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Recent Blog Posts
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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 ... Show MoreIf 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.
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.
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.
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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Connect All the Things: Qlik Talend Cloud's Latest Connector Releases - and a Mi...
Back in 1985, Tears for Fears sang "Everybody Wants to Rule the World." They weren't thinking about data integration pipelines - but they might as wel... Show MoreBack in 1985, Tears for Fears sang "Everybody Wants to Rule the World." They weren't thinking about data integration pipelines - but they might as well have been. Because if you're a data engineer or an analytics leader in 2026, it’s not world domination you are after; it's domination of your data world: you want all your data, wherever it lives, moving wherever it needs to go.
This round of latest connector releases takes us a meaningful step closer to that.
What's New
We’ve been hard at work natively integrating Stitch's full SaaS connector catalogue into Qlik Talend Cloud, giving you access to 100+ pre-built connectors for popular sources including Salesforce, Jira, Marketo, Zendesk, and more.
Since May, we have released twelve more Stitch-powered connectors in Qlik Talend Cloud, some new and some improved, covering a broad sweep of the modern SaaS stack. Whether your data lives in a DevOps pipeline, an e-commerce platform, a marketing automation tool, or a customer engagement suite, there's something here for you.
The additional Stitch-powered connectors are:
- Circle CI - continuous integration and delivery pipelines
- NetSuite - ERP and financial management
- SendGrid - email delivery and engagement
- Shopify - e-commerce platform
- Contentful - headless CMS
- Delighted - customer experience and NPS
- Iterable - cross-channel marketing
- Branch - mobile attribution and linking
- Segment - customer data platform
- Microsoft Graph - Microsoft 365 data and services
- Microsoft Teams - collaboration and communications
- Sendwithus - transactional email
That's a wide net. From pulling CI/CD telemetry out of Circle CI to surfacing customer sentiment from Delighted, to unifying your Microsoft 365 data through MS Graph - the range reflects just how distributed the modern data landscape has become. Remember the saying “there’s an app for that”. Well, think of Qlik Talend Cloud when you want to move data from 'that app'.
The Milestone That Actually Matters
Here's where it gets interesting - and I'd argue this is the more important story with this update.
July 1st is an important milestone in Qlik Talend Cloud. This update is not just about extending the connector list. It also completes something we’ve been working on for a while. Because now All of the connectors, including every previous Stitch-originated SaaS connector that is available in Qlik Talend Cloud today and in the future, will support the full range of data movement use cases: Replication, Pipelines, and Qlik Open Lakehouse.
Previously, Stitch-based connectors were limited to the Replication use case. That was fine as far as it went, as that was exactly the use case Stitch was built for - but it left a gap in today's modern world. If you wanted to route data into a pipeline or land it directly into an open lakehouse architecture with iceberg tables, these connectors weren't available for the job.
That gap is now bridged.
What does that mean in practice? It means that whatever architectural path you're on - whether you're running straightforward replication into a warehouse, building out event-driven pipelines, or building a modern data architecture on open formats like Iceberg and open standards with Qlik Open Lakehouse - your Stitch-powered connectors are ready to travel the whole journey with you. No asterisks. No "supported for this use case only."
Why Stitch Connectors in Qlik Talend Cloud?
A quick word on the "under the hood" picture, for anyone coming to this fresh.
Stitch is a SaaS-native data ingestion technology from Qlik’s Talend acquisition, purpose-built for cloud-based source connectivity. The connectors delivered through this program are taking the best of Stitch technology and making it better in Qlik Talend Cloud, designed to connect to the APIs of modern SaaS applications - the kind of tools that power marketing, sales, operations, development, and finance teams - and bring that data into your data movement infrastructure securely, reliably and at scale.
The connectors are delivered continuously in the cloud, so you don't have to wait for a software release cycle to access new sources. When a new connector ships, it's available in your source and targets list when building your projects. These twelve are the latest in a steady cadence that's been building the breadth of Qlik's SaaS connectivity for some time now.
What's Next
The cadence continues. For a complete list of connectors, check out the Connector Factory on Qlik.com and our documentation in Qlik Help. These sites will be continuously updated as new connectors are released.
If you're looking for a specific connector that isn't listed on the Qlik Connector Factory, then you can request it by clicking the request button on the bottom of that page.
The best place to track what's coming - and to register your ideas to help shape Qlik - is the Qlik Community Ideation Forum.
If you're ready to start moving data from any of these latest new connectors, the Qlik Talend Cloud documentation has everything you need to get set up.
Qlik Help: Qlik Talend Cloud Connectors →
Not tried Qlik Talend Cloud yet? You should. Try for free here
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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 unde... Show MoreQlik 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 – 21:00To 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 -
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 ta... Show MorePrivate Preview · Google CloudQlik 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.
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:
TRUSTBuild 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.
FREEDOMQuery 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 OPTIMIZATIONSpot 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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【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... Show MoreQlik 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 -
Recruiting Assistant
Recruiting Assistant iA American Warranty Group This Qlik Sense application helps internal recruiters efficiently review and identify qualified ... Show MoreRecruiting AssistantiA 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 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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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=impressio... Show MoreEffective 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_zonebreakdowns=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 -
【8/25（火）ブース, 9/11（金）オンライン講演】小売未来 Days 2026 に出展します！
Qlik は、ブースおよびオンライン講演に参加します！ 小売未来 Days 2026は、「未来のヒントが集まる、小売・流通業界の最前線を学ぶ」をテーマに開催する、 業界最大級のイベントです。業界のトップランナーや実務家、専門家が登壇し、小売・流通業界の多様な課題に対し、DX、リテールメディア、EC・... Show MoreQlik は、ブースおよびオンライン講演に参加します！
小売未来 Days 2026は、「未来のヒントが集まる、小売・流通業界の最前線を学ぶ」をテーマに開催する、 業界最大級のイベントです。業界のトップランナーや実務家、専門家が登壇し、小売・流通業界の多様な課題に対し、DX、リテールメディア、EC・OMO、 物流改革、需要予測、AI活用など、未来の成⻑に欠かせないテーマを網羅的に解説します。
今すぐ申し込む8/25（火）ブース出展 11:00 - 18:30
大崎ブライトコアホール（東京都品川区北品川5丁目5-1 大崎ブライトコア3F）
9/11（金）オンライン講演 14:20 - 14:40
サッポログループのデータ基盤構築とQlik採用の背景 -
Closing the Data and AI Skills Gap: How Academia and Industry Can Prepare Tomorr...
Universities play a critical role in developing these capabilities, but they should not have to do it alone. Meaningful collaboration between academia... Show MoreUniversities play a critical role in developing these capabilities, but they should not have to do it alone. Meaningful collaboration between academia and industry can help educators connect curricula with workplace needs while giving students practical experience before they begin their careers.
This is the purpose of the Qlik Academic Program.
The program provides qualified university educators and students with free access to Qlik technology and learning resources. Members can develop experience with Qlik Cloud Analytics, Qlik Predict, Qlik Answers and Qlik Talend Cloud, exploring the complete data journey—from integrating and preparing trusted data to visualization, predictive analytics and AI-powered answers. They can also access self-paced learning pathways, ready-to-use teaching materials and Qlik product qualifications that help them demonstrate their knowledge.
Access, however, is only the starting point. The greatest impact happens when these resources become part of meaningful, hands-on learning.
Across EMEA, we have seen the results of this approach. At the University of Worcester, a two-day bootcamp gave students the opportunity to build practical analytics skills with support from Qlik experts. Every participating student passed the Qlik Business Analyst Qualification. At AGH University of Science and Technology in Poland, an advanced workshop introduced master’s students to predictive analytics, AI and practical business applications. Further collaborations across Germany, Portugal, Spain and other markets are helping educators bring current industry technology and real-world problem-solving into their classrooms.
Our Educator Ambassador community strengthens this work further. These educators champion data literacy within their institutions, exchange effective teaching practices and help us understand how academic needs are evolving. Their experience ensures that the program remains grounded not only in technology, but also in the realities of teaching and learning.
None of this happens through software alone. Educators contribute subject expertise and an understanding of their students. Qlik colleagues and volunteers share technical knowledge and industry experience. Students contribute curiosity, creativity and new perspectives. Together, they create learning environments in which data becomes something students actively explore rather than simply study.
For Qlik, this makes the Academic Program much more than a free technology initiative. It is a long-term investment in the global data and AI ecosystem. Students who graduate with practical experience, stronger data literacy and Qlik qualifications enter the workforce better prepared to create value with data. They may become future analysts, consultants, customers, partners, educators or innovators—and their capabilities will benefit the organizations and communities they join.
Preparing tomorrow’s workforce is a shared responsibility. By connecting accessible technology, relevant learning resources, committed educators and industry expertise, we can help students move confidently from classroom knowledge to career-ready capability.
The Qlik Academic Program is proud to support that journey.
Are you a university educator or student? Discover the Qlik Academic Program and start your journey today!
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Qlik Digest is back! Read the Analytics Issue here.
Make Qlik work harder for you Qlik Digest is back. Every month we bring you the insights, product knowledge, and real-world stories that keep you o... Show More
Make Qlik work harder for you
Qlik Digest is back. Every month we bring you the insights, product knowledge, and real-world stories that keep you one step ahead, because staying current shouldn't feel like hard work.
In this issue
Customer Spotlight - Goodbye firefighting. One manufacturer now spots problems before they start. Now Live - Introducing agentic AI that's built for the builders. Insider Tips - Need for speed? Five app tune-ups. Tune In - AI pilots work. Production does not. Qlik and AWS break down why.Customer SpotlightHow NKT went from reactive firefighting to predicting production problems before they hit
Millions SavedPer YearWeeks to SecondsReporting Time30 MinsPrediction ModelFind out how NKT uses Qlik to predict staff absences, cut reporting from weeks to seconds, and give customers straight answers on delivery.
See How NKT Gets Answers in Seconds →Now LiveIntroducing agentic AI that's built for the builders.
Qlik Cloud® now includes AI agents built specifically for data engineering teams. See what's new and how to put it to work.
Watch the 30-Minute Webinar On-Demand →Insider TipsFive ways to keep your Qlik Cloud apps running fast
Slow apps erode trust. This member article from Qlik Community walks through five foundational best practices to keep your Qlik applications fast, scalable, and responsive.
Fix Slow Apps →
Could your business act within 48 hours?
Demand for one running shoe spikes, 48 hours to respond , one trusted data product means the business is ready before the moment arrives.
Watch and See How It's Done →Tune In -
Qlik Digest is back! Read the Data Issue here.
Make Qlik work harder for you Qlik Digest is back. Every month we bring you the insights, product knowledge, and real-world stories that keep you o... Show More
Make Qlik work harder for you
Qlik Digest is back. Every month we bring you the insights, product knowledge, and real-world stories that keep you one step ahead, because staying current shouldn't feel like hard work.
In this issue
Think Ahead - Whitepaper: What agentic AI means for data engineers. Customer Spotlight - 18 systems, zero integration failures. How MillerKnoll did it. Now Live - Agentic AI that's built for the builders. Tune In - Why enterprise AI stalls in production. Qlik and AWS go live.Think AheadThe Evolution of the Data Engineer
Rethinking pipelines, data operations, and data quality for the Agentic AI era
AI initiatives demand ever more context, trust, and resilience from your data. This whitepaper shows what agentic data engineering means for you.
Read the Whitepaper →Customer SpotlightHow MillerKnoll eliminated data integration failures and what it means for your pipelines
ZeroDowntime80%Support Tickets Eliminated5 Days to <2ShippingMillerKnoll went from 80% of support tickets being integration failures across 18 ERP systems to launching a full order fulfilment platform in six weeks with Qlik Talend.
See How MillerKnoll Eliminated Pipeline Failures →Now LiveIntroducing agentic AI that's built for the builders.
Qlik Talend Cloud® now includes AI agents built specifically for data engineering teams. Watch the webinar to see what's new and how to put it to work.
Watch On-Demand →Insider TipsCould your business act within 48 hours?
Demand for one running shoe spikes, 48 hours to respond, one trusted data product means the business is ready before the moment arrives.
Watch and See How It's Done →
Build an open lakehouse on Iceberg – without the lock-in
Proprietary warehouse formats make switching expensive. This Techspert Talks session shows how to build an open lakehouse on Apache Iceberg with Qlik Open Lakehouse, no lock-in.
Watch Now →Tune In -
【オンデマンド配信】Qlik 基調講演とテクノロジー講演をプレイバック！ - AI Reality Tour Tokyo 2026 ダイジェスト
多くの方にご参加いただき、盛況のうちに終了した「AI Reality Tour Tokyo 2026]では、Qlik による基調講演やテクノロジー講演、Qlik のユーザーが語る先進的な事例、Qlik のパートナー企業による最新のソリューションなど、企業の AI 戦略を成功に導く最新情報をご紹介し... Show More多くの方にご参加いただき、盛況のうちに終了した「AI Reality Tour Tokyo 2026]では、Qlik による基調講演やテクノロジー講演、Qlik のユーザーが語る先進的な事例、Qlik のパートナー企業による最新のソリューションなど、企業の AI 戦略を成功に導く最新情報をご紹介しました。
当日の Qlik の基調講演とテクノロジー講演をオンデマンドでご視聴いただけます。予測 AI / 生成 AI / エージェンティック AI を統合した Qlik の最新製品をご紹介します。
※本 Web セミナーは、2026年 6月 10日（水）開催の「AI Reality Tour Tokyo 2026」 における Qlik 講演を録画したものです。
※視聴無料。パソコン・タブレット・スマートフォンで、どこからでもご視聴いただけます。講演概要 Qlik 基調講演（同時通訳）
エンタープライズ AI の現実 / 製品デモ：実証から実行へQlik が描く AI とデータ活用のビジョン、信頼できるデータ基盤によってAI の精度・連携性・拡張性を高める取り組みを解説します。AI アーキテクチャの中核として、エージェンティック AI や意思決定を支えるデータ基盤の役割と最新イノベーションを紹介します。 テクノロジー講演 - データ分析 生成 AI の進化に伴い、Qlik が目指す 分析 と AI の全体像を紹介するとともに、信頼できるデータ基盤やAIによる意思決定支援の考え方を解説します。さらに、Qlik Answers® や Qlik MCP Server のアーキテクチャと実装、生成 AI との安全な連携をデモを交えてご紹介します。 テクノロジー講演 - データ統合 AI 活用を支える信頼性の高いデータ基盤の構築手法を解説します。Qlik Talend Cloud® による AI 活用型データパイプライン、自動最適化や多様なデータ連携、データ品質向上、Iceberg を活用したデータ共有まで、AI-ready なデータ基盤の実現方法をデモを交えてご紹介します。 -
【オンデマンド配信】6 週間のラーニング Web セミナーシリーズ：Learning Voyage
Qlik のエキスパートが、データ / 分析 / AI に関するトピックを徹底解説！ 本 Web セミナーシリーズは、最新のデータ活用と分析の知識を身につけられるオンデマンドの Web セミナーです。6 週間にわたり、毎週 1 本ずつ新しい Web セミナーを公開していきます。 Qlik の年次グ... Show MoreQlik のエキスパートが、データ / 分析 / AI に関するトピックを徹底解説！
本 Web セミナーシリーズは、最新のデータ活用と分析の知識を身につけられるオンデマンドの Web セミナーです。6 週間にわたり、毎週 1 本ずつ新しい Web セミナーを公開していきます。
Qlik の年次グローバルカンファレンス「Qlik Connect」において、特に人気を集めたプレゼンテーションを厳選。データ主導文化 / Qlik Answers / 予測 AI / MCP / ガバナンス / 最先端のデータパイプラインなど、インサイトを行動につなげる実践的なアイデアや事例を徹底解説します。
※本 Web セミナーは、AI による日本語字幕付きでお届けします。
今すぐ視聴する- イノベーションの核心へ
バズワードの裏にある真の価値を探ります。Iceberg ベースのオープンレイクハウス、エージェンティックAI など、実際のユースケースから真の価値を明らかにします。 - Qlik のデータエキスパートが徹底解説
豊富な経験を有する Qlik のデータエキスパートが、難解な概念をすぐに実践できる具体的なガイダンスに落とし込みます。イノベーションを加速し、ビジネスに目に見える成果をもたらすための道筋を、わかりやすく解説します。 - 興味のある Web セミナーをオンデマンドで
データ分析を深く掘り下げる、データ統合と品質管理の成功例を幅広く学ぶなど、興味のある Web セミナーを選んで、好きな時にオンデマンドで繰り返し視聴することができます。
トピック
1 週目
データ主導型企業の 7 つの習慣
データ統合
データ分析
2 週目
AI 時代の DataOps：AI を活用した Qlik Talend Cloud® パイプライン構築のコード化
今すぐ始めるエージェンティック AI：Qlik Answers® のエージェントで生産性を最適化
3 週目
Qlik Talend Cloud® の新機能と活用例
MCP サーバーで Qlik の機能を拡張
4 週目
Iceberg の活用例：Qlik Talend Cloud® でレイクハウスを構築
不測の事態に対応：Qlik Discovery Agent で重要な変化を瞬時に把握
5 週目
AI 搭載の自律型データ振り分けでリアルタイムエンタープライズを実現
予測 AI でデータ分析を加速
6 週目
エージェントの未来：データ品質とガバナンスの自律化、分析 / AI 向けの ELT を容易に実現
AI 主導型企業向けの信頼できるデータ基盤の構築
※ 視聴無料。パソコン・タブレット・スマートフォンで、どこからでもごご視聴いただけます。
※ AI による日本語字幕付きでお届けします。 - イノベーションの核心へ
-
Qlik Reporting Service metering update for Word and PowerPoint beginning August ...
At the beginning of 2026, the Qlik Reporting Service expanded its offering with support for Microsoft Word and Microsoft PowerPoint report capabilitie... Show MoreAt the beginning of 2026, the Qlik Reporting Service expanded its offering with support for Microsoft Word and Microsoft PowerPoint report capabilities. For the duration of the first half of the year, these report formats have been executing in an unmetered state.
As of August 1st, 2026, these report formats will be metered. Customers using Word and PowerPoint reports will notice an increase in the report execution metrics.
What does this mean for me?
If you are a customer making use of these report formats, you should review your consumption and ensure that your Qlik Reporting Service quota is adequate for your aggregate report consumption at this time.
How do I know how many Word and PowerPoint reports are being sent?
- Talk to your report developers, they would have a good understanding of which report task are in production and how many reports are being executed
- For an aggregate view across your tenant, use the Report Analyzer app as per Qlik Cloud Monitoring Apps Workflow Guide
Thank you for choosing Qlik,
Qlik Support -
Upcoming deprecation of Qlik Analytics charts in May 2027
Qlik constantly refines its Analytics, over time replacing old charts with new, modernized alternatives. These deprecations are announced well in adva... Show MoreQlik constantly refines its Analytics, over time replacing old charts with new, modernized alternatives. These deprecations are announced well in advance and come with instructions on how to best replace these old charts, whether that is to use a new one, several new ones, or make use of new settings.
This blog post covers the deprecation of charts in May 2027 and offers you guidance on how to replace them.
What charts are being deprecated and when?
The following seven visualization bundle charts are up for deprecation in May 2027. Most have already been removed from the asset panel and are no longer a part of recent applications.
- The Bar and Area chart
- The old Bullet chart
- The old Heatmap chart
- The Button for navigation
- The Share button
- The Show/Hide container
- The old Tabbed container
- The Multi KPI
What do I use instead?
Since these are old charts, most are no longer in use. If you happen to still have a very old application and need to replace deprecated charts, see Visualization bundle > Deprecated charts for more information on what to use instead.
How do I find out if my apps are using the deprecated charts?
Qlik recommends reviewing your apps for old charts. Depending on your platform (Qlik Cloud or Client-managed), there are different methods you can deploy.
Qlik Cloud
Qlik Cloud administrators should use the Qlik Cloud Monitoring Apps to track the usage. The App Analyzer has a sheet dedicated to where deprecated charts are being used on a tenant in Qlik Cloud. The App Analyzer is based on usage events rather than scanning every app. Use the App Analyzer to find which apps and sheets have charts that need to be updated to newer and more modern alternatives. The easiest way to install and update the Qlik Cloud Monitoring Apps is to use the automation template. If you already have the App Analyzer, just remove the automation and install a new one to get the latest version of the App Analyzer.
Client-managed
For client-managed installations, use the Content Monitor app, included in every Qlik Sense Enterprise on Windows installation beginning with the May 2026 release.
The Content Monitor has a sheet for tracking deprecated charts. At reload, the Content Monitor app scans every app in the installation to list all applications and sheets that are using charts that are being deprecated. It also lists the installed extensions and their deprecation status.
If you want to track usage in prior versions, download the Qlik Sense Content Monitor from the Qlik download page. It comes bundled with the required Object Scanner.
Thank you for choosing Qlik,
Qlik Support -
What 1,000 job postings reveal about the data analyst career in 2026
Every year, thousands of students ask the same question: is data analytics still a good career bet? The 2026 numbers say yes. In Data Analyst Job Outl... Show MoreEvery year, thousands of students ask the same question: is data analytics still a good career bet? The 2026 numbers say yes. In Data Analyst Job Outlook 2026, 365 Data Science analyzed over 1,000 real job postings to map what employers actually want from data analysts today. The report adds a wider signal, citing a US Bureau of Labor Statistics projection of 23% job growth by 2032.
The research highlights four trends shaping the data analyst career in 2026:
- Specialists are winning. Nearly 70% of job postings focus on a core set of skills such as SQL, statistics, and visualization rather than long lists of requirements. The takeaway from the research: master the core analytics toolkit deeply before expanding into adjacent areas.
- Data visualization is the most in-demand skill. It appears in more postings than any other analysis skill, and dashboards remain the bridge between raw data and business decisions. Employers want analysts who can turn complex datasets into stories stakeholders understand.
- AI is transforming the role, not replacing it. Machine learning mentions in job postings doubled year over year, and the report cites that 70% of analysts say AI automation makes their work more effective. The analysts who thrive are the ones who understand how AI-driven insights are produced and how to question them.
- Communication is a technical skill now. Stakeholder communication appears in nearly 60% of postings, ahead of most hard skills. Analysis that no one understands is analysis that changes nothing.
Across all four, one theme repeats: the market rewards professionals who combine hands-on analytics skills with the ability to explain, question, and communicate data. That is data literacy, and it's exactly what the Qlik Academic Program helps universities build.
The program gives students free access to the full Qlik stack, from data integration and Qlik Cloud Analytics to Qlik Answers and agents, alongside courses, qualifications, and certifications. Students don't just read about visualization and AI-assisted analytics; they build dashboards, integrate real data, and learn how AI-driven decisions are made. Those are precisely the skills leading the 2026 hiring data.
Professors are supported too, with training and ready-to-use teaching resources that make it easier to bring analytics into the classroom, so curricula keep pace with a job market that is evolving faster than any syllabus revision cycle.
The report's conclusion is encouraging: the field isn't oversaturated for those with the right combination of skills. For students, the best time to start building that combination is before graduation, with real tools, real data, and credentials employers recognize.
By giving students, professors, and universities free access to analytics software, learning content, and certifications, the Qlik Academic Program helps learners graduate ready for a career field that the data shows is still growing.
Join our global community for free: Qlik Academic Program: Creating a Data-Literate World
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Qlik Talend Studio 2026-06 will move from Java 17 to Java 21
To all Talend customers, This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studi... Show MoreTo all Talend customers,
This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studio starting with the 2026-06 release.
This change only concerns the Talend Studio desktop application. It has no impact on the Jobs and Services you build with it, which will continue being compliant with Java 17 runtime environments (and Java 8 for Big Data Jobs).
You have multiple options to make a smooth transition:
- The Talend Studio embedded upgrade mechanism (Feature Manager) will offer handling the JDK version upgrade
- Should you prefer, a new installer of Talend Studio 2026-06 with JDK 21 embedded will also be provided
- You will also be free to manually configure Talend Studio to start with JDK 21, provided you are on Talend Studio 2025-09 or higher
Additional details:
Along with this change, the June release will come with many additions from a Java version perspective:
- Talend Studio will allow building Jobs and Services for Java 21 runtimes, in addition to Java 17 runtimes. Java 17 will remain the default setup for runtimes for the time being
- Remote Engines, Cloud Engines, Dynamic Engines, Job Server, and Talend Runtime will support running Jobs and Services in Java 21, in addition to the Java versions they already support
- Talend Administration Center (TAC), Data Preparation (client-managed), Data Stewardship (client-managed), Talend SAP RFC Server, Talend Semantic Dictionary, and Talend Identity and Access Management will also support running with Java 21 in addition to Java 17
For further questions, please start a chat with us to contact Qlik Support and subscribe to the Support Blog for future updates.
Thank you for choosing Qlik,
Qlik Support -
Shift Left: What DevOps can teach us about catching bad data
There’s a moment every data team knows. An executive questions a number in a dashboard. An AI model returns a recommendation that doesn’t make sense. ... Show MoreThere’s a moment every data team knows. An executive questions a number in a dashboard. An AI model returns a recommendation that doesn’t make sense. A downstream report shows a discrepancy no one can explain. The root cause turns out to be a data quality issue that has been sitting in the pipeline for weeks.
The frustrating part is that the issue rarely originated where it was found. It perhaps started upstream, from an incorrect transformation, a schema change that wasn’t handled, or a source system that began emitting invalid values two sprints ago. Every unidentified data issue compounds downstream, driving rework, slowing decisions, and eroding trust in analytics and AI. Gartner research estimates that the cost of poor data quality averages $12.9 million per organization annually.
By the time an AI app or a business user surfaces the problem, it has aged, compounded, and become harder to trace. There is a better containment model, and software engineering figured it out years ago.
Borrowing from DevOps: shift quality and governance left
In software development, “shifting left” means moving quality and governance checks earlier in the lifecycle. Barry Boehm’s research quantified why it matters for code development. A defect caught during development costs the least to fix, a defect found during testing costs considerably more, and a defect that reaches production costs the most by far. The multiplier compounds at every stage you move downstream, because context degrades, blast radius widens, and the fix becomes harder to isolate. Software engineering recognized the problem years ago, internalized it and reorganized the whole discipline around it. This gave rise to DevOps Research and Assessment (DORA) framework for continuously improving software delivery.
Furthermore, the DORA concept can be applied to other domains beyond just software engineering. Indeed, we can apply it to data engineering as well, specifically data quality. Thinking through the lens of DORA metrics, KPIs such as deployment frequency, lead time for changes, change failure rate, and mean time to recovery can be mapped to equivalents within data quality engineering.
DORA Metric
Software Engineering
Data Quality Equivalent
Deployment Frequency
How often code is deployed
How often quality rules and controls are deployed into live pipelines
Lead Time for Changes
Time from commit to production
Time from identifying a DQ requirement to enforcing the rule in production
Change Failure Rate
% of deployments causing incidents
% of pipeline or schema changes that introduce data defects
Mean Time to Recovery
Time to restore service
Time to detect, remediate, and validate a data quality issue
Just like code, data follows the same curve. A quality issue caught at ingestion costs a fraction of what it costs when it surfaces in a dashboard, a regulatory report, or an AI model.
What shifting quality left means for data engineers
Shifting quality left has a specific meaning for data engineers. Validation logic runs at the point where data is created and transformed, not the point where it is consumed. When a new source is onboarded, completeness and schema checks run immediately. When a transformation is written, it carries assertions about what the output should look like. When a pipeline runs, anomaly detection fires in real time, not three weeks later when someone notices a metric looks off. This repositions the data engineer from pipeline builder to quality-accountable producer.
The data engineering role itself is changing in the process. Hand-crafting rule expressions and wiring checks into every job used to fill the day. Qlik’s Agentic Data Engineering capabilities, now generally available in Qlik Talend Cloud changes what that work looks like. With the Data Quality Agent, an engineer describes what good data looks like in plain language; the agent finds the target dataset, checks existing fields and rules to avoid redundancy, generates the rule expressions, and lets the engineer refine them before anything is written. The engineer directs and reviews; the agent does the mechanical work. Building pipelines and fixing data quality issues becomes a higher-leverage discipline allowing the quality coverage to widen well beyond the specialists who used to own it.
Observability is the key to practicality
Shifting quality left only works if you can see what is happening across your pipelines. This is where data observability comes in, and it is worth being precise because it is often conflated with monitoring.
Monitoring tells you that something failed. Observability tells you why, when it started, and where it originated. A monitoring check might flag that a table has unexpected nulls. An observability signal tells you that null values in a specific column rose 40% over the last two hours, that the spike correlates with a transformation updated yesterday, and that this pattern has appeared twice before in the past month. That is a signal you can act on.
Effective observability means instrumenting pipelines continuously for freshness (is data arriving when it should?), completeness (are expected volumes showing up?), schema consistency (are structure changes propagating correctly?), distribution drift (are values behaving as they historically have?), and rule conformance (are business-defined constraints holding?). These run as ongoing signals that are continuously woven into the operational fabric.
The Qlik Trust Score for AI condenses the trustworthiness of a dataset or data product into a single, intuitive measure. Because Trust Score is tracked over time, an engineer can compare it before and after the Data Quality Agent acts — seeing exactly which signals moved the score and by how much. The evolution of Trust Score becomes the feedback loop: every new rule, every remediation, and every schema change shows up as a measurable shift, so the impact of each intervention is visible rather than inferred. When you combine that visibility with earlier enforcement, you get a compounding effect. Rules embedded in pipelines generate signals upstream. These signals surface issues while they are still close to the source. Resolution happens faster because the context is fresh. Over time, you build a map of where your data is fragile and where it is reliable, and you address quality proactively rather than reactively.
Ownership shifts as quality and governance become an engineering discipline
The technical model only works if the organizational model supports it. Traditional data quality programs concentrate ownership in a small governance team operating at a remove from the engineering work. That team defines policy. Engineers implement pipelines. Governance audits outputs. The feedback loop is long, and by the time an issue surfaces through that channel, it has already caused downstream harm.
The old stereotype was governance as the data police: no, you can’t use that field, please attend next month’s council. Even well-run enablement teams operated from the outside, persuading and reminding everyone else to comply. That does not scale. Quality and governance that survives AI shows up where the work happens, in pull requests, in CI/CD, and in the pipelines themselves.
A left-shifted model distributes accountability differently. Data engineers own quality at the point of production. Domain teams are responsible for the data they generate. Business stewards contribute the contextual judgment automated systems cannot provide, because they know what “correct” means for their domain and can tell a real problem from expected variance.
This is a change in how quality work is structured and who participates, not a reorganization. Automated checks handle the mechanical layer. Human judgment handles the interpretation layer. Two more of the new agents in Qlik Talend Cloud make that split workable. Data products are how the gap between data producers and data consumers finally closes — producers publish a governed, contract-bound asset; consumers get something discoverable, documented, and safe to trust. The Data Product Agent manages the full lifecycle of a data product through conversation, with pre-activation validation, dependency awareness, and explicit confirmation before anything is created, deactivated, or deleted. Producers evolve the product without breaking downstream consumers, and governance is enforced at every step without adding process overhead, yet allowing human judgment to be in the loop at each decision point. The Catalog and Business Glossary Agent then makes those products understandable in business terms. It derives glossary terms from business domain context, data product structure, field metadata, and app measures and dimensions, and links each term to the assets it describes. Documentation stays in sync with the data instead of rotting the moment someone renames a column, and other agents inherit correct, governed metadata to reason over.
Where to start
The Data Quality, Catalog and Business Glossary, and Data Product Agents, along with Declarative Pipelines, are all generally available today in Qlik Talend Cloud. Clive Bearman walks through each one, with demos, in the full launch announcement: From Intent to Trusted Data: Agentic Data Engineering is Now Generally Available. If you are ready to move quality upstream, start here:
🔗 Release notes: here
🔗 Want a demo, join the session : here -
Watch! Q&A with Qlik: New to Qlik Cloud
Don't miss our previous Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience. WA... Show MoreDon't miss our previous Q&A with Qlik! Pull up a chair and chat with our panel of experts to help you get the most out of your Qlik experience.
