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Hi everyone,
We are experiencing an OAuth 2.0 authentication issue when trying to connect Qlik Cloud Analytics to Salesforce using the Salesforce connector from Add Data.
A new Salesforce External Client App was created following Salesforce's recent security changes.
According to Salesforce's updated security requirements, PKCE is mandatory for newly created applications, and the option to disable it is locked.
However, the Qlik documentation instructs us to disable PKCE as part of the connection setup.
We attempted to configure the Salesforce Callback URL using both:
https://connector.qlik.com/auth/oauth/v2.htm
https://connector.qlik.com/auth/oauth/v3.htm
Both attempts failed during authentication, resulting in the following errors:
redirect_uri_mismatch
OAUTH_APPROVAL_ERROR_GENERIC
We would appreciate hearing from anyone who has encountered a similar issue or has successfully established a Salesforce OAuth 2.0 connection in Qlik Cloud Analytics under the new PKCE requirements.
Thanks in advance for sharing your experience!
Hello team,
I am looking to automate our consumption report export and would like to know if the following workflow is possible:
- Generate and save CSV files to Azure Blob Storage using Qlik Application Automation
- Ingest the data into Snowflake and visualize it in Qlik Sense or Excel
Could you please confirm if this process is feasible? If so, any documentation or guides would be greatly appreciated.
Best Regards.
Hi ,
I configured a notification in Talend Administration Center (TAC) with the category Tasks and the event On task failed.
The notification email is received correctly when Include log files is disabled. However, when I enable this option, the email is no longer received.
In the configuration shown in the attached screenshot, Compress in a single file is also enabled.
What could cause this behavior? Are there any additional settings or permissions required to attach the execution logs? Which TAC logs should I check to investigate the issue?
Or how can I include the task’s error message in the notification email?
Thanks,
You've already invested in your data, analytics, and business logic. How can you get more out of that investment as you explore agentic AI?
As organizations explore agentic AI, one thing is becoming clear: AI needs more than access to accurate data. It also needs the business context to understand what that data means.
Throughout this blog series, we've looked at how your Qlik investment can help you put agentic AI to work. You can ask questions about your data, bring Qlik intelligence into the AI assistants you already use, and have agents watch for meaningful changes and help look ahead.
But behind these experiences is an important foundation: trusted data that can be reused, along with the business meaning that makes it useful. That's where Qlik Data Products and semantic models come together.
Qlik Data Products organize data into governed, reusable assets that support different analytics and AI use cases.
They make data easier to discover and reuse, with information that helps you understand its origin, ownership, and governance. And you don't have to start over to create one. You can build on the trusted data you already work with in Qlik, as well as data in platforms such as Snowflake and Databricks.
Instead of preparing the same data for every new use case, you can make trusted data easier to find, access, and reuse.
But making data reusable is only part of the story.
Trusted data tells you what happened. Business context helps you understand what it means.
Consider a simple question:
“Why did margin fall in the Northeast last month?”
Two applications might use the same accurate data but return different answers because they calculate margin differently. One might include discounts or shipping costs, while another doesn't.
Now imagine AI agents using that data across different business processes. Without consistent definitions, those differences can lead to conflicting answers and decisions.
That's why we've introduced Semantic Models in Qlik Data Products.
Semantic models let you add business meaning directly to your Qlik Data Product. You can define measures, dimensions and relationships, and connect business terms such as “sales,” “margin,” and “customer” directly to your organization's business glossary. This means your data products can provide more than trusted data. They can also carry the context needed to understand and interpret that data consistently.
For your teams, that can mean less time reconciling different definitions, more confidence in the metrics they use, and a clearer starting point for building new analytics and AI experiences.
Data products make trusted data reusable. Semantic models make the business meaning behind that data reusable, too.
Together, they help you extend the value of your data beyond individual applications and use cases.
And you don't have to recreate all your existing business logic from scratch. With the new public preview capability, you can extract a data product directly from an existing application. The resulting data product includes the application's data and a semantic model of its relationships, dimensions, and measures. Read the public preview post to learn more on how to enable the capability and get started.
VIDEO: https://videos.qlik.com/watch/FZD6LqhLT77sgWeLbKzh7P
As AI becomes more embedded in everyday business processes, trusted data and consistent business meaning become even more important.
Helps you ask questions about your data.
Makes Qlik data and intelligence available to AI assistants and agents.
Can watch for meaningful changes.
Helps you look ahead.
Each serves a different use case, but all depend on trusted data. Data Products are designed to work across these experiences, so you prepare the data once and build from there.
Qlik Data Products and semantic models extend into Qlik Answers and AI agents connecting through Qlik MCP Server, so the shared business definitions and context you build in a data product reach your AI workflows to interpret your data more consistently. You'll also be able to use AI to help build and maintain your data products, semantic models, and business glossary as your data evolves.
Create your first Data Product. Start with data you already trust, then use a semantic model to define what it means to your business. From there, explore how you can put it to work with an AI agent.
Have an existing analytics application? Try the new extraction capability in Public Preview to build on the data and logic you already have.
Hello everyone,
my department and I are currently bulding a new BI-Department.
At the moment I'm loading the TABLES from SF into Qlik. But I also have usecases where I have to concatenate several Tables.
One Engineer pointed out to my, why we're not using the views. So I tried to find usecased where people are using views and where the tables.
Unfortunately we have nobody in our team who is experienced in this kind of topic.
Is here anyone who can help me?
Hi everyone,
I just published a new Data Voyagers video on Qlik Code, the Visual Studio Code extension that lets you edit Qlik Cloud load scripts in VS Code instead of the Data load editor.
I wanted to cover the whole thing in one place, so the video goes through each feature in order. Once you sign in to your tenant, you can browse spaces, apps, and scripts, then view, edit, and sync load scripts using direct or local editing. The editor supports the Qlik script language with syntax highlighting, completions, hover help, and diagnostics. You can also insert script content to load files from the data catalog or a data connection, or pull in existing script assets like QVS files. When you are ready to test, you can reload the app, preview your data, look at the data model, and open the app in Qlik Cloud from your browser.
A few limits from the documentation are worth knowing. Qlik Code is aimed at developers who already work in Qlik Cloud, and it does not replace the activity centers, so tasks like managing spaces, sheets, and app permissions stay there.
Watch the video here: https://youtu.be/8omcueKfuQ8
Documentation: https://help.qlik.com/en-US/cloud-services/Subsystems/Hub/Content/Sense_Hub/QlikCode/code-extension-home.htm
I would like to hear how this fits your workflow. Are you already writing scripts in VS Code, or still in the Data load editor? Which feature would you use first, and what is missing for you? Tell me in the comments.
Regards,
Mark Costa
Hi everyone,
When configuring a notification in Talend Administration Center (TAC), the Mails selection dialog displays users’ email addresses in the Login column, but the Email column is empty, as shown in the attached screenshot.
When creating a user in TAC, I enter the email address in the Login field. However, the user creation form does not show a separate Email field.
Is this expected behavior? Should TAC also display the login email address in the Email column, or is there another setting required to populate it?
How does TAC determine which email address to use when sending notifications to the selected users?
Thanks
Your analytics applications already hold years of business logic. Now, you can reuse that work by extracting a data product from an existing application, making it governed, reusable, and discoverable.
What it is
Extract data product from application creates a new data product from an analytics application you’ve already built. The data product includes both the application’s data and a semantic model of its relationships, dimensions, and measures.
Why try it?
The business logic you’ve already built can now do more. Extraction brings your application’s relationships, dimensions, and measures into a governed data product, giving them a shared, reusable home. This lets you use existing work, rather than recreate it manually.
Get started
Start with an existing application.
Your original application stays as it is, and you can keep using it.
To extract, the application must be in a personal or shared space, and you need the “Can edit data in applications” permission. Extraction runs a script that creates new datasets as data files in Qlik Cloud, so consider the size of your application first.
For the full steps, see Qlik Help | Extracting data products from analytics applications.
To learn more about semantic models, see Qlik Help | Semantic models in data products.
What to know before you try it
This capability is in Public Preview, and there are a few limitations to keep in mind:
How to enable the preview
Feature previews must first be enabled for your tenant, and your tenant admin needs to grant you access via the “Feature preview” permission. If you don’t see Feature previews in your profile, check with your tenant admin, as described in Managing feature preview for your tenant.
Once you have access, go to your “User profile > Feature previews” and activate the “Extract data product from application” preview feature. See Feature previews for details on how the program works.
Share your feedback
Use the in-product feedback button, either in the Feature previews dialog or when clicking on the “Preview” badge available in the “Extract to new data product” dialog.
We’re especially curious about:
I'm pretty new to Talend Studio and building pipelines with it and I've got a confusing situation.
I need to pull the three main fields from an HTTP Response (headers, status, body) and store them in a database (for audit purposes). However, I also need to dive into the body of the response (which will be Json), find a specific tag (a metadata tag), and capture two fields from this metadata tag. With both outputs, I then need to combine it into one and save an audit record to a database.
I know how to work with either the tJavaRow (to get the header, status, and body fields) and the tExtractJsonFields (to get the metadata). What I'm having an issue with is combining these two outputs back together and then saving it to the database. Every route to do this seems either overly complicated (trying to do some Java package imports and writing even more custom Java code) or is just flat-out not allowed by Talend Studio because you can't tUnion or even tMap from the 'same source' back together.
Is there a simpler way to do what I'm wanting? For example, my HTTP response might be:
{"headers": "someheaders", "status": 200, "body": { "meta": { "field1": "value1", "field2": "value2"}}}
And I need to capture it into my database like this
headers -> someheaders
status -> 200
field1 -> value1
field2 -> value2
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