, 2025 Qlik Talend Administration Center
cross-site scripting vulnerabilityQTAC-1883 January 23, 2026 Thank you for choosing Qlik,
Qlik Support -
Qlik Automate: Automation ownership changes for Analytics Admins May 2026
Qlik introduced a change in how automation permissions are handled for the Analytics Admin role. When was the change introduced? The change is already... Show MoreQlik introduced a change in how automation permissions are handled for the Analytics Admin role.
When was the change introduced?
The change is already live as of the 11th of May, 2026.
What does that mean for me?
Analytics Admins can now claim ownership of another user's automation. After claiming ownership, they can make necessary changes to it and enable the automation. However, they can no longer transfer ownership to another user.
How do I claim ownership of an automation?
As an Analytics admin, to claim ownership of an automation:
- Navigate to the Automations section in the Administration Console
- Locate the automation you want to claim ownership of, and click the Actions menu (...)
- Choose Claim ownership
This behavior change only applies to Analytics Admins. Tenant admins can still transfer ownership to any user with the appropriate access rights in the tenant.
If you have any questions, we're happy to assist. Reply to this blog post or take your queries to our Support Chat.
Thank you for choosing Qlik,
Qlik Support -
Predicting London ULEZ effects on emissions.
Predicting London ULEZ effects on emissions.C40 CitiesUse historic emissions data and the expansion of Londo's Ultra Low Emissions Zone program to pre... Show MorePredicting London ULEZ effects on emissions.C40 CitiesUse historic emissions data and the expansion of Londo's Ultra Low Emissions Zone program to predict its effects on air quality in each of London's boroughs.Discoveries
We have been able to corroborate the model's accuracy using real data from the first ULEZ expansion, so we are confident it will predict the second expansion effects effectively.
Impact
This app and the ML experiment behind it has served as an internal demonstration of Qlik's machine learning capabilities, making its adoption easier.
Audience
Our internal data science team. It serves as a proof of concept for Qlik Predict.
Data and advanced analytics
An effective use case of machine learning in its prediction mode.
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From Raw Data to AI-Ready: Accelerating GenAI and Agentic Initiatives with Qlik ...
Most enterprise AI projects don’t fail because the model is wrong. They fail because the data isn’t ready. Data engineering leaders are now being aske... Show MoreMost enterprise AI projects don’t fail because the model is wrong. They fail because the data isn’t ready. Data engineering leaders are now being asked to support a new wave of generative and agentic workloads that demand fresher data, broader source coverage, tighter governance, and richer context than traditional BI ever required — and to deliver it without growing the team.
Qlik Talend Cloud Data Integration was built to close that gap. It provides a single, governed pipeline from operational sources to an open lakehouse — and on to the vector indexes, feature stores, and APIs that your AI systems actually consume. Combined with Qlik Open Lakehouse on Apache Iceberg, it turns your AI inputs into reusable AI data products: named, versioned, governed assets that any RAG application or agent can consume off the shelf.
This post walks through the reference architecture, the pipeline that produces those data products, and a worked example that takes raw CRM and product data all the way to a working RAG copilot and an agentic workflow — both running off the same Iceberg foundation.
Why data is the bottleneck for enterprise AI
GenAI and agentic systems are not fundamentally different consumers of data, but they are far more demanding ones. A model is only as accurate, current, and trustworthy as the context it retrieves at inference time. For data engineering leaders, that translates into six hard requirements:
- Freshness — Embeddings and agent context become stale quickly. Real-time CDC matters more than nightly batch.
- Breadth — Useful AI requires content from CRMs, ticketing systems, document stores, ERPs, and operational databases — often dozens of sources per use case.
- Quality — Bad data doesn’t just produce bad answers. It produces confidently wrong answers, which are worse.
- Governance — PII, masking rules, lineage, and access controls must travel with the data into vector stores and tool calls, not stop at the warehouse boundary.
- Openness — Locking AI-ready data into a proprietary store creates rework every time the model, framework, or query engine changes.
- Reuse — Hand-rolling a new pipeline for every AI use case is how programs stall. The same curated data should serve a RAG copilot today and an agent tomorrow.
Meeting all six at once with one-off pipelines is what kills enterprise AI velocity. The path forward is consolidation: one governed integration platform feeding one open lakehouse, with the Gold zone publishing reusable AI data products that any model, agent, or analyst can consume. Build once, govern once, serve many.
Qlik Talend Cloud + Iceberg: a reference architecture
The architecture has four layers: sources, integration, an open Iceberg lakehouse with medallion zones, and an AI serving layer. Qlik Talend Cloud handles change data capture, transformation, quality, and catalog metadata across the entire flow. The Gold zone is where curated outputs are published as named AI data products.
Two design choices make this architecture work for AI specifically.
First, the integration layer is real-time by default — log-based CDC keeps Bronze and Silver tables current without batch windows.
Second, Gold is treated as a publishing surface, not a staging area. Each Gold data product is named, versioned, governed, and discoverable in the catalog. RAG and agents become two interfaces over the same products: built once, governed once, consumed many times.
Figure 1. Reference architecture: Qlik Talend Cloud + open Iceberg lakehouse, serving RAG, agentic, and analytics workloads from the same governed Gold layer.
The pipeline: from raw data to AI use
The pipeline that operates on the architecture above runs in six stages — automated end-to-end, with quality and lineage enforced at every step. Each stage produces a more refined and trusted asset. Bronze preserves raw, append-only CDC for replay and audit. Silver applies data quality rules, deduplication, masking, and Type-2 history. Gold publishes AI data products: a document product (chunk-friendly text + metadata) for RAG, and a state product (curated entity, feature, and policy data) for agents. Both are versioned and registered, so consumers — vector indexers, semantic APIs, BI engines — read the same governed truth.
Figure 2. The six-stage pipeline. Because every stage writes to Iceberg, downstream consumers — vector indexers, semantic APIs, BI engines — read the same governed truth.
Worked example: from CRM tickets to a customer-support agent
Picture a data engineering team chartered with delivering an AI-powered customer-support assistant. The use case has both a RAG side (deflecting common questions with vetted answers) and an agentic side (the assistant can look up customer status, open tickets, and trigger actions). The raw inputs are typical:
- Salesforce — accounts, contacts, cases, case comments.
- ServiceNow — incident records and resolution notes.
- Confluence and SharePoint — a few thousand product KB articles.
- Postgres operational DB — subscription and entitlement state.
The pipeline at work
- Ingest. Qlik Talend Cloud uses log-based CDC to stream changes from Salesforce, ServiceNow, and Postgres in real time. KB articles are pulled on a connector schedule with content-hash detection so only changed docs flow through.
- Land in Bronze. Every change is written to append-only Iceberg tables in cloud object storage, partitioned by source and ingestion date. The raw audit trail is preserved for replay.
- Standardize in Silver. Push-down ELT cleanses text, masks PII (customer email, phone), conforms keys and status codes, and applies Type-2 history to entity tables (customer, case, entitlement, interaction). Trust scores are written alongside each table.
- Publish two Gold data products. rag_documents — KB articles + anonymized resolution notes from closed tickets, pre-joined and metadata-tagged for retrieval. agent_state — a fused customer_360 view, current entitlement state, and a small policy_rules table that defines what actions agents are allowed to take. Both are versioned, lineage-tracked, and registered in the catalog.
- Vectorize. rag_documents is chunked and embedded into a managed vector index with metadata filters for product, language, and access tier. The job is incremental — only new and changed rows of the data product trigger re-embedding.
- Serve and audit. agent_state is exposed via a thin Semantic API and parameterized SQL endpoints, ready for agent tool calls. Every agent action is written back to an audit_log Iceberg table — inputs, decision, tool call, outcome — so the same lakehouse that grounds the agent also makes its behavior explainable.
Powering RAG
When a customer asks “Why was my last bill higher than usual?”, the copilot retrieves the top-k chunks from the rag_documents data product, filtered by the customer’s product entitlement — with a structured lookup against agent_state for the customer’s current invoice context. Because the underlying data products are continuously refreshed by Qlik Talend Cloud, the copilot cites guidance that reflects the current pricing schedule, not last month’s. Every retrieved chunk carries its lineage, so answers can be traced back to a specific source row in Salesforce or a specific KB article version.
Powering agentic workflows
For agentic flows, the assistant plans and executes multi-step tasks against the same agent_state product: confirm identity, check entitlement, open a case in Salesforce via a write-back tool, and escalate to a human agent if confidence drops below a threshold defined in policy_rules. Every step is recorded in the audit_log table for explainability. The agent’s tools are backed by exactly the same data products the RAG side uses — which means a behavior change in the data, like a new product or pricing tier, propagates to both surfaces immediately, with no parallel pipelines and no copy-paste schemas. RAG and agents really are two interfaces over one set of products.
From pipeline to production: your next move
The fastest enterprise AI programs aren’t the ones with the cleverest prompts or the largest models. They’re the ones treating AI data products as the unit of delivery. Qlik Talend Cloud and Qlik Open Lakehouse give your team three things at once: real-time movement of broad source data, governed transformation into named and versioned data products, and an open Iceberg foundation that any model, framework, or agent can plug into. Build once, govern once, serve both RAG and agents from the same products.
A 10–15 day starting sprint for data engineering leaders:
- Pick one use case with two surfaces. Choose a domain where you need both a RAG copilot and a constrained agent (one or two write actions). Working backward from both surfaces forces the right data product shape.
- Stand up CDC into Iceberg. Wire two or three high-value sources into Bronze via Qlik Talend Cloud, build the Silver entity layer, and publish two Gold data products: one for retrieval, one for action.
- Measure freshness, trust, and reuse. Track event-to-context latency (freshness), quality-rule pass rate (trust), and how many AI surfaces consume the same Gold products (reuse). These three numbers tell you whether the pattern is ready to scale to the next domain — as configuration, not reinvention.
Talk to your Qlik team. Ask about the AI-ready data solution templates — pre-built pipeline patterns for the most common GenAI and agentic use cases, including the customer-service pattern walked through above.
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Unlocking Qlik Open Lakehouse Access from Talend Studio
Native Qlik Open Lakehouse interoperability for Talend Studio With the March release, Talend Studio introduces native support for querying Qlik Open L... Show More
Native Qlik Open Lakehouse interoperability for Talend StudioWith the March release, Talend Studio introduces native support for querying Qlik Open Lakehouse datasets through Amazon Athena — available in both Standard Data Integration jobs and Spark-based Big Data workflows.
This means developers can now connect to Qlik Open Lakehouse data, execute SQL queries, and integrate results downstream the Talend job without manual JDBC configuration or custom setup.
Connecting Talend Studio to Qlik Open Lakehouse
Talend Studio now connects natively to Qlik Open Lakehouse through Amazon Athena — a SQL query engine that runs directly on top of cloud storage, enabling access to Iceberg-managed data without data movement or duplication. Developers can:
- Access Qlik Open Lakehouse data with an out-of-the-box configuration, no manual JDBC setup required
- Execute SQL queries directly within Talend jobs (Standard and Big Data)
- Integrate Qlik Open Lakehouse data into existing Talend jobs without disrupting current workflows
Reliable by Design
Connecting to Qlik Open Lakehouse from Talend Studio is straightforward by design. The integration ships with dedicated Athena configuration and input components, eliminating manual setup. Runtime validation, improved error handling, and secure credential management ensure the connection remains stable and trustworthy in production environments.
How Data is Organized in Qlik Open Lakehouse
In Qlik Open Lakehouse, data is ingested incrementally and accumulated in Apache Iceberg tables. A logical abstraction layer — implemented as Trino views — resolves those changes into a consolidated latest-state representation, which different engines can query without handling change consolidation logic directly.
This model supports two complementary data patterns:
- Current-state access (SCD Type 1): query the latest-state view through Athena for operational and integration use cases
- Full history access (SCD Type 2): query the underlying Iceberg tables directly for time-aware and audit analysis
Both patterns are available across Standard Data Integration and Big Data jobs in Talend Studio, enabling teams to work with Qlik Open Lakehouse data in the way that best suits their use case.
Looking Ahead
This integration enables Talend Studio users to access Qlik Open Lakehouse data without changing their existing workflows — while aligning with modern, open-format architectures that support multiple query engines.
Athena is the first fully supported access path in this model, with a roadmap to extend support to additional engines over time. For organizations moving away from traditional data warehouses or adopting multi-engine strategies, this represents a concrete step toward a more flexible data architecture.
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Watch! Q&A with Qlik: Qlik Cloud Migration
Don't miss our latest 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. WATCH ... Show MoreDon't miss our latest 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.
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SAP S/4HANA 移行の「壁」を突破する！テストデータ管理を劇的に変える！
本コラムでは、先日開催された TechEd 2025 にて、Qlik の Miguel Antunes が提唱した「スマートなテストと迅速な移行」をテーマに、次世代の SAP データ管理手法を紐解きます。 1. 移行プロジェクトを阻む「テストデータの三重苦」 S/4HANA への移行は、単なるソ... Show More本コラムでは、先日開催された TechEd 2025 にて、Qlik の Miguel Antunes が提唱した「スマートなテストと迅速な移行」をテーマに、次世代の SAP データ管理手法を紐解きます。1. 移行プロジェクトを阻む「テストデータの三重苦」
S/4HANA への移行は、単なるソフトウェアのアップグレードではなく、データモデルそのものの刷新を伴います。そのため、本番に近い環境でのテストが不可欠ですが、現場では以下の課題が頻出しています。-
時間とコストの浪費： フルコピー（システムの丸ごとコピー）を行うと、数日から数週間かかり、その間本番環境がロックされることもあります。
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インフラの肥大化： 不要なテラバイト単位のデータまでコピーすることで、非本番環境（検証・開発環境）のストレージコストが跳ね上がります。
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コンプライアンスのリスク： 個人情報（PII）が含まれるデータをそのままテストに使うことは、GDPR などの観点からも許されません。
2. Qlik Gold Client が提供する「アジャイル」なデータ管理
これらの課題を解決するのが、SAP 認定のテストデータ管理プラットフォーム「Qlik Gold Client」です。従来の「全部コピー」という力技ではなく、「必要なデータだけを、スライスして、同期する」というアプローチに切り替えます。主な特徴：-
データサブセットの抽出： 関連するデータだけを選択してコピー。例えば、5TB の本番環境から必要な 950GB だけを切り出し、数時間でテスト環境を構築可能です。
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強力なマスキング（難読化）： 抽出と同時に個人情報を匿名化。セキュリティを担保しながら、実際のデータに基づいた精度の高いテストが行えます。
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並列実行とダウンタイム・ゼロ： ソースシステムをロックすることなく、バックグラウンドでのデータ抽出が可能です。
3. S/4HANA 移行における具体的メリット
Qlik Gold Client を導入することで、移行プロジェクトの ROI は劇的に向上します。-
移行の加速： 迅速な概念実証（PoC）が可能になり、プロジェクト全体の納期短延を回避。
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TCO（総保有コスト）の削減： 非本番環境のサイズを 10〜50% 削減。インフラコストを最小限に抑えられます。
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DevOps の実現： 本番環境からのライブデータを差分更新できるため、常に最新の状況で開発・テスト・トレーニングを回すことができます。
まとめ：2027年に向けて
SAP S/4HANA への移行を「単なる苦行」にするか「ビジネス変革の好機」にするか。その鍵は、データの扱い方にあります。Qlik Gold Client は、RISE with SAP や S/4HANA Cloud（プライベートエディション）にも対応しており、SAP Store でも提供されています。膨大なデータに足を取られる前に、データ管理の「スマート化」を検討してみてはいかがでしょうか。
詳細はこちら： Qlik Gold Client ページ
本ソリューションのポイント-
課題： フルコピーによるダウンタイム、コスト増、プライバシーリスク
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解決： Qlik Gold Client による「データのサブセット化」と「自動マスキング」
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結果： テスト環境構築が数日にから数時間へ。インフラコストの大幅削減とプロジェクトの加速
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The New Write Table
The write table was introduced to Qlik Cloud Analytics last month so in this blog post, I will review how it works and how it can be added to an app. ... Show MoreThe write table was introduced to Qlik Cloud Analytics last month so in this blog post, I will review how it works and how it can be added to an app. The write table looks like the straight table but editable columns can be added to it to update or add data. The updated/added data is visible by other users of the app provided they have the correct permissions. Read more on write table permissions here. Something else to note, if using a touch screen device, is you will have to disable touch screen mode for the write table to work. Looking at the write table for the first time, I found it intuitive and easy to use. Let’s create a write table with some editable columns to see how easy it is.
The write table object can be added to a sheet like any other visualization. Once it is added, columns can be added the same way dimensions and measures are added to a straight table. Below is a small write table with course information including the course ID, course name, instructor and location.
To add an editable column from the properties panel, click on the plus sign (+) and select Editable column.
The new editable column will be added. In the properties for the column, the title for the column can be modified and from the show content drop down, manual user input or single selection can be selected. Manual user input will create a free form column that the user can type into. The single selection option will allow me to create a drop-down list of options that the user can choose from.
I will change the title to Course Level and for show content I will select single selection and add three list items by typing the list item and then clicking on the plus sign to add it to the list. The list items will be displayed in the drop-down in the order they are added but can be rearranged by hovering over the list-item and dragging it to the desired position. List-items can also be deleted by hovering over it and clicking the delete icon that appears to the left.
When you come out of edit mode, the message below will appear for the editable column prompting you to define a set of primary keys.
Once you click Define, you will see the pop-up below where you can select the column(s) that will be used for the unique primary key. This is necessary to save and map the data entered in the editable column to the data model. I will select the CourseID column as the primary key.
Once this is done, I will see the Course Level column with the drop-down of list-items I added.
Let’s add one more editable column that takes manual user unput and name it Notes.
As I add data or update the editable columns, the cells will be flagged orange to indicate that my edits have not been saved. Once I save the table, they will be flagged green and any new values entered are visible to other users. A cell will be blue if another user is currently making changes to the row, thus locking it. Changes are saved for 90 days in a change store (temporary storage location) provided by Qlik. After 90 days, the data will be deleted. It is also important to note that if an editable column is deleted, the data will be lost. This is also the case if the primary key used for the editable column is removed.
It is possible to retrieve the changes from a change store via the change-stores API or an automation. Using the REST connection and the change-store API, the changes made in a write table can be retrieved and stored in a QVD (if needed for more than 90 days) or added to the data model for use in other analytics. Qlik Automate can also be used to retrieve data from the change-store using the List Current Changes From Change Store block or the List Change Store History block. From there the data can be stored permanently in an external system for later use or used in the automation for another process. Qlik Help offers steps for retrieving data from a change-store.
The write table can make it easy for users to add updates, feedback and important information that may not be available in the data model. Not only can this be done quickly, but it can be immediately visible to other colleagues. Learn more about the write table in the Product Innovation blog along with links to videos and write table FAQs.
Thanks,
Jennell
