Qlik Talend® Unlocked: See How Leading Teams Modernize Talend

Qlik Talend Unlocked is a monthly series where engineers who have shipped this into production show you how. Each session takes on a real part of the modern Talend lifecycle, from CI/CD job promotion and test automation to migration, open lakehouse architectures, and real time integration. Practical approaches you can take straight back to your own environment, with results teams can measure.Episode one is on demand now, episode two is open for r... Show More

Qlik Talend Unlocked is a monthly series where engineers who have shipped this into production show you how. Each session takes on a real part of the modern Talend lifecycle, from CI/CD job promotion and test automation to migration, open lakehouse architectures, and real time integration. Practical approaches you can take straight back to your own environment, with results teams can measure.

Episode one is on demand now, episode two is open for registration. Register once and you are set for the whole series.

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Product Innovation

The Final Whistle: Who Takes the Trophy - Model or Mastery?

The World Cup is over, Spain are champions, and the third and final episode of our Model vs. Mastery podcast series has wrapped up with a verdict on the question we've been asking all along: when it comes to predicting football's biggest tournament, does the edge belong to the model or the human expert? Host Adam sat down with Steve Palmer and Nick Magnuson to close out the series, and the results were more decisive than anyone on the losing end... Show More

The World Cup is over, Spain are champions, and the third and final episode of our Model vs. Mastery podcast series has wrapped up with a verdict on the question we've been asking all along: when it comes to predicting football's biggest tournament, does the edge belong to the model or the human expert?

Host Adam sat down with Steve Palmer and Nick Magnuson to close out the series, and the results were more decisive than anyone on the losing end expected.

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Design

“Where is Qlik headed with AI?”

So when people ask, “Where is Qlik headed with AI?” I think there’s a better question:

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Design

Qlik Answers – Fast Mode & Chat Download

In this blog post, we'll cover two new features of Qlik Answers that you may not be aware of. Fast mode Download conversation as PDF Fast Mode Qlik Answers Fast mode delivers quick, concise responses to simple questions using your structured data. It is ideal for questions that can be answered directly, without deep analysis. For complex questions that require more analysis, use Thinking mode. And don’t worry, if you ask a question in Fast mode... Show More

In this blog post, we'll cover two new features of Qlik Answers that you may not be aware of.

Fast mode
Download conversation as PDF

Fast Mode

Qlik Answers Fast mode delivers quick, concise responses to simple questions using your structured data. It is ideal for questions that can be answered directly, without deep analysis. For complex questions that require more analysis, use Thinking mode. And don’t worry, if you ask a question in Fast mode that requires more analysis, Qlik Answers will recommend that you switch to Thinking mode. Fast mode and Thinking mode can be used interchangeably in a single conversation.

fast mode.png

You can learn more about Fast mode here:

Download Conversations as PDF

Now, you can download your Qlik conversations for future reference or to share with others. There are two ways you can do this. You can download a chat once Answers has responded to your question. The download button will appear as shown below. When you click it, a PDF of the Qlik Answers chat will be downloaded. The download feature is available for chats created after this feature’s release.

Qlik Answers chats can also be downloaded from the Feedback tab in an assistant, but this requires the audit admin role. To view chats, open an assistant and go to the Feedback tab. Click on the three ellipses at the end of a row and select View answer details. From here, you can review and download the chat.

Fast mode and chat downloads are both live in Qlik Answers today, and they're built to make your workflow quicker and your insights easier to share. Ask a question in Fast mode next time you need a quick answer and download a chat any time you want to keep a record or bring a colleague up to speed.

Thanks,

Jennell

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Qlik Academic Program

What Students Really Gain From Working With Real Data

The most valuable lessons in analytics don’t always come from clean datasets, perfect answers, or step-by-step instructions. Sometimes, they come from the mess.

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Qlik Digest

Qlik Digest - Analytics Edition - August 2026

Agentic AI is now live inside the Qlik Cloud apps you already use, migration to Qlik Cloud just got a lot simpler, and South Central Ambulance Service shows how predictive maintenance keeps critical vehicles on the road.

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Qlik Digest

Qlik Digest - Data Edition - August 2026

Schneider Electric moved from weeks-long reporting to near-real-time data, plus five strategies for turning agentic AI into real outcomes and an easier path to Qlik Talend Cloud migration.

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127

Support Updates

Security Statement: npm Supply Chain Compromise, Qlik Package Impact

Initial Publication: August 4, 2026Edit August 5, 19:10 UTC: Impact section updated.Edit August 6, 06:11 UTC: Formatting changes only.Edit August 6, 06:35 UTC: Investigation concluded, removed banner. Certain open source packages released by Qlik® have been impacted by an ongoing, industry-wide npm supply chain compromise ("Shai-Hulud") affecting keyv, cacheable, and related packages. As of this writing, the campaign has affected many organizatio... Show More

Initial Publication: August 4, 2026
Edit August 5, 19:10 UTC: Impact section updated.
Edit August 6, 06:11 UTC: Formatting changes only.
Edit August 6, 06:35 UTC: Investigation concluded, removed banner.

Certain open source packages released by Qlik® have been impacted by an ongoing, industry-wide npm supply chain compromise ("Shai-Hulud") affecting keyv, cacheable, and related packages. As of this writing, the campaign has affected many organizations beyond Qlik, including over 440 unique packages and 2,200+ package versions across the npm ecosystem. A Qlik npm publishing credential was harvested from an internal build system after it installed a package with a poisoned transitive dependency, and was used to publish unauthorized, malicious versions of several Qlik-owned open source packages to the public npm registry. This appears to be a consequence of the broader worm's automated spread across the npm ecosystem, rather than a targeted attack on Qlik. The investigation into full scope and impact remains active, and Qlik has taken active steps to mitigate the impact.

Impact

We have confirmed that no Qlik products, including Qlik Cloud, Talend Cloud, and our client-managed offerings, were impacted.
No compromised code was released into any Qlik product.

31 packages in the @qlik npm scope had malicious versions published to the registry, all published and detected within the same 10:46 to 10:53 UTC window on August 4, 2026:

Package	Version
@qlik/api	2.14.2
@qlik/browserslist-config	3.0.2
@qlik/carbon-core	2.1.1
@qlik/carboncopy	1.1.6
@qlik/design-tokens	1.3.13
@qlik/dts-bundler	2.0.3
@qlik/embed-react	2.5.3
@qlik/embed-runtime	1.6.4
@qlik/embed-svelte	1.1.4
@qlik/embed-web-components	1.7.3
@qlik/eslint-config	2.0.20
@qlik/eslint-config-base	0.1.1
@qlik/eslint-config-react	0.1.1
@qlik/eslint-config-svelte	0.1.1
@qlik/eslint-config-vue	0.1.1
@qlik/nebula-table-utils	2.6.9
@qlik/oxfmt-config	0.1.6
@qlik/oxlint-config	0.7.2
@qlik/prettier-config	1.0.3
@qlik/react-native-simple-grid	1.5.5
@qlik/runtime-module-loader	1.5.1
@qlik/sdk	0.28.1
@qlik/sprout-design-docs	1.0.2
@qlik/sprout-gesture	0.0.13
@qlik/sprout-icons	0.12.3
@qlik/sprout-react	6.45.3
@qlik/sprout-react-table	0.16.7
@qlik/tsconfig	1.0.3
qlik-chart-modules	1.1.1
qlik-modifiers	0.10.1
qlik-object-conversion	0.17.2

What Qlik has done

Revoked the compromised npm publishing credential and disabled automated publishing pipelines (GitHub Actions), including across the qlik-oss organization
Confirmed that npm removed the malicious versions from the public registry; purging internal mirrors/caches
Added precautionary blocks on our cloud firewalls
Rotating exposed secrets and keys, in a controlled sequence, after confirming affected hosts are clean
Completed indicator-of-compromise sweeps across related GitHub organizations; no indicators of persistence found
Continuing a full investigation across our repositories and build infrastructure

What you should do

If you installed any of the affected package versions listed above:

Treat any npm, GitHub, or cloud credentials present in that environment as potentially compromised
Scan for compromise before rotating credentials; do not rotate first
Reinstall using npm install --ignore-scripts once a clean version is confirmed available

More information

For independent tracking of the scale of this campaign across the npm ecosystem, see Socket's write-up: keyv and cacheable compromise.

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Qlik Learning

Harness the power of AI with this brand-new certification!

The Qlik AI Specialist certification validates your knowledge of key AI concepts and applications, including the fundamental skills needed to create Qlik predictive AI models and generative AI assistants.

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1151

Product Innovation

SAP Say "You Better Stop" using ODP-RFC - Here's What Qlik Customers Need to Do

Something significant has shifted in the SAP ecosystem. SAP first prohibited ODP-RFC access for third-party tools back in February 2024. For over two years, that rule existed on paper without technical enforcement. In April 2026, that changed: SAP announced active enforcement was coming, and in June 2026, it arrived. Here is your plain-language explanation of what it means for Qlik customers, and where Qlik goes from here.

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Support Updates

Upcoming QlikView component deprecation: QlikView OCX, Plugin for Cloud, Source ...

As Qlik moves to further modernize its existing product line, the following legacy QlikView components will be deprecated and are no longer included in the upcoming QlikView September 2026 release: QlikView OCX: The QlikView OCX Component will no longer be maintained, and no further releases will be made available as of the September 2026 release. QlikView IE Plugin for Cloud: While connecting to QlikView documents in Qlik Cloud will continue to... Show More

As Qlik moves to further modernize its existing product line, the following legacy QlikView components will be deprecated and are no longer included in the upcoming QlikView September 2026 release:

QlikView OCX: The QlikView OCX Component will no longer be maintained, and no further releases will be made available as of the September 2026 release.
QlikView IE Plugin for Cloud: While connecting to QlikView documents in Qlik Cloud will continue to function as expected with previous IE Plugins for Cloud, no new versions of the client will be released.
The IE Plugin for On-Premise installations will remain supported and will be included in the September 2026 release.
QlikView Source Control: Connecting QlikView Desktop to a Source Control system will no longer be supported or updated as of the September 2026 release.
QlikView WorkBench: QlikView Workbench, used in Visual Studio to build QlikView integrations, will no longer be supported or updated as of the September 2026 release.

What about previous versions?

Older supported releases of the listed features (such as QlikView September 2025) remain fully operational and will be maintained in accordance with Qlik's Release Management Policy.

We're happy to assist with any follow-up questions. Reply to this blog post or take your queries to our Support Chat.

And if you're ready to migrate to Qlik Cloud, get started with Migrating from QlikView, or contact your Qlik account team to arrange a more hands-on consultation.

Thank you for choosing Qlik,
Qlik Support

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Product Innovation

Introducing the Next Evolution of Agentic Analytics in Qlik

We are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effective action. This launch builds on the agentic experience we introduced earlier this year and continues our vision of Qlik as the trusted intelligence layer for AI, helping you move beyond analytics consumption toward a system where AI reasons, predicts, acts, and assists—grounded in trusted data and... Show More

We are extending our agentic architecture with new analytics agents and capabilities that deepen AI reasoning, broaden access, and drive more effective action.

This launch builds on the agentic experience we introduced earlier this year and continues our vision of Qlik as the trusted intelligence layer for AI, helping you move beyond analytics consumption toward a system where AI reasons, predicts, acts, and assists—grounded in trusted data and powered by the Qlik analytics engine.

Here is what’s new.

Drive Action from Insight with Automate Agent

With the new Automate Agent, you can now use natural language to trigger actions and workflows directly from your analytics experience.

The agent executes workflows across Qlik and downstream systems based on user requests or AI-driven recommendations. It determines the appropriate automation, prompts confirmation when needed, and executes tasks end-to-end.

Instead of stopping at an insight, you can move immediately to execution, helping your teams respond faster and close the loop between analytics and outcomes.

See What’s Likely to Happen with Predict Agent

Our new Predict Agent lets you ask forward-looking questions in plain language and generate predictions without requiring data science expertise.

Built on Qlik Predict, the agent guides you through the full data science lifecycle from understanding the problem, building and validating models, to generating predictions and interpreting results—with full explainability.

Make it easier for more people to use predictive analytics to make proactive and better-informed decisions.

Screenshot 2026-06-24 at 5.32.37 AM.png

Get Faster, Richer Answers from Qlik Answers

We are introducing several new improvements to your Qlik Answers that improve speed, expand input types, and increase control over AI interpretation.

Fast Mode:

Get faster answers for more simple questions.

Fast mode will deliver concise, low-latency responses for quick exploring and faster-paced workflows while still benefiting from the accuracy and trust of the Qlik analytics engine.

Screenshot 2026-06-09 at 12.08.51 PM.png

Multimodal Capability:

Coming soon

Qlik Answers will be able to read images, charts, and documents.

By understanding and reasoning over visual content, Qlik Answers will unlock insights from a wider range of business materials, reducing manual effort of interpreting non-text data.

Semantic Customization:

Coming soon

Provide context for your data to guide Qlik Answers.

Customize semantic information by adding definitions for fields and master items directly within the logical model, to give Qlik Answers a better understanding of what your data means. This creates a transparent and correctable process to review, edit, and enrich business definitions and context to prevent misinterpretations before they influence answers or impact users.

Unlock powerful insight through MCP with new advanced reasoning tools

Coming soon

New engine-driven advanced reasoning MCP tools deliver mathematically rigorous analytics directly to AI agents and assistants. These capabilities extend analysis beyond basic querying and enable advanced decision intelligence driven by Qlik’s analytics engine.

Analyze the Gray: Discover meaningful patterns hidden in excluded data and understand what may be missing from the current view

Prove Causality: Estimate the causal effect of one variable on another and evaluate alternative scenarios

Optimize Decisions: Determine the best allocation of resources under real-world constraints

Simulate Risk: Explore thousands of possible outcomes and understand probabilities and confidence ranges

Predict Outcomes: Train and deploy machine learning models directly against governed Qlik data

Explore Alternatives: Re-run history under different conditions to understand the impact of actions not taken

The next frontier in AI won’t be determined by which LLM you choose, but by how much analytical power you have under the hood. These capabilities will enable assistants and agents to reason more effectively using governed data and engine-driven analytics that drive more informed, explainable, and actionable decisions.

The Next Step in Agentic Analytics

With this June release, you can now go further with AI across every stage of decision-making. This helps transform analytics into trusted intelligence your teams can rely on.

Get started and learn more:

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Community News

Some Mid-Year Updates!

Hi Qlik Community, We have a few exciting updates to share this month! From a new home for our local meetup groups to interactive Community discussion and learning opportunities, there is so much to explore across the Community. Take a look at what’s new:

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131

Design

Select Visible Points on a Map in Qlik Cloud using Spatial Indexes

If you have worked with point maps in Qlik, you are probably familiar with how selections normally work. You zoom into an area, use the lasso tool to draw around the points you want, confirm the selection, and repeat the process if you want to explore somewhere else. Qlik Cloud now supports another option for point layers called Auto select visible:Instead of manually selecting points, you move around the map and Qlik updates the selection based ... Show More

If you have worked with point maps in Qlik, you are probably familiar with how selections normally work. You zoom into an area, use the lasso tool to draw around the points you want, confirm the selection, and repeat the process if you want to explore somewhere else.

Qlik Cloud now supports another option for point layers called Auto select visible:
Instead of manually selecting points, you move around the map and Qlik updates the selection based on the area you are viewing. Zoom into Philadelphia and the locations in that area are selected. Pan over to another city and the selection changes with the map.

There is a little more happening behind the scenes, though. Before you turn on Auto select visible, you first need to create a Spatial Index for your location data. In this post, I’ll walk through what that means and how you can try it yourself with a simple example.

What changed?

The idea of selecting visible locations is not completely new to Qlik. Auto Select Visible existed in the GeoAnalytics map extension before it became available in the native Qlik Cloud map.

What changed recently is that you can now use this behavior directly in the native Qlik map chart. You no longer need the old GeoAnalytics map extension, but you still use GeoOperations to create the Spatial Index that the map relies on.

The easiest way to think about the workflow is that there are two parts. During reload, GeoOperations prepares the Spatial Index and adds it to your data model. Once the app is loaded, the native Qlik map uses that index as the user pans and zooms.

What is a Spatial Index?

A Spatial Index is a way of organizing geographic data so Qlik has a more efficient way to understand where points are located.

Imagine a grid placed over your map. Each location belongs to one or more cells in that grid, depending on the level of detail. Qlik creates several levels of these cells so it has smaller cells for detailed views and larger cells for wider geographic views.

When GeoOperations creates the index, you will see fields such as SpatialIndex, SpatialIndexLevel2, SpatialIndexLevel3, and so on. These values represent cells in the spatial grid.

This is useful because Qlik does not need to treat every map movement as a completely new geographic calculation across all of your points. It already has a spatial structure that tells it how those locations are grouped geographically.

Why does this need to happen in the load script?

The reason is that the map setting and the Spatial Index have different jobs. The Spatial Index prepares your location data during reload. "Auto select visible" then uses that prepared information while interacting with the map.

So the Spatial Index is created once when the app reloads instead of being rebuilt every time somebody pans or zooms the map.

GeoOperations connection

You need GeoOperations to create the Spatial Index, but you do not have to build the script manually.

Screenshot 2026-08-07 105002.png

If you create a Qlik GeoOperations connection, you can select Spatial Index from the available operations, choose your fields, and let Qlik generate the script for you. This is probably the easiest option if you are using the feature for the first time.

You can also write the GeoOperations ScriptEval call directly in the Data load editor.

Screenshot 2026-08-07 105356.png

Try it with a simple example

Attached to the post is a QVF that uses a demo dataset containing SiteID, SiteName, Metro, State, Latitude, Longitude, MonthlyRevenue, and Customers.

Step 1: Load the locations

First, upload the CSV into DataFiles in your Qlik Cloud app or space. Then load it using the following script:

Sites:
LOAD
    SiteID,
    SiteName,
    Metro,
    State,
    Num#(Latitude, '0.000000', '.', ',') as Latitude,
    Num#(Longitude, '0.000000', '.', ',') as Longitude,
    Num#(MonthlyRevenue, '0.00', '.', ',') as MonthlyRevenue,
    Num#(Customers, '0', '.', ',') as Customers,
    GeoMakePoint(
        Num#(Latitude, '0.000000', '.', ','),
        Num#(Longitude, '0.000000', '.', ',')
    ) as SitePoint
FROM [lib://DataFiles/qlik_spatial_index_demo_20k.csv]
(txt, codepage is 28591, embedded labels, delimiter is ',', msq);

The important part here is SitePoint. Our CSV stores latitude and longitude separately, so GeoMakePoint combines them into the geographic point field we will use on the map and pass to GeoOperations.

Step 2: Create the Spatial Index

Once the Sites table is loaded, we can pass it through the GeoOperations Spatial Index operation.


[SiteSpatialIndex]:
LOAD *
EXTENSION GeoOperations.ScriptEval('
    SELECT
        SiteID,
        SpatialIndex,
        SpatialIndexLevel2,
        SpatialIndexLevel3,
        SpatialIndexLevel4,
        SpatialIndexLevel5,
        SpatialIndexLevel6
    FROM SpatialIndex(
        gridSize="0.002",
        gridWidthHeightRatio="1.5",
        nLevels="6",
        levelFactor="4"
    )
    DATASOURCE dataset INTABLE
        keyField="SiteID",
        pointField="SitePoint",
        crs="auto"
', Sites);

The two fields that matter most here are keyField and pointField. SiteID connects the generated index back to the original Sites table, while SitePoint tells GeoOperations which geographic field should be indexed.

For this example, I am also using a gridSize of 0.002. I originally tested the default value of 0.04, but at a detailed street-level zoom it grouped too many nearby locations into the same grid cell. Reducing it to a lower number gave the demo much more precise selections when zooming into individual points.

After the reload, your data model will contain the original Sites table and the SiteSpatialIndex table, associated through SiteID.

Step 3: Create the map

Now add a native Map chart and create a Point layer. Use SiteID as the dimension and SitePoint as the location field.

Once the point layer is working, go into Map settings and change Zoom behavior from Auto zoom to Auto select visible. A Spatial index option will appear underneath it. Select SpatialIndex.

Screenshot 2026-08-07 103850.png
Screenshot 2026-08-07 103859.png

At this point, start zooming and panning around the map. Qlik will update the selection as the visible area changes.

Auto zoom versus Auto select visible

It is also worth understanding how this differs from the default Auto zoom behavior.

With Auto zoom, you make a selection somewhere in the app and the map moves to show the selected locations. Auto select visible works in the opposite direction. You move the map yourself, and the map view drives the selection.

Why might I see more selected sites than visible points?

Spatial Index selections work with grid cells, not individual screen pixels. If several locations fall inside the same spatial index cell, those sites can all become associated with the selection even when only one point appears inside a tightly zoomed map view.

That is where gridSize becomes important. A larger grid size creates larger cells, which is useful when users normally work at wider geographic views. A smaller grid creates more detailed cells and gives you more precise behavior when users zoom down to individual streets or locations.

I attached the sample CSV and complete load script so you can try the same setup in your own Qlik Cloud tenant.

Thank you for reading!

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Product Innovation

Connect All the Things: Qlik Talend Cloud's Latest Connector Releases - and a Mi...

Everybody wants to rule their data world — and this release gets you closer. Twelve new Stitch-powered connectors land in Qlik Talend Cloud, from Circle CI to Shopify to Microsoft Teams. But the bigger news drops on 1 July: every Stitch-originated connector now travels the full journey — Replication, Pipelines, and Qlik Open Lakehouse. No more "Replication only." Read on.

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Support Updates

Upcoming Maintenance for Qlik Cloud - AWS – Europe (Ireland): August 2026

Qlik Cloud tenants hosted in the Europe (Ireland) region will undergo scheduled maintenance in August 2026 to upgrade the database supporting the underlying authentication service. The upgrade strengthens our ability to deliver a secure and reliable experience. The maintenance window is expected to last 30 minutes. Other regions are unaffected. What is the expected impact? If you are already signed in: You can continue working as usual. If yo... Show More

Qlik Cloud tenants hosted in the Europe (Ireland) region will undergo scheduled maintenance in August 2026 to upgrade the database supporting the underlying authentication service. The upgrade strengthens our ability to deliver a secure and reliable experience.

The maintenance window is expected to last 30 minutes.

Other regions are unaffected.

What is the expected impact?

If you are already signed in: You can continue working as usual.

If you need to sign out or sign in: Logging in and out are temporarily unavailable during the maintenance window. If you attempt to do so, you may encounter the following message:

{"errors":[{"title":"Unexpected server error","code":"UNEXPECTED","status":"500"}]}

This is expected and does not indicate a problem with your account.

What do I need to do to prepare?

No action is required.

What follow-up actions are required?

None.

When will the maintenance take place?

The maintenance is scheduled for:

Region	Maintenance Start	Maintenance End
Europe (Ireland) (eu-w-1)	Wednesday 12 August 2026 21:30 IST (Ireland) UTC: 12/08/26 – 20:30	Wednesday 12 August 2026 22:00 IST (Ireland) UTC: 12/08/26 – 21:00

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

Thank you for choosing Qlik,
Qlik Support

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Product Innovation

Qlik Open Lakehouse on Google Cloud Platform

Private Preview · Google Cloud Qlik Open Lakehouse comes to Google Cloud Sign up for the Private Preview and get hands-on with real-time Iceberg tables in your own Google Cloud project — no lock-in, no re-architecting. Sign up for the Private Preview! If you're running analytics on Google Cloud, you already know the tension. BigQuery is fast and familiar, but the more data you pour into it, the more your ingestion and storage bill climbs, and ... Show More

Private Preview · Google Cloud

Qlik Open Lakehouse comes to Google Cloud

Sign up for the Private Preview and get hands-on with real-time Iceberg tables in your own Google Cloud project — no lock-in, no re-architecting.

Sign up for the Private Preview!

If you're running analytics on Google Cloud, you already know the tension. BigQuery is fast and familiar, but the more data you pour into it, the more your ingestion and storage bill climbs, and the more your data ends up shaped around one engine. You want the performance without the lock-in, you want your data to stay yours, and you want to free up some budget for more valuable workloads.

That's exactly the problem Qlik Open Lakehouse solves — and we're getting ready to launch a Private Preview on Google Cloud this September that you can be part of.

Qlik Open Lakehouse gives you a fully managed way to ingest real-time data straight into Apache Iceberg tables that live in your own Google Cloud Storage buckets. No proprietary formats, no copies, no re-architecting your data layer. Query those same tables from BigQuery, Trino, or any Iceberg-compatible engine, and pay a fraction of what you'd normally spend moving and storing that data.

If you've followed our work on AWS, this will feel familiar — and that's the point. We're porting a proven architecture to Google Cloud, not building a science experiment.

How it works: a walk through the architecture

Open Lakehouse builds the bronze layer of your medallion architecture, ingesting and landing real-time data as optimized Apache Iceberg tables, with the heavy lifting executed in your own Google Cloud project. From there, push-down transformations in Qlik Talend® Cloud pipelines create your silver and gold layers directly on BigQuery. Here's the path your data takes, stage by stage.

	1 · Ingestion at the edge Your sources land through two paths. For change data capture and bulk loads from databases, SAP, and mainframe systems, the Qlik Data Movement Gateway sits in your managed environment and handles CDC and bulk ingestion. For streaming and event data, you connect Kafka topics or Google Cloud Storage directly. Both routes feed the landing stage without your data ever leaving your control. Later in the Private Preview, we'll also add support for Qlik Replicate as a source, so if you're already a Replicate customer, you can onboard data from your existing CDC sources into Iceberg without migrating a single pipeline or switching tools.
	2 · Compute in your project Two components run on Google Compute Engine inside your environment. The Network Integration Agent runs on a standard Compute Engine VM and brokers secure connectivity between the Qlik Talend Cloud tenant and your resources. The Open Lakehouse Cluster — the engine that writes and optimizes your Iceberg tables — runs on a Compute Engine managed instance group built on Spot VMs. That Spot-based design is deliberate: it's where a large part of the cost savings comes from, because you're running the ingestion and optimization workload on Google Cloud's most cost-efficient compute instead of always-on, premium-priced infrastructure.
	3 · Open storage you own Everything lands in Google Cloud Storage, split across a landing bucket for incoming data, a config bucket for pipeline metadata, and table storage where your data comes to rest as Apache Iceberg tables. These buckets are in your account. The data is written in an open table format from the start, so there's never a moment where it's trapped in something only Qlik can read.
	4 · A catalog any engine can use Iceberg tables are registered in the Google Lakehouse runtime catalog, so your tables are discoverable and governable through Google Cloud's native catalog rather than a Qlik-proprietary one. That's what makes zero-copy querying real: BigQuery reads the same tables through ELT, and so can Trino or any other Iceberg-compatible engine, all pointing at one physical copy of the data.
	5 · A managed bronze layer, ready to build on The pipeline moves through managed stages — landing, storage, and mirror — to produce clean, governed Iceberg tables without you hand-coding each hop. You get Type 1 and Type 2 slowly changing dimensions or append-only patterns out of the box, so history and current-state tables are handled for you. We'll also add zero-copy mirroring to BigQuery during the Private Preview, so you can surface your Iceberg data straight in BigQuery. If you want to replace an existing native table, you can, since downstream queries keep working exactly as before.

The result: real-time data flows from your sources into governed, query-ready Iceberg tables in your own Google Cloud Storage, optimized continuously, queryable from BigQuery and beyond — and you never gave up control of a single byte.

Built on proven AWS foundations

We didn't start from zero for Google Cloud. Qlik Open Lakehouse first shipped on AWS, where it's already running these same patterns: CDC and streaming ingestion, Spot-based compute for cost-efficient optimization, Iceberg tables in customer-owned object storage, and open querying across engines.

Bringing it to Google Cloud is a deliberate step toward feature parity across clouds. The architecture mirrors what's proven on AWS, adapted to Google Cloud's building blocks: Compute Engine in place of EC2, Google Cloud Storage in place of S3, BigQuery and the Google Lakehouse runtime catalog as the native query and catalog layer. Same engine, same principles, GCP-native execution.

This matters because of what it says about our direction. We believe you should be free to choose where your data lives, which engine queries it, and to change your mind later without a migration project. Delivering the same open lakehouse capability across AWS and Google Cloud, with more to come, is how we make that freedom real rather than a slide in a pitch deck.

Why it matters

Everything above maps to three things we care about:

TRUST

Build governed data products in Qlik Talend Cloud on top of your Iceberg tables. Your data stays in open Apache Iceberg format, in your own account.

FREEDOM

Query from BigQuery, Trino, or any Iceberg-compatible engine, and swap or add engines later without re-architecting. No lock-in to a single vendor or format.

COST OPTIMIZATION

Spot VM compute and zero-copy, single-storage architecture mean you spend far less to move and hold your data at scale.

Join the Private Preview

We're accepting participants now, and we're prioritizing organizations already standardized on Google Cloud and working with BigQuery or Apache Iceberg in their accounts.

Request your spot in the Private Preview →

Opt in through the form and the Qlik team will reach out to schedule a welcome call. Already signed up? Sit tight — we'll be in touch.

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Japan

【9/10（木）- 11（金）開催】Snowflake World Tour Tokyo に出展します！

Qlik は、Blue Square Partner として、ブースとシアターセッションで参加します！エンタープライズ AI 戦略において、Snowflake 上のデータ基盤整備は急務となっています。本展示では、基幹システムからのリアルタイムのデータパイプライン構築、データ抽出コストを最適化できる Iceberg レイクハウス、分析とデータエンジニアリングをシームレスにつなぐ MCP サーバーをご紹介します。統合からインサイト創出まであらゆるデータから価値を生む AI 時代の最新アプローチをご体験いただけます。ぜひ、Qlik ブース（Blue Square ＃25）・Qlik シアターセッションにご来場ください！今すぐ申し込む Qlik シアターセッション概要 9/10（木）シアターB 15:35-15:50AI エージェントに Qlik のすべてを－ MCP Server で広がる Snowflake 活用Qlik MCP Server は、Qlik のすべてを AI ... Show More

Qlik は、Blue Square Partner として、ブースとシアターセッションで参加します！

エンタープライズ AI 戦略において、Snowflake 上のデータ基盤整備は急務となっています。本展示では、基幹システムからのリアルタイムのデータパイプライン構築、データ抽出コストを最適化できる Iceberg レイクハウス、分析とデータエンジニアリングをシームレスにつなぐ MCP サーバーをご紹介します。統合からインサイト創出まであらゆるデータから価値を生む AI 時代の最新アプローチをご体験いただけます。

ぜひ、Qlik ブース（Blue Square ＃25）・Qlik シアターセッションにご来場ください！

今すぐ申し込む

Qlik シアターセッション概要

9/10（木）シアターB 15:35-15:50

AI エージェントに Qlik のすべてを－ MCP Server で広がる Snowflake 活用

Qlik MCP Server は、Qlik のすべてを AI エージェントに解放する仕組みです。Snowflake Native App を通じて、CoWork や Cortex Agents から自然言語でのアプリ探索やダッシュボード操作、Declarative Pipelines では Snowflake へのパイプライン構築を実現します。分析とデータエンジニアリングを 1 つの AI の入り口でつなぐ新しい形を紹介します。

クリックテック・ジャパン株式会社
ソリューション技術部シニアソリューションアーキテクト
阿部智師

9/11（金）シアターB 14:35-14:50

Iceberg レイクハウスが変える、エンタープライズデータ統合・活用のコスト常識

SAP やメインフレームなど複雑な基幹システムからのCDC によるリアルタイム統合、ノーコードでのデータ品質・変換ルール適用、AI SQL Assistant によるクエリ生成、Trust Score とリネージで裏付けられた信頼できる AI-Ready なデータ基盤構築をデモでご紹介します。加えて、Iceberg レイクハウスが実現する大量データの抽出におけるコストの最適化、2.5 倍のデータ鮮度、Snowflake へのゼロコピー連携もご紹介。コスト常識を塗り替えます。

クリックテック・ジャパン株式会社
ソリューション技術部プリンシパルソリューションアーキテクト
中嶋翔

今すぐ申し込む

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Explore Qlik Gallery

Recruiting Assistant

Recruiting Assistant iA American Warranty Group This Qlik Sense application helps internal recruiters efficiently review and identify qualified job applicants. It analyzes resume content and displays a word cloud of key skills, qualifications, and terms, allowing recruiters to quickly spot candidates with relevant experience. When a candidate is selected, their resume is displayed directly within the application for detailed review. Used by... Show More

Recruiting Assistant

This Qlik Sense application helps internal recruiters efficiently review and identify qualified job applicants. It analyzes resume content and displays a word cloud of key skills, qualifications, and terms, allowing recruiters to quickly spot candidates with relevant experience. When a candidate is selected, their resume is displayed directly within the application for detailed review. Used by the recruiting team, the app reduces manual screening time, improves candidate discovery, and enables faster, more informed hiring decisions.

Discoveries

Best-fit candidates faster: Recruiters can quickly identify applicants whose resumes include the most relevant keywords for a specific role. Skill gaps in the applicant pool: The app may reveal when certain required skills or qualifications are missing or uncommon across candidates. Reduced manual screening effort: By narrowing candidate review through keyword patterns, the app helps recruiters spend more time evaluating strong matches instead of scanning every resume manually.

Impact

The application significantly reduces the time recruiters spend manually reviewing resumes by quickly highlighting candidates with relevant skills and experience. It improves the quality and consistency of candidate screening, helps uncover qualified applicants who might otherwise be overlooked, and enables faster hiring decisions. By providing immediate visibility into resume content and key qualifications, the app increases recruiting efficiency and supports better talent acquisition outcomes.

Audience

HR and hiring managers

Data and advanced analytics

The app has reduced the time recruiters spend screening resumes by quickly identifying candidates with the most relevant skills and experience. This has enabled faster, more consistent candidate evaluations and helped uncover qualified applicants who may have been overlooked during manual reviews.

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Support Updates

Qlik Stitch: Facebook Ads Insights API enablement effective August 6th, 2026

Effective August 6th, 2026, the following breakdowns need to be enabled for Ad Accounts using the Stitch Facebook Ads connector: breakdowns=impression_device (and any combination including impression_device) breakdowns=hourly_stats_aggregated_by_audience_time_zone breakdowns=frequency_value This change is driven by Facebook. For details, see Ads Insights API -- Breakdown Availability Changes | developers.facebook.com. Thank you for choosing Q... Show More

Effective August 6th, 2026, the following breakdowns need to be enabled for Ad Accounts using the Stitch Facebook Ads connector:

breakdowns=impression_device (and any combination including impression_device)
breakdowns=hourly_stats_aggregated_by_audience_time_zone
breakdowns=frequency_value

This change is driven by Facebook. For details, see Ads Insights API -- Breakdown Availability Changes | developers.facebook.com.

Thank you for choosing Qlik,
Qlik Support

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Blogs

Recent Blog Posts

Impact

What Qlik has done

What you should do

More information

What about previous versions?

What is the expected impact?

What do I need to do to prepare?

What follow-up actions are required?

When will the maintenance take place?

Qlik Open Lakehouse comes to Google Cloud

How it works: a walk through the architecture

1 · Ingestion at the edge

2 · Compute in your project

3 · Open storage you own

4 · A catalog any engine can use

5 · A managed bronze layer, ready to build on

Built on proven AWS foundations

Why it matters

Join the Private Preview

Qlik は、Blue Square Partner として、ブースとシアターセッションで参加します！

Discoveries

Impact

Audience

Data and advanced analytics