The core object of Qlik Replicate is a Task, which is the instance of a table synchronization activity reading data from the Source endpoint and writing it to the target endpoint.

Depending on the use case, Qlik Replicate has multiple task types available. In this article, we provide you with an overview of the Task Types, the Options available in your tasks, and how to Migrate/Import tasks between environments. 

Content:

• Task Types
• Unidirectional
• Bidirectional
• Log Stream
• Task options
• Full load
• Apply Changes
• Store Changes
• Additional Information on Different Types of Tasks
• Task Setup
• Full Load vs CDC
• Log Stream tasks
• Bidirectional tasks
• Task Migration
• Backing up, Restoring, and moving Qlik Replicate
• Exporting, migrating, and importing tasks
• Step-by-Step Migration

Task Types

Unidirectional

The most commonly used type. It replicates data from Database A to Database B.

Bidirectional

Used when Database A also needs to be updated whenever there is any change in Database B. Consists of two separately created tasks.

Log Stream

Qlik Replicate will save the Data Changes from the Source database locally, and these changes can later be down streamed to multiple Targets in parallel, instead of having multiple tasks reading from the same source and writing to different targets.

Task options

Full load

Essentially a full copy of the Source database, Qlik Replicate will read all columns/rows from the Source DB and create the exact same copy on the Target DB (if there are no filters or transformations setup).

Apply Changes

The CDC part of Qlik Replicate. Having this enabled will make so that Qlik Replicate will read the transaction log from the Source and apply any changes (INSERT, UPDATE, DELETE) to the Target DB.

Store Changes

By default disabled, enabling this will make so that a Change or Audit table is created on the Target side and all changes are Inserted into that Table Before and After images.

A task needs at least one option enabled but it can have all 3 enabled (being a task that will Full Load, Capture Data Changes and Store Changes)

Additional Information on Different Types of Tasks

Task Setup

Before you get started with designing the features that you need for a task, you must first define the task's default behavior.

For general information on setting up tasks in Qlik Replicate, see Adding tasks | Qlik Replicate Help.

Full Load vs CDC

Qlik Replicate tasks can be configured to move data from a source DB to a Target DB. These tasks can be configured in a variety of ways to move data from the source to the target database.

For an in-depth comparison and introduction on the two, see An Introduction to Qlik Replicate Tasks: Full Load vs CDC | Official Qlik Support Article.

Log Stream tasks

Log Stream enables a dedicated Qlik Replicate task to save data changes from the transaction log of a single source database and apply them to multiple targets, thereby eliminating the overhead of reading the logs for each target separately.

For an in-depth explanation on how to use Log Stream, see Using the Log Stream | Qlik Replicate Help.

Bidirectional tasks

For information on how to set up bidirectional tasks, see Setting up Bidirectional replication | Qlik Replicate Help.

Task Migration

Backing up, Restoring, and moving Qlik Replicate

• Qlik Replicate Backup and Restore
• How to Move the Data Directory to a Different Replicate Server

Exporting, migrating, and importing tasks

• Migrating Task | Qlik Replicate Help 
• Backing up or Exporting Tasks from Qlik Replicate 

Step-by-Step Migration

1. Before you begin a migration, stop all tasks.
   
   Before stopping the Qlik Replicate services to perform a migration, it’s best to stop all running tasks. If there are Log Stream tasks running, the best practice would be to stop first all child tasks reading from the Log Stream staging folder and then stop the Parent Log Stream task.
   
   
2. Back up the ~/data folder.
   
   See Qlik Replicate Backup and Restore
   
   
3. Export the Qlik Replicate Repository (task/endpoints/server settings).
   
   See Backing up or Exporting Tasks from Qlik Replicate.
   
   
4. Import the Qlik Replicate repository to a test or UAT environment.
   
   See Importing tasks.
   
   
5. Verify all functionality in your test or UAT environment.
   
   
6. Import the Qlik Replicate repository to your Production environment. 
   
   See Importing tasks.
   
   
7. Resume/start tasks to verify all functionality.
   
   We recommend monitoring the tasks on the new server and comparing to old task logs to confirm all tasks were resumed where they should have resumed from.
   
   

Environment

Qlik Replicate 

Can tasks be exported from Qlik Replicate or is a full ~\data backup required?

You can export all or individual tasks with repctl exportrepository.

To export tasks using the command line:

1. On the Qlik Replicate host where you want to export the task, open the Start menu and expand Qlik Replicate, then select Qlik Replicate Command Line
   

   You can also open the Windows command-line console and change the directory to  <product_dir>\Attunity Replicate>\bin

2. Run the following command:

   repctl connect

3. To export tasks:
   
   For exporting all tasks, run: repctl exportrepository
   For exporting a specific task, run: repctl exportrepository task=”task:name”
   

   Modify either command if you use a custom data directory. Example: repctl -d <custom_data_directory> exportrepository task=task_name

4. By default, the repository will be exported to the Qlik Replicate directory /imports. In case you want it to be somewhere else, specify the path by adding: folder_name=path at the end of the command.
   
   Example: repctl -d <custom_data_directory> exportrepository task=task_name [folder_name=<path>]

Environment

Qlik Replicate 

The information in this article is provided as-is and to be used at own discretion. Depending on tool(s) used, customization(s), and/or other factors ongoing support on the solution below may not be provided by Qlik Support.

Related Content 

Importing Qlik Replicate Tasks/Repository

Qlik Replicate tasks can be configured to move data from a source DB to a Target DB. These tasks can be configured in a variety of ways to move data from the source to the target database.

This article aims to compare three popular task configurations and will provide additional details for the available Full Load settings. It is a supporting document to the Techspert discussion: Full Load.

Available configurations:

• FULL LOAD only
• CDC only
• FULL LOAD and CDC task

FULL LOAD only

FULL LOAD tasks are probably the simplest form of a Qlik Replicate task. With a Full Load only task, the data will be copied from the source tables to the target tables (Base Tables) and then the task will stop.

The target tables are basically a mirror image of the source tables with static data based on the last time the task was run.

The target tables are not kept in sync. This type of configuration is very similar to an old-style ETL type job, where the data is copied over one time when the task runs.

This Full Load type of task can be scheduled to run using replicates built-in scheduler or the Qlik replicate command line or rest API.

The task and target tables can utilize some features of Qlik replicate such as transformations and filters.

NOTE: A full load-only task can get around a limitation with LOB columns that are in a table with no primary key.
This limitation is for CDC tasks and the following is the CDC task notification you will get:

Column 'MyLob' was removed from table definition 'dbo.No_PK_LOB': the column data type is LOB and the table has no primary key or unique index

It is required that a table with LOBs has a primary key, due to the way the task will perform a lookup back to the source table to get the LOB value. As this lookup is only done during the CDC phase, it is not a limitation for a Full Load task, which will be able to read the entire LOB column when it is copying the source data to the target table.

Full Load settings screen:

CDC only

CDC only tasks will capture changes from the source and write them out to the target database. In this type of task, there are typically no (Base Tables) in the target.

The Apply changes setting is typically disabled and only the Store Changes setting is enabled. With this type of configuration, the task will read changes from the source and write out the records to the Qlik Replicate Store Changes table also known as __CT tables.

This type of task configuration is typically used when the data in the target __CT table is going to feed a downstream process. The structure of these __CT tables is usually based on the source table and will contain the fields from the source as well as any transformation fields. The big feature of the __CT table is that it also contains the various header information from the source such as commit time, Operation Indicator etc.

Store Changes settings screen:

FULL LOAD and CDC task

FULL LOAD and CDC tasks will typically contain the target (Base Tables) and they will populate those tables during the full load phase of a task.

Then the CDC phase of the task will capture every change to the source records and apply them to the target (Base Tables). This is a very typical configuration for a Qlik replicate task.

Note: this type of task can also have the store changes enabled (as described above in 2)

Conceptual vs. Actual task process order

Clarification of an often misunderstood task phase order related to the Full Load and CDC task:

When discussing Full Load and CDC combined in a single task, we think in terms of the Full Load phase starting first and then the CDC phase starting second to apply changes and keep the task in sync. While this is conceptually correct, the phases actually are reversed and in fact, the CDC phase is the one that starts first; capturing and caching changes.

Once the CDC phase starts, then the full load phase will start.

This order is needed in order to insure that any changes made to the table during the Full Load will be captured.

For example, if you happen to have a very large table that takes three hours to fully load and the CDC phase did not start until it was complete, you would miss those three hours of changes being applied to the table.

If you have ever seen a message in the log at the top of the log about consistency timeout this is why:

W: Transaction consistency timeout occurred. x transactions are still open

CDC - Apply changes setting screen:

Related Content

Transformation: Source - Filter for Delete
Filter for last 90 days of data in Qlik Replicate
Qlik Replicate Transaction Consistency Timeout occurred. xtransactions are still open
Qlik Replicate Full Load and CDC Split Task: Considerations

The information in this article is provided as-is and to be used at own discretion. Depending on tool(s) used, customization(s), and/or other factors ongoing support on the solution below may not be provided by Qlik Support.

Environment

Qlik Replicate 

Talend Studio periodically requires patch updates. In an air-gapped environment with no internet access, Talend Studio cannot reach the default update site (https://update.talend.com/Studio/8/updates/latest) to download and apply patches automatically.

This article describes how to download a Studio patch on a machine with internet access, then apply it locally by pointing Talend Studio's update settings to the downloaded patch file.

1. Download the required Talend Studio patch on a machine with internet access, and save it to a folder on the local machine, for example: D:/Talend_Patch/Patch_20260619_R2026-06_v1-8.0.1.zip 
   For information on how to download the patch, see How to download Qlik Talend Product patches.
2. Configure Talend Studio to use the local patch in Talend Studio:
   1. go to Window > Preferences > Talend > Update Settings.
   2. Change the Update URL field from the default update site https://update.talend.com/Studio/8/updates/latestto the local patch path, for example: jar:file:/D:/Talend_Patch/Patch_20260619_R2026-06_v1-8.0.1.zip!/
   3. Click Apply and Close.
3. Open Feature Manager in Talend Studio, and follow the prompts to complete the local patch installation

Environment

• Talend Studio

The IBM DB2 for iSeries source endpoint occasionally encounters an error during the CDC stage. This issue appears to be linked to the presence of the IBM i Access ODBC Driver versions 7.1.26 and 7.1.27.

The error message in the task log file:

[SOURCE_CAPTURE ]E: Error parsing [1020109] (db2i_endpoint_capture.c:652)

The issue specifically arises during the CDC stage; however, the Full Load stage operates smoothly without any complications.

Resolution:

Qlik has certified the DB2i ODBC driver version 07.01.029. An update to the official Qlik documentation is currently pending.

Either:

• Upgrade your IBM i Access ODBC client to 07.01.029, 
• or downgrade from versions 07.01.027/07.01.026 to 07.01.025

For compatibility reasons, it's advisable to revert to version '07.01.025' if you choose to downgrade, as '07.01.026' exhibits the same issue.

Various factors can contribute to encountering the 'Error parsing' message, including:
    • DB2i ODBC Version '07.01.027' (as described in this article)
    • In a single task, the total number of captured tables exceeds 300
    • The source table is created by DDS
    • Garbage data in table
    • Special characters in table object identifier (table name, or column name)
If you continue to encounter the error after switching to '07.01.025', please reach out to Qlik Support for further assistance.

Cause:

The behavior of the IBM DB2i ODBC Versions '07.01.026' & '07.01.027' differ slightly from that of '07.01.025'. In certain scenarios, it may return incorrect column lengths

Internal Investigation ID(s):

#00158029, #00160002, QB-26413

Environment:

• Qlik Replicate All versions
• IBM DB2i All versions
• IBM DB2i Client versions '07.01.026' and '07.01.027'

Qlik Replicate 

Your AI is only as good as what it can find. 

Part One of How to prepare your Qlik App for Qlik Answers laid out a six-step checklist:

• clarify field names,
• streamline the data model,
• check date formats,
• build master items with descriptions,
• configure vocabulary,
• and validate with real queries.

It ended with a simple line: you do not need a perfect data model, you need a clear one. This is what clear means underneath the surface. Keep the shortlist clean, and the model's fluency has good material to work with.

Part Two details what Qlik Answers is doing when it reads your apps and expands your understanding of why those six steps move the needle.

Content

• The mental model: retrieval rather than omniscience
• Lever 1: Visibility
• Lever 2: Names
• Lever 3: Master items
• Lever 4: Descriptions
• Lever 5: Vocabulary
• In Summary

The mental model: retrieval rather than omniscience

It is easy to assume the AI "sees" your entire app and simply needs to be clever enough to choose well. In practice it works a little differently, and for good reason. An app with 500 fields would present far more noise than signal to any model.

Instead, the system builds a search index of your app's metadata. Every visible field and master item gets a compact semantic profile, and that profile is what the system searches against.

For a master measure, that profile is built from several signals together:

• the name of the master item
• the description you have written for it
• the actual expression behind it
• any business glossary term you have linked to it

The system reads these as a whole and forms one understanding of what the asset means. When a question arrives, it retrieves the handful of assets that best match its meaning and passes them to the AI.

That single idea reframes the work. The useful question is rarely "is the AI clever enough to understand my field?" More often it is "will my field be found, and will it be the natural choice when it is?"

We'll tackle this in order, using five different levers. These levers act at different stages, which is why the sequence in which we outline them is important. Visibility decides what exists. Names influence what gets found first. Master items shape what the AI builds your numbers with and whether they carry your definitions. Descriptions help the right things get found at all. Vocabulary closes the last gap between your words and the model's understanding.

Working the later levers on an app still full of visible technical fields means refining assets that are competing with a great deal of noise. It tends to go more smoothly to start with the gate, then the definitions, then the language.

Lever 1: Visibility

The one truly decisive setting.

Hiding a field in your business logic does more than lower its priority. It takes the field entirely out of consideration. A hidden field is never profiled, never indexed, and never retrievable, so it cannot appear in an answer.

That is why visibility is worth looking at first. Every technical field left visible, every surrogate key, ETL timestamp, or FIELD_CODE_2_FINAL, competes with your real business fields. Working through a wide app and hiding the fields that should never be an answer is usually a few hours of work, sometimes less, and it improves every question asked afterwards. It is one of the most approachable, high-value things you can do to an app.

Hide with intent. If people do ask about a field, hiding it takes away their ability to get answers about it. Think of this as curation rather than cleanup.

Lever 2: Names

Names carry weight in retrieval.

When a question contains a term that closely matches an asset's name, that asset is favored strongly in the results.

So it pays to name assets the way your users actually speak. If everyone in the business says "Net Revenue", a master measure called "Net Revenue" tends to be found quickly and reliably. One called "NR_calc_v3" leaves the system to infer what you meant. It often will, and it is worth giving it a clear signal where you can do so easily.

Lever 3: Master items

Define your metrics once, with care.

Master items sit high on this list for a straightforward reason: when a well-defined master item and a raw field could both answer a question, the system leans toward the master item. It is your sanctioned, promoted answer.

The stronger argument, though, is trust rather than mechanics. The model does understand what "net sales" means in general terms. The question is whether you would prefer it to work from the general idea or from your definition.

Does your net sales exclude intercompany transfers? Is it net of returns or gross of them? Which currency, at which exchange rate, over which fiscal calendar? Answered from raw fields, each of those becomes a reasonable assumption. Answered through a master measure, it follows your business logic, your set analysis, and your exceptions, consistently. When the number reaches a report your leadership is reading, a defined metric gives everyone confidence in where it came from. Define the metric once, as a master item, and every answer built on it inherits the definition you stand behind.

It is worth building a master item for every metric that carries a real business definition, while keeping the library tidy. A collection of "Sales", "Sales 2", and "Sales New" tends to reintroduce the very ambiguity master items are meant to resolve, so clear naming here matters as much as the definitions themselves.

Lever 4: Descriptions

Think of them as configuration rather than documentation.

Descriptions are often treated as a nice-to-have for the data catalog. In an AI-ready app, they do more.

The description you write on a field or master item feeds directly into its semantic profile, which is what the system searches. A clear note on what "Net Sales" includes and excludes helps that item surface for the many ways people might phrase the question, and sets it apart from the other sales measures nearby.

There is no need to describe everything. The ones worth your time are the ambiguous ones: lookalike measures, fields whose names are abbreviations, anything a new analyst would have to ask a colleague about. If a person would want the explanation, the search index benefits from it too.

Lever 5: Vocabulary

Focus on your language, not on educating the model.

This is the lever people tend to overthink, so it is worth being clear about its purpose. You are not here to teach the AI general knowledge. There is a genuinely capable model underneath, and it already understands a great deal of the world.

It knows what revenue, profit, and margin are. It understands churn, retention, and attrition. It is comfortable with YoY, MoM, and QoQ, with fiscal quarters, gross versus net, currencies, countries and regions, standard units, and the everyday language of sales, finance, and operations. There is little value in a glossary entry explaining that "revenue" means money coming in.

Where vocabulary earns its keep is the language that is specific to you, which the model has no way to know:

• Your internal product codenames.
• An acronym only your company uses.
• The name your business gave to a segment it defined itself.
• A regional grouping that exists only on your org chart.
• If your business says "GMV" and the field is named "Gross_Merch_Value"

Those are examples the model cannot bridge on its own, and this is precisely where a vocabulary entry helps.

So a small, proprietary vocabulary tends to serve you best. A handful of terms that are truly yours will often do more than a long list restating things the model already knew. Vocabulary is a useful tool with a focused job: teaching the model your dialect rather than the dictionary.

Two items from that checklist do not appear as levers above: date formats and validation. Neither is a retrieval signal. A Date or Timestamp tag is a data type requirement your time-based questions depend on, not something the search index weighs when it ranks assets. Validating with real queries is not a lever at all. It is how you find out whether the five above actually worked.

In Summary

When people ask your app questions in plain language, the AI does not read your whole data model. It searches a curated index of your fields and master items, retrieves a small shortlist, and reasons over that. Most of what helps an app answer well comes down to making sure the right assets land on that shortlist. A sensible order to work through: hide what should never be an answer, name assets the way your users talk, define your real metrics as master items, describe the ambiguous ones, and add vocabulary for the terms that are genuinely yours.

Environment

• Qlik Cloud
• Qlik Answers

Changing the background color of the button object to none causes the button to display with its default background color (#006580). This is working as expected as of today.

Resolution

Qlik is reviewing QB-29806 for future improvements. 

Workaround

To achieve transparency (mimicking none as the background color):

1. In the button's background option, choose "By Expression"
2. Fill in the expression as follows:

   ARGB(0,0,0,0)

   

Cause

This feature predates the introduction of transparency in the colorpicker, meaning it was designed to revert to the default color when none is chosen. 

Internal Investigation ID(s)

QB-29806

Environment

• Qlik Sense Enterprise on Windows August 2023 and higher
• Qlik Cloud

Consulting (often referred to as Professional Services) needs to be contacted when either one of the following is true:

• There is a need for step-by-step deployment walk-through assistance.
• The assistance needed is of a "how-to", "development task", "performance baseline/benchmark", and/or of a "health-check" nature and could not be satisfied by Posting to the Qlik Community.
• The assistance needed is not of a break/fix nature, Feature Request, report of known or potential new product defect, or an inquiry in reference to information that can be found under published resources such as Knowledge Base, Release Notes, and Qlik Help.

To begin a Consulting services engagement, begin at Support and Services or contact your Account Manager.

Qlik NPrinting Designer crashes when formatting data labels in a Microsoft Excel chart.

Resolution

Resolving the crash requires enabling a feature (DisableOfficeEmbed=1) that allows the Qlik NPrinting Designer to work without embedding Office. With the option enabled, the Designer opens on the left side of the screen, while Office opens the remaining area.

Qlik NPrinting Designer features remain unchanged.

Changes to the configuration files may impact your product. Only make changes documented by Qlik Support.

1. Go to the %programdata%/NPrinting folder
   
   
2. Find the NPrinting.ini file and create a backup copy of it
   
   If the file does not exist, download the attached NPrinting.zip and extract the embedded NPrinting.ini. Then copy the downloaded NPrinting.ini into the %programdata%/NPrinting folder.
   
   You can skip step three; the downloaded file includes the DisableOfficeEmbed=1 flag.
    
3. Open your NPrinting.ini and add the DisableOfficeEmbed=1 feature flag.
   
   To disable the option again, set the value to 0.
   
   Example:
   

   [\Software\NPrinting\Options]
   DisableOfficeEmbed=1

4. Save and close the file
5. Restart your device

The Qlik NPrinting Designer and Server .ini files are overwritten when the product is upgraded or reinstalled. Please ensure you document any changes to this or other configuration files.

Cause

The crash is caused by a component used to embed the Office application into the Qlik NPrinting Designer.
 

Internal Investigation ID(s)

SUPPORT-11351

Environment

• Qlik NPrinting

Building an OSGI job in Talend Studio (version 2026-07 and 2026-08) fails with the following error:

ERROR] mvn <args> -rf :job_<JOB NAME>
at org.talend.repository.ui.wizards.exportjob.scriptsmanager.BuildJobManager.buildJob(BuildJobManager.java:305)

Resolution

Caused by defect QAPPINT-3008 (SUPPORT-11651) and resolved in the Talend Studio 2026-09 release. See the release notes for details.

Internal Investigation ID(s)

QAPPINT-3008, SUPPORT-11651

Environment

• Talend Studio 2026-07 and 2026-08

This article explains the steps to configure Kerberos with Qlik Sense Enterprise on Windows.

Note that the actual setup, implementation, and configuration of Kerberos for use with Qlik Sense Enterprise on Windows is the responsibility of the local IT administrator.

The following requires appropriate permissions in Active Directory to add Service Principal Names to the account running the Qlik Sense services.

A Service Principal Name may be registered using the following command:

setspn -A http/HOST serviceaccount

Where:

• HOST is the name of the server hosting Qlik Sense Enterprise on Windows
• serviceaccount is the account running the services

Once the SPN has been registered:

1. Open a command prompt with administrative privileges and type:
   

   setspn -U -S http/QlikServer1 COMPANYX\serviceAccount 
   setspn -U -S http/QlikServer1.companyx.local COMPANYX\serviceAccount

   Review the command for your respective Windows version.
   
   
2. Go to the Qlik Sense Management Console (QMC) > Proxy > Edit the proxy you wish to use
   
3. Tick the Enable Kerberos checkbox.

An SPN must be set for both the short hostname and FQDN for the target Qlik Sense server for Kerberos to work correctly. This is not related to URLs configured in the Web Client allowlist under the Virtual Proxy configuration.

For more information about Service Principal Names, see: Service principal names | learn.microsoft.com.

Important note for Monitoring Apps: If Kerberos is enabled on the proxy service, Windows authentication will fail as the REST connector does not support Kerberos. See Qlik Sense: Modify REST connections for Monitoring Apps to use JWT authentication.

This article is currently under review.

This article explains how to extract changes from a Change Store and store them in a QVD by using a load script in Qlik Analytics. 

The article also includes

• An app example with an incremental load script that will store new changes in a QVD
• Configuration instructions for the examples

Scenario

This example will create an analytics app for Vendor Reviews. The idea is that you, as a company, are working with multiple vendors. Once a quarter, you want to review these vendors.

The example is simplified, but it can be extended with additional data for real-world examples or for other “review” use cases like employee reviews, budget reviews, and so on.

The data model

The app’s data model is a single table “Vendors” that contains a Vendor ID, Vendor Name, and City:

Vendors:
Load * inline [
"Vendor ID","Vendor Name","City"
1,Dunder Mifflin,Ghent
2,Nuka Cola,Leuven
3,Octan, Brussels
4,Kitchen Table International,Antwerp
];

The Write Table

The Write Table contains two data model fields: Vendor ID and Vendor Name. They are both configured as primary keys to demonstrate how this can work for composite keys.

The Write Table is then extended with three editable columns:

• Quarter (Single select)
• Action required? (Single select)
• Comment (Manual user input)

Prerequisites

• A shared space
• A managed space (optional but advised for the tutorial)
• A connection to the Change-stores API to the Analytics REST connector in the shared space. A step-by-step guide on creating this connection is available in Extracting write table changes with the REST connector in Qlik Cloud.

Steps

1. Upload the attached .QVF file to a shared space
2. Open the private sheet Vendor Reviews
3. Click the Reload App (A) button and make sure data appears (B) in the top table
   

   
   

4. Go to Edit sheet (A) mode
5. Drag a Write Table Chart (B) on the top table, and choose the option Convert to: Write Table (C).
   
   This transforms the table into a Write Table with two data model columns Vendor ID and Vendor Name.
   
   
   

    

6. Go to the Data section in the Write Table’s Properties menu and add an editable column
   
   
   

    

7. This prompts you to define a primary key inside the table. Click Define (A) in the table and use both Vendor ID and Vendor Name as primary keys (B).
   
   You can also just use Vendor ID, but we want to show that this also supports composite primary keys.
   
   
   
   
   
   
8. Configure the editable column:
   
   
   • Title: Quarter
   • Show content: Single selection
   • Add options for Q1Y26 through Q4Y26.
     
     Tip! Also add an empty option by clicking the Add button without specifying a value.
     
     
     

      

9. Add another Editable column with the below configuration
   
   
   • Title: Action required
   • Type: Single select
   • Options:  Yes and No
     
     

     

10. Add another Editable column with the below configuration
    
    
    • Title: Review
    • Type: Single select
    • Options:  Yes and No
      
      
      
      
11. The Write Table is now set up.
    
    Go to the Write Table’s properties and locate the Change store (A) section. Copy the Change store ID (B).
    
    
    

     

12. Leave the Edit sheet mode. Then add changes for at least two records. Save those changes.
    
    
    

     

13. Go to the app’s load script editor and uncomment the second script section by first selecting all lines in the script section (CTRL+A or CMD+A) (A) and then clicking the comment button (B) in the toolbar.
    
    
    

     

14. Configure the settings in the CONFIGURATION part at the end of the load script
    
    
    

     

15. Update the load script with the IDs of the editable columns.
    
    The easiest solution to get these IDs is to test your connection. Make sure the connection URL is configured to use the /changes/tabular-views endpoint and uses the correct change store ID.
    
    
    

     

16. Copy and paste the example load script (for the editable columns only) and paste it in the app’s load script SQL Select statement that starts on line 159:
    
    
    

     

17. Replace the corresponding * symbols in the LOAD statement that starts on line 176:
    
    
    

     

18. Choose which records you want to track in your table by configuring the Exists Key on line 216.
    
    This key will be used to filter the “granularity” on which we want to store changes in the QVD and data model, as the load script will only load unique existing keys (line 235).
    
    
    • $(vExistsKeyFormula) is a pipe-separated list of the primary keys.
    • In this example, Quarter is added as an additional part of the exists key to keep track of changes by Quarter.
    • Optionally, this can be extended with createdBy and updatedAt to extend the granularity to every change made:
      
      
      

       

19. Reload the app and verify that the correct change store table is created in your data model. The second table in the sheet should also successfully show vendors and their reviews.

Environment

• Qlik Cloud Analytics

Databricks Unity Catalog Volumes do not consistently reflect the current state of a staging path to the very next Qlik Replicate or ODBC operation that depends on it. This occurs for either a new or not yet created path, as well as for newly created files.

Both of these scenarios trigger a FileNotFoundException/PATH_NOT_FOUND error from the Databricks backend, but not from Qlik Replicate itself.

Example:

LIST fails on staging subfolder during pre-load cleanup, even with the service account having all required permissions. 

LIST '/Volumes/…/qlik_volume/qlikstage1/<task-id>/1/'; → HiveSQLException: java.io.FileNotFoundException: No such file or directory ..

[TARGET_APPLY    ]E:  RetCode: SQL_ERROR  SqlState: 08S01 NativeError: 115 Message: [Simba][Hardy] (115) Connection failed with error: SSL_read: error code: 0 [1022502]  (ar_odbc_stmt.c:4730)

Resolution

This is caused by the Internal Parameter $info.query_syntax.error_code_connection_failure.

Remove the parameter to resolve the issue.  

Environment

• Qlik Replicate

This article explains how the Qlik Sense app button component can be used to send custom parameters directly to the automation without requiring a temporary bookmark. This can be useful when creating a writeback solution on a big app as creating and applying bookmarks could take a bit longer for big apps which adds delays to the solution. More information on the native writeback solution can be found here: How to build a native write back solution.

Contents

• Button configuration
• Automation configuration
• Limitations
• Related Content

Button configuration

1. In this example, we'll use an app that contains car sales opportunities from Salesforce. The straight table, the variable input dropdown, and the native button are important for this example.
   

   
   

2. Start editing the sheet and go to the button configuration section, set the action to "Execute automation" and specify the automation that should run when the button is clicked. If your automation isn't shown, you can copy and paste the automation id.
   
   
   
   
3. Additional input parameters can now be added through the "Add parameter" button.
   
   

   

4. Parameters require a key and a value, it's possible to use Qlik Expressions as input for the value. These expressions are evaluated when the button is clicked.
   
   
   
   
5. After adding the parameters, use the "Copy input block" button to copy the all parameters that will be sent to the automation to your clipboard.
   
   
   
   

Automation configuration

1. If you want anyone who can access the button to be able to run the automation, then set the automation's run mode to "Triggered".

   If you want to limit this to a specific group of users, you can leave the automation in Manual run mode and place it in a shared space that this group of users can access. More information about this is available here: Introducing Automation Sharing and Collaboration. Make sure to disable the Run mode: triggered option in the button configuration.

   
2. Right-click in the automation canvas and select "Paste Block(s)" to add the Inputs block to the automation. Connect the new Inputs block to the Start block. Apart from the default parameters, you'll also find your custom parameters as Inputs in the block.
   
   

   

3. You can now reference the custom parameters from the button in other blocks in the automation by selecting the output from the Inputs block.
   
   
   
   
   

    

Limitations

1. The maximum payload size when triggering an automation is 32kb. When using the GetCurrentSelections expression, every field value will be sent to the automation. This could exceed the 32kb limit. Use the temporary bookmark solution as described in this article instead: Native writeback solutions.
2. Other automation limitations are available here: Platform limitations.
3. More info on configuring the native button can be found here: Creating buttons.

Related Content

• Creating buttons
• Native writeback solutions

Environment

• Qlik Cloud
• Qlik Application Automation

The information in this article is provided as-is and will be used at your discretion. Depending on the tool(s) used, customization(s), and/or other factors, ongoing support on the solution below may not be provided by Qlik Support.

An automation may fail multiple times with the following error if a block requires a reload ID as an input:

code: HTTP-400
title: Invalid Request.
detail: Missing mandatory parameter "reload_id".

Resolution

Resolving the timeout requires us to confirm the Do Reload block's execution time. This is a multi-step process.

1. Go to History, then select Run, and Export This Run
2. Fetch the "timestamp_start" (start of execution) and "timestamp" (end of execution)
   
   Example of an exported run: 
   
   

   {
      "block": "DoReload",
      "index": 1,
      "in_count": 11,
      "timestamp_start": 1788341229516403,
      "timestamp": 1788341410632675,
      "memory_usage": 6665144,
      "out_count": 1,
      "out": "Timeout",
      "in": {
        "blendr_on_error": "stop",
        "datasource": "dc8a0970-08c0-11ec-af77-a15c8651f6af",
        "app_id": "d15180c1-caac-4aac-85af-7bf606694f22",
        "run_mode": "Wait for reload to complete",
        "timeout": 120,
        "partial": null,
        "weight": null,
        "variables": null,
        "throw_error_on_failed_reload": null,
        "blendr_snippet_guid": "b5dbb7a0-e715-11ea-b2bf-43bb35adee19",
        "blendr_connector_guid": "61a87510-c7a3-11ea-95da-0fb0c241e75c"
      }
    }​

   
   The values in our example are:
   
   "timestamp_start": 1788341229516403
   "timestamp": 1788341410632675
   
   
3. Run the values through a UNIX time converter to see how long the block takes to respond.
   
   
4. This is not yet enough to decide how long it takes the app to reload; we also need to match what we have in the automation with the app's reload history section. 
   
   Example: We see that the block failed with a timeout after 3 minutes, while the reload history instance of the app indicates the total time it took the app to reload is 5 minutes.

Based on this, we come to the following conclusion:

This is expected behaviour from the automation side: the load took 5 minutes, while the automation Do Reload was configured to wait on the load for 2 minutes. We receive back a timeout as a response, rather than the reload ID, triggering the error. 

From here on, it is necessary to resolve the timeout by reconfiguring the automation. Multiple options (or combinations of them) can be applied:

• Increase the timeout value for the Do Reload block
• Set the Do Reload block to proceed with the automation and report an error, such as by adding a Condition Block after the Do Reload block to check for a timeout before continuing the automation.
• Improve the underlying application reload performance by improving the application structure.

Cause

This failure occurs when a Do Reload block fails (and the reload_id isn't created) or fails with a timeout. This leads to the Do Reload block sending a timeout string as a response, rather than an object with a reload id. 

Example response for a failed (timeout) reload:

In this example, the Do Reload block is configured to wait for the reload to finish, with a 2-minute timeout if it does not complete within that time.

Setting a timeout on the Do Reload block does not prevent the app from reloading. The reload continues running in the background even after the block times out.

The Do Reload block only starts the app reload and waits for a response before the automation continues. If no response is received within the configured timeout value (2 minutes in this example), the block times out.

If an automation fails this way repeatedly, it usually means only that the app reload is taking longer than the value configured in the Do Reload block's timeout field. This does not indicate an issue with Qlik Automate.

An issue only exists if the app reload completes well within the configured timeout and the block still times out. For example, if the app reload normally takes 2 minutes, but the timeout field is set to 4 minutes, and the block still times out, further investigation is needed.

Environment

• Qlik Automate

This article explains how to connect to data from SAP OData services and retrieve data with the Qlik Cloud Analytics ODATA connector.

Requirements

• A valid SAP account to access the ODATA services in the SAP environment

  Public cloud SAP tenant only.

Configuration

1. Create a new application in your Qlik Sense workspace, and go to the Data Load editor
2. On the right panel, click New Connection and choose the ODATA connector
3. Configure the connection:
   1. Enter the URL of the SAP ODATA service and your credentials
   2. Test the connection, then click Save
      
      

How to retrieve and load data

1. Identify the entities (resources) of the ODATA service
   1. On the data connection you created, click the Data button
      
      
      
      
   2. On the left menu, choose ListResources (A) and enter $format=xml (B) in the OData Query Options, then choose application/atom+xml in Accept (C) and click Preview data (D)
      
      
      
      
      
      
   3. Retrieve the list of entities
      
      
   4. Check and click GetData, then enter the following parameters:
      • The name of the entity from which you want to retrieve data in OData Resource Path
      • $format=xml in OData Query Options
      • Application/atom+xml in Accept
        
        
        
        
   5. Click Preview data
      
      
   6. Click Insert script
      
      
      
      Example:
      
      
      
      
      
      
2. The next step is to curate the load statement and optionally provide business-friendly names for the SAP fields.
   
   Example:
   
   
   
   
3. Repeat all steps for each entity you want to retrieve data from. Then curate the load script to avoid synthetic keys and build a functional data model in your analytic application.

The example in this article is provided as is and only documents a basic option. Further customization can be achieved using a data architecture with QVD layers.

Optional: Instead of manually going through all the steps, it is possible to use an LLM and a skill on SAP and Qlik to generate a first curated version of the full load script by providing the LLM the metadata file of the OData service. It would save some time in the building process.

Environment

• Qlik Sense Enterprise on Windows

This article answers the most frequently asked questions about Qlik Discovery Agent. It is split into five sub-sections:

• General FAQ
• Availability and Integration
• How does Discovery Agent work?
• Technical FAQ
• Insight Generation Logic

If you are looking for information on how to get started, check out the Discovery Agent Interactive Walkthrough and our Discovery Agent Documentation.

General FAQ

What is Qlik Discovery Agent?

Discovery Agent is an AI-powered, always-on monitoring capability in Qlik Cloud that automatically detects meaningful changes, anomalies, and trends in your data. It requires no rules, thresholds, or manual setup. Discovery Agent identifies spikes, drops, trend shifts, baseline changes, and data quality issues, then delivers clear, plain-language insights in a prioritized feed.

What makes Discovery Agent different from traditional alerting?

Traditional BI alerts rely on predefined thresholds or manual logic. Discovery Agent uses the Qlik Analytics Engine and its associative capabilities to evaluate wide combinations of data relationships automatically and proactively surface only those insights that matter. It is context aware, adaptive, and far more scalable than rules driven systems.

Who is Discovery Agent for?

• Executives who want high-level visibility into what changed
• Business users who need easy-to-read insights without deep analytics skills
• Non-technical users who do not want to build dashboards or rules
• Analysts who want automated detection to replace repetitive KPI monitoring

Availability and Integration

Does Discovery Agent work with Qlik Cloud Analytics?

Yes. Discovery Agent is built directly into Qlik Cloud Analytics and leverages the Qlik Analytics Engine for associative, large scale anomaly detection.

A Premium or Enterprise subscription is required. See Qlik Pricing for details or contact your Qlik account representative. 

Does Discovery Agent integrate with Qlik Answers?

Yes. You can ask questions directly from an insight card, and context from the insight will be transferred into Qlik Answers.

Is Discovery Agent available on Qlik Sense Enterprise Client-Managed?

No. Discovery Agent is built exclusively for Qlik Cloud.

Does Discovery Agent impact app performance?

No. Monitoring runs outside active dashboards, ensuring no performance impact on live analytics experiences.

Does Discovery Agent follow Qlik Cloud security and governance rules?

Yes. Insight delivery respects user permissions, governed access, and security boundaries.

How does Discovery Agent work?

How does Discovery Agent detect insights?

Discovery Agent analyzes updated app data models using associative evaluation to identify:

• Spikes or dips
• Trend shifts
• New or emerging baselines
• Record highs and lows
• Values above or below expected patterns
• Missing or inconsistent data

No rules or thresholds are required.

Is Discovery Agent real-time?

Discovery Agent is always on, but processes changes when the application’s data model updates. Insights refresh after reload and appear in the feed once the system evaluates new data. Updated are currently capped at one reload per day.

How often does the feed refresh?

The feed automatically refreshes upon reload. For most apps, this occurs once per day or whenever new data is introduced.

Can I customize the insights I see?

Yes. You can follow specific apps or insight categories once the Following tab is released. Filtering options are also planned to help tailor results.

Technical FAQ

What are Insight Triggers?

Insight Triggers are structured metric definitions that serve as the foundation for generating analytical insights within the application. Each trigger is composed of a measure or expression,  such as a calculated field or KPI, along with a set of additional configuration parameters. These parameters include the frequency at which the trigger evaluates data and the type of calculation to be applied (example: sum, average, count).

Together, these elements define the conditions under which an insight is surfaced to the user.

Do I Have to Set a Date Period for Each Trigger?

Yes, a date period is required for every trigger you configure.

All insights generated by the system are trend-based, meaning they analyze data over time to identify patterns, changes, or anomalies. This requires a date period to be added to the trigger's associated group. Without a defined time range, the system cannot perform the temporal comparisons necessary to produce meaningful insights.

How Do I Refresh the Insight Feed?

The Insight Feed refreshes automatically each time the page is reloaded. No manual refresh action is required. The feed itself is regenerated once per day, and this regeneration is triggered by the introduction of new data into the application or applications that contain active triggers. As a result, the feed will always reflect the most recent data available as of the last daily reload cycle.

Can I Filter Individual Values in a Dimension?

Filtering functionality is available in the Feed. A Filter button is currently visible at the top of the feed during the preview phase of the application. Users can use this to find specific insights in the feed.

How Many Triggers Can I Create?

• Each application supports a maximum of 50 triggers. For each trigger that has been configured, the system will calculate and present up to 5 insights per reload cycle.
• A tenant-wide guardrail exists to help control the volume of insights in the feed, providing broad analytical coverage across your defined metrics and expressions.

Where Are Triggers Stored?

Triggers are stored directly within the application in which they are created. They are not stored externally or in a centralized repository. That means each application manages its own set of triggers independently, and triggers defined in one application will not carry over to or affect another application.

Can I Ask Questions Directly in the Feed?

Direct question-and-answer functionality within the feed is available.

The Insight Feed is integrated with Qlik Answers, enabling users to ask natural language questions without leaving the feed interface. Because each card displayed in the feed is tied to a specific application, context from the relevant card will be automatically transferred to Qlik Answers to ensure accurate, contextually appropriate responses.

Why Am I Seeing a Large Number of Old Insights?

This behavior is expected and occurs specifically after the first reload following the creation of new triggers.

During this initial reload, the system performs a comprehensive scan of all available historical data, rather than only the most recent data. This allows it to identify any and all qualifying insights across the full dataset. This is a one-time process. All subsequent reloads after this initial one will only evaluate and surface insights based on newly introduced data, so the volume of older insights will not continue to grow with each reload.

Does This Feature Require the Cross-Region Inference Toggle?

Yes. The Insight Feed and its associated trigger functionality require the cross-region inference toggle to be enabled. Please ensure this setting is activated in your environment before attempting to configure triggers or access the feed. If you are unsure how to enable the cross-region inference toggle, contact your system administrator or refer to the relevant configuration documentation.

How Do I Remove Insights from the Feed?

To remove specific insights from the Insight Feed, you must delete the trigger that is generating those insights. Because the feed is dynamically generated based on active triggers, removing a trigger will prevent its associated insights from appearing in future feed reloads.

Deleting a trigger is a permanent action.

If you wish to stop surfacing certain insights temporarily, consider whether disabling or modifying the trigger may be a more appropriate course of action, depending on your platform's available options.

Does This Feature Support Section Access?

Section Access is not currently supported for applications used with the Insight Feed.

Any application that has Section Access enabled is incompatible with this feature at this time. As a result, all users who have been granted access to a given application will be able to see the insights generated from that application's triggers, regardless of any Section Access restrictions that may otherwise apply within that application.

This is an important consideration when deciding which applications to configure with triggers, particularly for datasets that contain sensitive or role-restricted data. Support for Section Access may be introduced in a future release.

Insight Generation Logic

How many data points are required to generate an insight?

Below is the minimum data requirement:

Weekly/Monthly/Quarterly/Yearly aggregation

• Spike detection, record high/low, model deviation: 4–50 data points
• Trend/baseline change: 20–50 data points

Daily aggregation

• Spike detection, record high/low, model deviation: 7–365 data points
• Trend/baseline change: 20–365 data points

Why am I not getting any insights?

Missing dates in the date field may prevent calculations. Creating a master calendar in the
load script can resolve this. Qlik is exploring options for date imputation.

When the Qlik Sense Capability Service Logs takes up a huge amount of disk space you have the possibility to clean the logs and to prevent them from growing as big as they were again.

Resolution

You can clean out the folder using the methods described in How To Reduce Log Folder Size Over Time.

After the folder has been cleaned out there is the possibility to switch off the Capacity Service logging:

1. Open the services.conf in C:\Program Files\Qlik\Sense\ServiceDispatcher and add the line --log-level=off under the capabilityservice.parameters section.
   
   [capabilityservice.parameters]
   --port=${CapabilityPort}
   --file="${PWD}\..\CapabilityService\capabilities.json"
   --log-level=off
   
   

   The parameter can be one of the following:

   • info (default) 
   • warning
   • error
   • off
   
   
   
2. Save the services.conf
3. Restart the Qlik Sense Service Dispatcher Service

Cause

Logs in the folder C:\ProgramData\Qlik\Sense\Log\CapabilityService are not automatically archived in the Qlik Sense archived logs folder.

Environment

Qlik Sense Enterprise on Windows February 2019 and above

When only a Unique Index Key is defined in the source, the task will by default create a non-clustered key in the target.

This article documents how to create a Primary Key in the target by introducing an Internal Parameter at the SQL Server target. 

Resolution

Source: Oracle
Target: SQL Server

Add the following parameter:

• $info.query_syntax.create_index  
• Value: ALTER TABLE ${QO}${TABLE_OWNER}${QC}.${QO}${TABLE_NAME}${QC} ADD CONSTRAINT ${QO}PK_${TABLE_NAME}${QC} PRIMARY KEY ( ${COLUMN_LIST} ) 

Make sure columns with a unique index defined at the source, as well as columns that are marked as Key Manually in the UI, have the NOT NULL constraint enabled.

Environment

• Qlik Cloud
• Talend Cloud

QTAC-2537 R2026-08 patch introduced a label sorting issue in the Talend Job Conductor.

Sorting the label on the Execution Plan page in previous patches works as expected. The issue is specific to sorting label names in the Job Conductor, whereas sorting the label name in ascending or descending order fails. 

Resolution

This issue will be fixed in an upcoming Talend Administration Center patch. If a solution is required as soon as possible, another alternative is to revert to a previous patch, as this only impacts version QTAC-2537 (R2026-08).

Review the Talend Release Notes for details.

Internal Investigation ID(s)

QTAC-2707

Environment

• Talend Administration Center