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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.
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!
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.
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.
No action is required.
None.
The maintenance is scheduled for:
| Region | Maintenance Start | Maintenance End |
| Europe (Ireland) (eu-w-1) |
|
|
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
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 シアター B |
| 9/10(木)15:35-15:50 AI エージェントに Qlik のすべてを - MCP サーバーで広がる 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 |
|
9/11(金)14:35-14:50 クリックテック・ジャパン株式会社 |

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.

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.

HR and hiring managers

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.
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_zonebreakdowns=frequency_valueThis change is driven by Facebook. For details, see Ads Insights API -- Breakdown Availability Changes | developers.facebook.com.
Thank you for choosing Qlik,
Qlik Support
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 3 F)
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
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。 |
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。
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|
|
トピック |
|
|
1 週目 |
データ主導型企業の 7 つの習慣 |
|
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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 |
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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 |
At the beginning of 2026, the Qlik Reporting Service expanded its offering with support for Microsoft Word and Microsoft PowerPoint report capabilities. For the duration of the first half of the year, these report formats have been executing in an unmetered state.
As of August 1st, 2026, these report formats will be metered. Customers using Word and PowerPoint reports will notice an increase in the report execution metrics.
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.
Thank you for choosing Qlik,
Qlik Support
Qlik constantly refines its Analytics, over time replacing old charts with new, modernized alternatives. These deprecations are announced well in advance and come with instructions on how to best replace these old charts, whether that is to use a new one, several new ones, or make use of new settings.
This blog post covers the deprecation of charts in May 2027 and offers you guidance on how to replace them.
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.
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.
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 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.
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
Every year, thousands of students ask the same question: is data analytics still a good career bet? The 2026 numbers say yes. In Data Analyst Job Outlook 2026, 365 Data Science analyzed over 1,000 real job postings to map what employers actually want from data analysts today. The report adds a wider signal, citing a US Bureau of Labor Statistics projection of 23% job growth by 2032.
The research highlights four trends shaping the data analyst career in 2026:
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
To all Talend customers,
This is an advance notice that, to enhance both your security and experience, Java 21 will be required to launch Talend Studio starting with the 2026-06 release.
This change only concerns the Talend Studio desktop application. It has no impact on the Jobs and Services you build with it, which will continue being compliant with Java 17 runtime environments (and Java 8 for Big Data Jobs).
Along with this change, the June release will come with many additions from a Java version perspective:
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
In April this year, REVA University, Bangalore inaugurated a Centre of Excellence in Analytics and AI supported by the Qlik Academic Program. The inauguration was a successful event with dignitaries from the University and officials from ICT Academy present.
REVA University is ranked 6th among State Private Universities in Karnataka and 47th in India (Source reva.edu.in)
Following the inauguration, REVA signed up students from its departments including Computer Science Engineering, AIML, AIDS, CSSE, ISE, IOT and CSIT.
There was a good amount of coordination between all departments due to the establishment of the CoE which also ensured structured training for students. One of the key benefit of the CoE is to have a physical space for coordinated training of the Qlik Academic Program courses.
Students were able to complete the training and appear for the exams offered under the academic program portal in a time bound manner. Almost all students completed the Qlik Sense Business Analyst Qualification successfully and few of them also completed the Qlik Sense Data Architect Qualification and the Data Literacy Qualification. The total qualifications gained by students were more than 1000 which is a commendable achievement considering the limited time span.
The number of students getting "Qlik ready" is a big plus for the Qlik ecosystem including its customers and partners and also students will get an edge in the job market where Analytics and AI skills are lacking.
To know how you can make your students career ready with analytics and AI skills, visit qlik.com/academicprogram to know more and register to access world class training and qualifications.