Task Chaining with qlik-cli

Reloading data into Qlik applications is a necessary component of the BI lifecycle. In this tutorial, you are going to learn how to use the reload API to create tasks, loop to catch status changes in a reload, and how to think about setting up task chaining using a script.

  • Prerequisites
  • qlik-cli version 1.0.0 or greater - Text editor for manipulating PowerShell or Bash scripts
  • Reload using CLI basics

You can access reloads using qlik-cli through the qlik reload command. Running the command with the --help flag lists the different sub-commands and flags available.

  • Create a reload task

To use the create command, first obtain the app metadata of the application using the item command.

Note: Qlik Sense Apps in Qlik Cloud have an itemId and a resourceId . The resourceId is often referred to as the appId and is a GUID. The itemId is the unique ID of the app in the items collection.

Issue the create command referencing the resourceId property from the app metadata object.

The response returns all of the relevant information related to the newly created reload entity. If you add a -q to the end of the CLI command, only the ID for the reload task is returned. This can be useful when chaining multiple commands together.

  • Check reload status

Check the status of the reload you created by using the get command and passing the item ID. The item ID is the value in the response from the create command.

Because the reload API is RESTful, the commands in the CLI are request/response in nature. The system doesn’t notify you when the reload reaches a completed state. To evaluate the status of reloads, put the get command in a loop.

As long as the status of the reload isn’t SUCCEEDED or FAILED , the loop keeps running.

Task chaining example

The ability to execute a reload upon the completion status of a preceding reload is a desired capability in the Qlik platform. Task chaining, as this process is commonly called, needs to be able to accept an instruction set to know what to do based on the outcome of a previously run reload. qlik-cli isn’t opinionated with regard to the instruction set because there aren’t specific task chaining commands available in the tool. That said, functions like the loopReloadStatus example and a mechanism to facilitate the next step in an instruction set use the standard reload CLI commands, giving you the flexibility to design an instruction set that works for your requirements.

  • Make a task chain instruction set

In this example, the instruction set is a JSON array with simple logic. Each entry in the array has a name for the task, the name of the Qlik app to run the reload command, the array item (by number) to go to next upon a SUCCEEDED reload outcome, and the array item (by number) to go to next upon a FAILED outcome. A -1 value in the succeed or failed properties instructs the process to end.

  • Check reload success or failure

The Check reload status section covers the loopReloadStatus function that observes the reload task status before going to the next step.

  • Run the task chain

To run the task chain in a logical fashion, employ another function called runTaskChainItem that accepts two arguments: one passing the instruction set, the $taskObject and another passing the specific item to run, the $task .

The function gets the app name to reload and calls qlik item ls to retrieve the application properties.

With the app properties returned, run qlik reload create passing the resourceId from the app properties to start the reload task.

Once created, the loopReloadStatus function is run passing in the reload ID from the previous command. The function loops until a success or failure status prints in the response from the tenant.

The $reloadResult variable contains SUCCEEDED or FAILED for a value. The next command evaluates the value to return from a matching property name.

Based on the $result variable value, the function either runs the corresponding task item in the instruction set or finishes running.

Now that the functions and instruction set are written, call the runTaskChainItem function.

Reloading apps is useful to obtain the latest data for a Qlik app. But for large deployments, reloads are multi-layered and often generate datasets as part of a downstream app reload. That’s why chaining reloads together is so useful. Using qlik-cli, you can combine reloads and tasks with operational workflows from other applications to increase efficiency and automate insight to action.

  • Complete scripts

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Ptarmigan Labs

How to start on-premise Qlik Sense tasks from anywhere

Start Qlik Sense reload tasks from anywhere, using a single http call. Can be very useful when source systems need to trigger data reloads in Sense. The solution is low cost and can be made as secure as needed using Azure Event Grid and Function Apps.

Göran Sander

Göran Sander

I've encountered this challenge again and again over the years when helping companies with their Qlik Sense environments:

A client wants to start Sense tasks from some other system - sometimes even over the Internet - but does not (usually for good reasons) want to expose their on-premise, client-managed Sense environment to the world.

There are lots of ways to achieve this (keep reading for a summary of them), but in essence we're talking about this:

How can we start on-premise Sense reload tasks from anywhere in the world - maybe even over the Internet - in a simple, low cost and secure way?

What are the options?

The most common scenarios I've encountered over the years are outlined below, each with its own pros and cons. They will all allow Sense reload tasks to be started from remote systems.

The last one (option 4) is the one we'll look closer at in this blog post, as it nicely balance simplicity, low cost and security.

In the examples below Azure will be used as the foundation for the integration services, the same can most likely be achieved with other cloud providers too.

All code used in this post is available in this GitHub repository . Credentials, certificates etc have been removed from the files stored in that repository, but the general structure of files etc should still be useful when following along the text below.

1: Put Qlik Sense on the Internet

how to create task in qlik sense

While possible and relatively easy to implement, this is only a good option if there is also a thorough security framework in place.

Users have to be properly authenticated, there should probably be some kind of protection against DDOS attacks and attempts to brute force the QRS security. Blocking all IP addresses except a list of approved ones is probably a good idea too.

If the purpose is to allow Sense reload tasks to be started from Azure, other clouds or Internet in general, it's probably overkill to expose the entire QRS API to the Internet.

2: Secure site-to-site VPN tunnel to Sense

how to create task in qlik sense

Let's say you have systems running in Azure (or AWS, Google Cloud or elsewhere) and these systems should be able to start Sense reload tasks. I.e. one site in the cloud and one site locally/on-premise.

One solution is to create a site-to-site VPN tunnel between the two sites. This will make the two systems available to each other on a network level, allowing both for calls from the outside system to the QRS API and also data to be transfered from the on-prem site to the remote site.

The downside of this solution is that it requires rather expensive VPN tunnel endpoints on the on-prem side. Typically an enterprise grade firewall appliance is used for this, and those are not cheap.

There are also costs associated with the VPN tunnel on the remote/cloud side, no matter if it's Azure, AWS or something else.

3: Publish-subscribe model + Butler

how to create task in qlik sense

With this option Qlik Sense can be totally isolated, no inbound connections are needed. The important parts are:

  • Mosquitto is an open source MQTT broker with a good track record, but there are others too, both open source and commercial.
  • The open source Butler tool can both connect to an MQTT broker and start Qlik Sense reload tasks - and start reload tasks when certain MQTT messages arrive.

The concept is pretty simple:

  • A source system posts a MQTT message to an agreed upon MQTT topic, for example qliksense/starttask . The message contains the id of the Sense task to be started.
  • Butler subscribes to the same topic and gets the message within a few seconds of it being published.
  • Butler starts the task specified in the message.

4: Pub-sub + webhook + Butler

how to create task in qlik sense

Exactly the same as previous option, but with a webhook added to cater for systems that don't support MQTT.

The rationale for this solution is that plain, simple http(s) calls are very widely supported by all kinds of systems.

In other words: Most systems can be configured to make an http call - and thus also start a Qlik Sense reload task.

With this background in place, let's implement this solution in Azure and with an on-premise Qlik Sense server.

Solution overview

Steps to cover:

  • Set up a new Azure Event Grid namespace with MQTT enabled.
  • Create an Azure Function App that expose a webhook onto Internet.
  • Set up Butler to use MQTT and start Sense reload tasks using info in MQTT messages.
  • Test the whole solution.

When it comes to the Azure-related work, that can be carried out in various ways.

  • The Azure web portal can be used to create, configure and monitor resources.
  • Azure resources can be created and configured using bicep files, i.e. using an infrastructure-as-code concept. For production setups that's usually the preferred option, as it lets you store the bicep files in Git etc.
  • The az command line tool is available for Windows, macOS and Linux.
  • Most work could be done from within Visual Studio Code (=VSC), using the excellent Azure extensions there.

We'll use a mix of the above in the work that follows below.

MQTT server using Azure Event Grid

While it may seem like overkill to use Azure Event Grid as an MQTT Broker it's actually quite nice.

Event Grid is built for very large message volumes and Microsoft offers a pricing model that is quite good for our use case:

At the time of this writing the first one million MQTT messages per month in Azure Event Grid are free. That's a lot of started Sense reload tasks...

Steps to cover

  • Generate client certificates used by devices connecting to Event Grid MQTT.
  • Create an Event Grid namespace with MQTT enabled.
  • Register clients with the namespace. Only pre-approved clients will be able to connect to the namespace.
  • Verify that clients can connect and send/receive MQTT messages.
  • MQTT using Event Grid is very well documented by Microsoft, here is a good starting point .
  • This blog post extend the Microsoft documentation in some areas, and is worth a read too. Note that this text does more around security than we'll use for our use case, but it's a good source of information if you want to deploy the MQTT-start-Sense-tasks solution on your own, and require additional security.
  • Examples on how to configure MQTT in Event Grid can be found here .

Create certificates

The two references above describe very nicely how to set things up.

Some steps will be described here, but for details please refer to those texts and adapt as needed to your own naming conventions, Azure subscription etc.

The clients connecting to Event Grid will authenticate using certificates, and those certificates are created off a Certificate Authority (CA).

Let's first create that CA (using the step command line tool , in this case on macOS). Here I've entered a password of my own rather than leaving it empty (a pwd will be created for you then):

Now create two different client certificates, one for each of the clients we will connect to Event Grid with. Make the client certificates last 365 days.

Repeat for second user:

We now have two sets of certificate files:

Get the fingerprint for the two client certificates (these fingerprints will be used to authenticate with Event Grid):

Create Event Grid namespace

Let's use Azure CLI (=command line tools for this). If you prefer to use Azure web portal there are good examples in the second reference mentioned above, or on this page).

This page is also useful when dealing with configuring MQTT in Event Grid.

The following assumes Azure CLI is installed and that you have authenticated it with Azure.

First create an Azure resource group in which we can then create other resources:

To see a list of available locations (if you want to use something else than Central Sweden) you can use az account list-locations .

Let's then create an Event Grid namespace called mqttqsdemo1 with MQTT enabled:

We will need the value of topicSpacesConfiguration.hostname , i.e. mqttqsdemo1.swedencentral-1.ts.eventgrid.azure.net later when connecting our MQTT clients to Event Grid.

Register clients with Event Grid namespace

Register user1 with the namespace:

Repeat for user2, using that user's thumbprint.

Set up MQTT topic permissions

Both users are automatically added to the MQTT broker's $all client group.

Now create a simple topic space called AllTopics (MQTT topics are grouped into topic spaces in Event Grid). Include a single topic qliksense/# in the topic space:

Add a "permission binding" that gives the $all client group publish permissions to the created topic space:

Do the same again, but for subscribe permissions:

At this point the MQTT broker is up and running, with two users configured to use it.

Internet facing webhook using Function Apps

We also want a simple, Internet facing http endpoint (=webhook) that when called will result in a Qlik Sense reload task to be started.

If there isn't a need for the webhook to be Internet facing it can be deployed on Azure internal networks instead.

  • This will also create a storage account, an app service plan etc. Everything needed to get a working Function App.
  • Create local Function App project using Visual Studio Code
  • The function will forward http call parameters (e.g. a Qlik Sense task ID) to MQTT.
  • Deploy local Function App project to Azure.
  • Verify that a call to the webhook results in MQTT message being published

Create Azure storage account

The Function App (=FA) that will be created in next step needs a storage account for keeping logs etc.

If the FA is created via the Azure web UI, the storage account can be created as part of the FA creating process. If using az CLI command we first have to create a storage account.

This command will create a storage account called "stmqttqstaskdemo1" and also limit access to it so only specific IP addresses can address the account from Internet.

Initially the approved IP list is empty, easiest is to add new IPs via the Azure web UI. But it's also possible to use az command to do this.

Create Azure Function App

It's certainly possible to create a new FA using az CLI, but let's use the Azure web UI this time (the web UI automatically creates various resources the FA needs, which is convenient).

The result will be an FA in Azure that will host the webhook function that will eventually be used to start Qlik Sense reload tasks.

how to create task in qlik sense

The new FA still does not have any functions in it. In fact, we created it just to have a placeholder FA which we can later replace/overwrite with the local FA we will create in next step.

Create local Function App

Here we use Visual Studio Code (=VSC, make sure to have the Azure Function App extension installed) to create a local FA project, to which we'll add a function that will provide the webhook logic we need.

how to create task in qlik sense

We can test the new local FA from within VSC.

Press F5 to start debugging your project, then connect the storage account that was created as part of the Azure FA (or use a local emulator, either works).

how to create task in qlik sense

Let's try doing a GET call to the URL/webhook shown above:

Great, this confirms the local Function App works (in its default version).

Add new code to local project

Each FA in Azure can contain one or more functions , each doing different things. We'll only use a single function in this demo, but other functions can be added if needed.

We already have a function starttask , but it doesn't do what we want. Let's change that.

The function should

  • Accept POST calls with a text body. The body should be a Qlik Sense task ID.
  • Connect to the newly created Event Grid MQTT broker, authenticating as user1-id .
  • Forward the POST payload/body to a pre-defined MQTT topic

Let's update our local FA project accordingly.

First install the mqtt library from npm:

how to create task in qlik sense

Then update the Javascript code in the function that will handle incoming http calls:

Deploy local Function App project to Azure

With the final code in place, let's deploy the local Function App to Azure, overwriting the FA that already exists there.

how to create task in qlik sense

Demo of Function App being called from Internet

Let's test the final Function App running in Azure by calling the webhook and then trace the call all the way through the Function App and the MQTT broker, to the MQTT topic qliksense/starttask .

how to create task in qlik sense

  • Open the functions page (click on "starttask" in screen shot above, at red arrow).
  • Click on "Monitor" in menu on the left, then open the "Logs" tab.
  • Wait a few seconds, then you will be connected to the function log output.

how to create task in qlik sense

Now let's call the https endpoint (i.e. the "webhook"), again using curl on macOS. We're sending a simple JSON for this test.

Looking good, let's see what the logs in Azure shows:

how to create task in qlik sense

Also looking good.

Finally, let's see what showed up in the MQTT client I also had running, subscribed to the qliksense/starttask topic while connected as user2-id .

how to create task in qlik sense

Excellent, we have now verified that the Function App can forward the contents of a http POST call to a specific MQTT topic.

This is exactly what Butler expects in order to start Qlik Sense reload tasks.

Setting up Butler

  • Configure Butler to subscribe to the correct MQTT topic and start reload tasks based on the task IDs received on that topic.
  • Create Docker compose stack that will start Butler.
  • Verify that Butler is running and reacts to the correct MQTT messages.
  • This GitHub repository has a sample Docker compose.yaml file that will bring up Butler. There is also a sample Butler config file there. Make sure to check out the README file in that repo!
  • Butler documentation site at https://butler.ptarmiganlabs.com .
  • Butler docs around starting reload tasks by sending MQTT messages .

Starting Butler in a Docker container

Butler can be run as a standalone app, as a Windows service, as a Node.js application, in Docker or in Kubernetes.

Using Docker is a good idea, it's then easy to create dependencies on other micro services that should run alongside Butler (for example InfluxDB and Grafana). In this case we'll just use Butler, a docker compose file to start Butler is found in the GitHub repository linked in the reference section above. There you also find a working config file for Butler (working, except that you have to add the IP/host name for your own Sense server, where certificates are stored etc).

Let's start Butler by running docker compose up in the directory where the compose.yaml file is stored:

how to create task in qlik sense

Good, Butler is now running and also connected to the MQTT broker.

Time for a final, full scale test.

Seeing is believing - full demo

Start Qlik Sense reload tasks by calling Internet webhook.

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Active - QlikSenseTask is a tool for starting Qlik Sense tasks from the command line.

marcusspitzmiller/QlikSenseTask

Folders and files, repository files navigation.

Project Status: Active – The project has reached a stable, usable state and is being actively developed.

QlikSenseTask

QlikSenseTask.exe (QlikSenseTask.zip) is a tool for starting Qlik Sense tasks from the command line. Why QlikSenseTask? There will be many cases where QlikView data models and QVDs need to be loaded in QlikSense applications. This utility allows QlikView Publisher (and other schedulers) to call QlikSense tasks and load data into Qlik Sense apps.

Usage

Call QlikSenseTask.exe with the following parameters:

-proxy: - URL of Qlik Sense proxy -task: - name of reload task to execute. Put task names with spaces in quotes. -wait: - number of seconds the utility should wait for task to execute before timing out.

Example: qliksensetask -task:"Reload License Monitor" -proxy: https://localhost -wait:600"

Global Settings:

Use config.txt to set any of these parameters at a global level. The command line values override the settings in config.txt. For example, use config.txt to set the -proxy and -wait values at a global level so that each time the executable is called, these values to not need to be set from the command line.

With QlikView Publisher

With QlikSenseTask,exe, QlikView Publisher can be used to call reload tasks in Qlik Sense. Below, QlikSenseTask.exe is set up as a supporting task in Publisher which reloads a task in Qlik Sense named "Reload Sales Dashboard" and waits 90 seconds before terminating.

Pub Dependancy

This supporting task is triggered on the success of the reloaded data model in QlikView. Not pictured, the script in the Qlik Sense application binary loads the prepared QVW or QVD files.

Pub Task

Installation

Qlik Sense 1.0.X - QlikSenseTask.zip Qlik Sense 1.1 - QlikSenseTask-1.1.zip Qlik Sense 2.X - QlikSenseTask-2.X.zip Qlik Sense 3.X - QlikSenseTask-3.X.zip Note: 2.X is the same as 3.X, no changes. You can upgrade but it is totally the same. People were wondering if 2.X worked with 3.X, so this removes that doubt.

Security The current version of QlikSenseTask.exe only supports NTLM authentication. In the context of QlikView Publisher, this means that the service running Publisher will be the account to authenticate to Qlik Sense. (Note: It is possible but not yet implemented to use SSL certificates as an authentication mechanism. If there is a need, please let me know.)

The user that calls Qlik Sense needs to have access to the task. The simplest way to achieve this is to make the user a ContentAdmin in the Qlik Sense QMC.

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How to Trigger Tasks Remotely from Somewhere Else (Sense, Nprinting, etc.) to Launch a Qlikview Task?

Description.

  • Qlikview 11.20 and higher
  • QlikView: Call QMS API with cURL
  • QlikView - Call QMS API with PowerShell
  • QlikView: Connect to QMS API with Visual Studio
  • ...And more in the Knowledgebase (see  Troubleshooting Qlik Products: Identifying the issue and basics to get started )
  • It's not supported by Qlik (see  Is QMSEDX supported by Qlik? )
  • The tool was originally developed for Qlikview 11.20; it has not been tested by the author in 12.10+, and other customers have reported issues with it after upgrading to 12.10+.

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AI can also accomplish analytics tasks significantly faster than a human could, tasks such as data processing or performing exhaustive tests on all possible data combinations to identify hierarchies of relationships within the data.

Glossary Topic: Augmented Analytics - Move Beyond the Hype in AI Analytics

Move Beyond the Hype in AI Analytics

Use this checklist when you’re evaluating data analytics platforms to make sure you get the most possible value from AI.

Key Elements of AI Analytics

AI analytics (also known as augmented analytics ) enhances each aspect of data analysis by automating processes, improving accuracy and efficiency, enabling advanced techniques, and delivering specific insights and recommended actions.

Key Elements of AI Analytics

These key elements include: Data Collection and Preparation: AI enhances data collection and preparation by automating the process of gathering, cleaning, and integrating data from various sources. AI algorithms can analyze large volumes of data to detect errors or inconsistencies, and suggest data cleaning techniques, thereby improving the efficiency and accuracy of data preparation. Deployment and Integration: Technologies, such as containerization and cloud-based services, simplify the deployment of AI models in production environments. AI frameworks also offer integration capabilities with existing systems and provide APIs for seamless integration with other applications. Data Exploration and Visualization: Advanced algorithms for data analysis and visualization enhance data discovery , data mining and visualization. AI techniques, such as clustering and anomaly detection, can help uncover hidden patterns and outliers in the data. AI-powered visualization tools offer interactive and intuitive visual representations, enabling you to explore complex data sets and gain meaningful insights more effectively. Natural Language Processing (NLP): NLP supports data analytics in various ways: resolving language ambiguities and structuring data, enabling computers to communicate with you in your language for tasks like reading, speech recognition, and sentiment analysis, and providing immediate and understandable responses through NLG. Additionally, NLP aids research by analyzing vast amounts of text-based data to extract key facts, relationships, and summaries, while also performing tasks like sentiment analysis to determine the positivity, negativity, or neutrality of text. Natural Language Generation (NLG): NLG enables analytics tools to offer easily understandable responses and generate written reports in your language, automating routine analysis to save time and money. It also aids compliance teams in identifying crucial information from structured data and providing context, explanations, and potential next actions, while advanced NLG tools with machine learning capabilities can offer in-depth answers to complex questions. Machine Learning and Statistical Analysis: Machine Learning (ML) is a type of AI which automates predictive model building by allowing software to learn from historical data, identify patterns, and make predictions and decisions with little to no human guidance. ML models are the basis for most AI data analytics applications such as insight recommendations and natural language, search-based analytics. The best BI tools integrate an AutoML capability that allows you to build custom ML models without extensive training. AI algorithms, such as neural networks, support advanced techniques like deep learning, enabling more accurate and complex modeling capabilities for various use cases. Model Evaluation and Optimization: AI automates and accelerates model evaluation and optimization. AI algorithms can automatically evaluate model performance using various metrics, perform hyperparameter tuning, and optimize model architectures. AI techniques like Bayesian optimization and genetic algorithms help efficiently search the hyperparameter space, improving the overall performance of your AI models. Explainable AI (XAI) refers to techniques and processes that help you understand the rationale behind the output of your machine learning algorithm.

Predictive and Prescriptive Analytics: AI enables more accurate and powerful prediction models. With AI algorithms, predictive models can analyze large and diverse data sets, capture nonlinear relationships, and handle complex features more effectively. AI techniques, such as ensemble learning and deep neural networks, can improve the accuracy and robustness of predictive and prescriptive models.

how to create task in qlik sense

Prescriptive Analytics: Challenges & Solutions

Learn how to overcome the top 14 challenges you'll face.

AI Analytics Example

In these examples, a sales leader needs to gain insights such as the sales and cost by product in a specific category. Artificial intelligence analytics capabilities found in a modern BI tool helps him more efficiently gain the insights he needs. See how natural language processing makes it easy for a sales leader to understand the sales revenue and cost of sale by product.

how to create task in qlik sense

As shown in this example, you can type a question in regular language and NLP immediately determines the meaning of your text and generates relevant insights, including visual representations. You can then refine the resulting analytics and explore your data in ways you hadn’t considered before, to help you make the best business decisions.

A wide range of industries and job roles leverage AI analytics techniques. Here are some common predictive analytics examples across different industries.

how to create task in qlik sense

Insurance companies

may use AI analytics to assess and predict risks associated with policy applications and determine the likelihood of future claims.

Icon of buildings with a dollar sign beside them

Financial services firms

can utilize it to predict loan default probabilities, detect fraud, and forecast market movements for better investment decisions.

Icon of a shopping bag

Retailers and CPG companies

can leverage it to analyze past promotions’ effectiveness and predict which offers will be most successful in the future.

Icon of a hand with a heart in the palm

Healthcare companies

may employ it to forecast patient admissions and readmissions, enabling better management of patient care and resource allocation.

Icon of a lightbulb with gears

Energy and utilities sectors

can utilize it to analyze historical equipment failures and predict future energy demands based on past consumption patterns.

how to create task in qlik sense

Life sciences organizations

can employ it to develop patient personas and predict the likelihood of nonadherence to treatment plans.

Icon of a factory

Manufacturing and supply chain operations

can use it to forecast demand, optimize inventory management, and identify factors leading to production failures.

how to create task in qlik sense

The public sector

can leverage it to analyze population trends, plan infrastructure investments, and make informed decisions for public works projects.

AI in analytics can have a significant impact on your organization. By harnessing the power of AI, you can gain a competitive edge, drive innovation, and achieve better outcomes in various domains and industries. Here are some key benefits: Enhance Data Analysis: AI enables you to analyze vast amounts of data quickly and efficiently. AI algorithms can uncover patterns, trends, and correlations that may be difficult for humans to identify manually. This leads to more accurate and comprehensive data analysis, providing deeper insights and enabling data-driven decision-making. Improve Decision-Making: AI models can process complex data sets, perform predictive analysis, and provide recommendations based on historical data and patterns. This gives you actionable insights, helping you make strategic choices that drive business growth and competitive advantage. Automate Repetitive Tasks: AI automates repetitive and time-consuming tasks involved in data analysis. This frees up valuable time for data analysts and domain experts to focus on higher-value activities such as interpreting results, formulating strategies, and generating innovative ideas. Automation also reduces the risk of human error, leading to more reliable and consistent results. Increase Efficiency and Productivity: It accelerates data processing and analysis, helping you derive insights faster and make decisions more quickly. With AI algorithms handling complex tasks, you can streamline operations, improve productivity, and achieve more efficient resource allocation. This efficiency gain translates into cost savings and better utilization of resources. Enhance Customer Experience: It can provide you with a deeper understanding of customer behavior, preferences, and needs. By analyzing customer data, AI models can personalize marketing campaigns, optimize pricing strategies, and improve customer service. This leads to enhanced customer experiences, increased customer satisfaction, and improved customer retention. Identify New Opportunities: It can uncover hidden opportunities and potential areas for growth. By analyzing data from various sources, AI algorithms can identify market trends, customer segments, and emerging patterns that may not be apparent through traditional analysis. This helps you proactively identify new business opportunities, innovate, and stay ahead of the competition. Mitigate Risk: AI models can analyze historical data to detect anomalies, predict potential risks, and provide early warning indicators. This allows you to take preventive measures, develop risk mitigation strategies, and improve decision-making under uncertain conditions.

Modern Analytics Demo Video

Modern Analytics Demo Videos

See how to explore information and quickly gain insights.

Combine data from all your sources

Dig into KPI visualizations and dashboards

Get AI-generated insights

Learn More About AI and Machine Learning

how to create task in qlik sense

Download Datasheet

how to create task in qlik sense

Beyond the Hype: How to Get Real Value from AI in Analytics

how to create task in qlik sense

Augmented Analytics Insights

What are the main challenges of ai in analytics.

The first challenge is ensuring the availability of high-quality data that is relevant, accurate, and representative of the problem at hand. Second, there is a shortage of skilled professionals who can effectively develop, deploy, and interpret AI models. Third, ethical considerations arise in terms of data privacy, fairness, and bias, requiring you to establish robust governance frameworks. Next, the interpretability of AI models remains a challenge as complex models like deep learning neural networks are often regarded as black boxes. Lastly, AI models need ongoing monitoring to detect and address concept drift and to ensure that they remain accurate and unbiased as new data becomes available.

What are AI Analytics Tools?

AI analytics tools are software applications or platforms that enable you to leverage artificial intelligence techniques for data analysis and decision-making. These tools encompass a range of capabilities, including data collection and preparation, data exploration and visualization, machine learning algorithms, predictive analytics, natural language processing, and model evaluation and optimization. These tools often provide user-friendly interfaces, drag-and-drop functionalities, and visualization features to simplify the process of analyzing and interpreting data. They help you extract insights, make predictions, and generate actionable recommendations from complex and large-scale datasets. These tools contribute to improved efficiency, enhanced accuracy, and better decision-making, allowing you to harness the power of AI for data-driven insights and business success.

What does an AI data analyst do?

An AI data analyst is a skilled professional who specializes in extracting and analyzing statistical data from existing datasets. They play a crucial role in collecting, processing, and cleaning the data, utilizing machine learning models and advanced analytical techniques. Their primary objective is to enhance efficiency and performance by identifying meaningful patterns in the data, and they also generate informative reports to support decision-making. Given the promising future of the field, the career path of an AI data analyst is highly sought after due to the increasing demand for their expertise.

Is AI replacing data analysts?

Although AI is set to transform specific areas of data analysis, it’s not likely that it will completely replace data analysts. Instead, AI can augment the capabilities of data analysts by automating repetitive tasks like data processing and visualization, thereby freeing up their time for more valuable endeavors. Due to the current limitations of machines in comprehending context and adapting storytelling to diverse scenarios, data analysts' expertise and human judgment remain indispensable in the field.

See Modern Analytics in Action

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  • Integrating data
  • Connecting to target platforms

This section explains how to configure connectivity to a PostgreSQL target using the PostgreSQL Target connector. PostgreSQL can be used as a target in a replication task only. Before you can connect to a PostgreSQL target you need to configure the Required permissions on the database. If you are connecting to PostgreSQL via Data Movement gateway , you also need to install the driver as described in Driver setup .

For information about the limitations and considerations when using the PostgreSQL connector, see Limitations and considerations .

Setting connection properties

This section describes the available connection properties. All properties are required unless otherwise indicated.

Data target

Data gateway : Select the Data Movement gateway that will be used to test the connection to the PostgreSQL target. This should be the same gateway that was used to access the data source.

  • Requires Data Movement gateway 2023.5.10 or later.

You also need to install the appropriate driver on the Data Movement gateway machine. For details, see Driver setup below.

Cloud provider : Choose one of the following as appropriate:

None - Select when working with:

  • PostgreSQL on-premises
  • Amazon Aurora
  • Azure Database for PostgreSQL - Flexible Server.

For Amazon RDS for PostgreSQL.

Google Cloud - Select when working with:

  • Google Cloud SQL for PostgreSQL
  • Google Cloud AlloyDB for PostgreSQL

Microsoft Azure

For Microsoft Azure Database for PostgreSQL.

Host : The host name or IP address of the computer on which the PostgreSQL database is installed.

Port: The port to use when connecting to the database. The default is 5432.

Account properties

User Name and Password : The user name and password of a user authorized to access the PostgreSQL Server database.

Database properties

Database name: There are two methods you can use to specify a database:

  • Method 1 - Select from a list: Click Load databases and then select a database.
  • Method 2 - Manually: Select Enter database name manually and then enter the database name.
  • disable - Connect with a surname and password only.
  • allow - Establish an encrypted connection if requested by the server.
  • prefer - This is the default. Establishes an encrypted connection if the server supports encrypted connections, falling back to an unencrypted connection if an encrypted connection cannot be established.
  • require - Establishes an encrypted connection if the server supports encrypted connections. The connection attempt fails if an encrypted connection cannot be established.
  • verify-ca - Similar to Required , but also verifies the server Certificate Authority (CA) certificate against the configured CA certificates. The connection attempt fails if no valid matching CA certificates are found.
  • verify-full - Similar to Verify CA , but also performs host name identity verification by checking the host name the client (i.e. Data Movement gateway ) uses for connecting to the server against the identity in the certificate that the server sends to the client. The client checks whether the host name that it uses for connecting matches the Common Name value in the server certificate. The connection fails if there is a mismatch.
  • Trusted certificate: The Certificate Authority (CA) that issued the client certificate file in PEM format.
  • Client certificate: Upload the client certificate requested by the server.
  • Client certificate key : The client private key file in PEM format.
  • CRL: The CRL certificate. This file contains certificates revoked by certificate authorities. If the server certificate appears in this list, the connection will fail.
  • SSL compression: Select this option to compress the data before it is encrypted.
  • Max file size (KB): Select or type the maximum size (in KB) of a CSV file before it is loaded into the PostgreSQL target database. The default value is 32000 KB.

Internal properties

Internal properties are for special use cases and are therefore not exposed in the dialog. You should only use them if instructed by Qlik Support.

The display name for the connection.

Prerequisites

Required permissions.

The user specified in the connector settings must be a registered user in the PostgreSQL database.

In addition, the following privileges must be granted:

  • Create databases

Driver setup

You can install the driver using the driver installation utility (recommended) or manually. Manual installation should only be attempted in the unlikely event that you encounter an issue with the driver installation utility.

Using the driver installation utility to install the driver

This section describes how to install the required driver . The process involves running a script that will automatically download, install and configure the required driver . You can also run scripts to update and uninstall the driver as needed.

Preparing the installation

Make sure that Python 3.6 or later is installed on the Data Movement gateway server.

Python comes preinstalled on most Linux distributions. You can check which Python version is installed on your system, by running the following command:

python3 --version

Installing the driver

To download and install the driver :

On the Data Movement gateway machine, change the working directory to:

opt/qlik/gateway/movement/drivers/bin

Run the following command:

./install postgres

If the driver cannot be downloaded (due to access restrictions or technical issues), a message will be displayed instructing you where to download the driver and where to copy it on the Data Movement gateway machine. Once you have done that, run the install postgres command again.

Otherwise, the EULA for the driver will be displayed.

Do one of the following:

  • Press [Enter] repeatedly to slowly scroll through the EULA.
  • Press the Spacebar repeatedly to quickly scroll through the EULA.
  • Press q to quit the license text and be presented with the EULA acceptance options.
  • Type "y" and press [Enter] to accept the EULA and begin the installation.
  • Type "n" and press [Enter] to reject the EULA and exit the installation.

Type "v" and press [Enter] to view the EULA again.

The driver will be installed.

Updating the driver

Run the update command if you want to uninstall previous versions of the driver before installing the provided driver .

To download and update the driver :

./update postgres

If the driver cannot be downloaded (due to access restrictions or technical issues), a message will displayed instructing you where to download the driver and where to copy it on the Data Movement gateway machine. Once you have done that, run the update postgres command again.

  • Press [Enter] repeatedly to slowly scroll through the EULA .
  • Type "v" and press [Enter] to review the EULA from the beginning.

The old driver will be uninstalled and the new driver will be installed.

Uninstalling the driver

Run the uninstall command if you want to uninstall the driver .

To uninstall the driver :

Stop all tasks configured to use this connector.

./uninstall postgres

The driver will be uninstalled.

Installing the drivers and libraries manually

You should only attempt to install the driver manually if the automated driver installation did not complete successfully.

After Data Movement gateway is installed, download the following RPM files. You can find direct download links for the files under binary-artifacts in /opt/qlik/gateway/movement/drivers/manifests/ postgres.yaml. Once the download completes, copy the files to the Data Movement gateway machine.

  • postgresql<version>-libs-<version>PGDG.rhel8.x86_64.rpm
  • postgresql<version>-<version>PGDG.rhel8.x86_64.rpm
  • postgresql<version>-odbc-<version>PGDG.rhel8.x86_64.rpm

On the Data Movement gateway server, open a shell prompt and do the following:

Stop the Data Movement gateway service:

Optionally, confirm that the service has stopped:

The output should be as follows:

not running: /opt/qlik/gateway/movement/bin/agentctl -d /opt/qlik/gateway/movement/data service host

Install the RPM files.

Change the working directory to < Data Movement gateway -Install-Dir>/bin .

Copy the driver location to the site_arep_login.sh file, as follows:

echo "export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/lib:/usr/lib64:/usr/pgsql-<version>/lib" >> site_arep_login.sh

This will add the driver to "LD_LIBRARY_PATH" and update the driver location in the site_arep_login.sh file.

Optionally, confirm that the driver location was copied:

Makes sure that the /etc/odbcinst.ini file contains an entry for PostgreSQL, as in the following example:

[PostgreSQL Unicode(x64)]

Description = PostgreSQL ODBC driver

Driver = /usr/pgsql-13/lib/psqlodbc.so

Setup = /usr/pgsql-13/lib/psqlodbcw.so

UsageCount = 1

Start the Data Movement gateway service:

Optionally confirm that the service has started:

running: /opt/qlik/gateway/movement/bin/agentctl -d /opt/qlik/gateway/movement/data service host

Limitations and considerations

The following limitations apply when using PostgreSQL as a replication target:

  • UPDATE and DELETE operations on tables without a Primary Key/Unique Index that contain duplicate records is not supported. Any changes to these tables will affect all the records in the target.
  • Tables with a LOB column in the key are not supported in Batch Optimized Apply mode. When a VARBINARY column is used as a source table key, a BYTEA column will be created in the target. This causes unpredictable behavior in Batch Optimized Apply mode. As a workaround, we suggest using the SQLite HEX function to convert VARBINARY to VARCHAR.

Native data type information is preserved, and is displayed in the Native data type column in dataset views. If the column is not visible, you need to open the column picker of the data set view and select the Native data type column.

Data types when replicating from a PostgreSQL source

When replicating from a PostgreSQL source, the target table will be created with the same data types for all columns, apart from columns with user-defined or PostGIS data types. In such cases, the data type will be created as "character varying" in the target.

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IMAGES

  1. Qlik Sense Tutorial : A Complete Overview for Beginners

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  2. Creating a task to publish a link to QlikView document on the Qlik Sense Hub

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  3. Qlik Sense Tutorial : A Complete Overview for Beginners

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  4. Qlik Sense Tutorial : A Complete Overview for Beginners

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  5. An overview of the Qlik Sense product

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VIDEO

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  3. Qlik Sense Basic Tutorial for beginners [Complete Tutorial]

  4. Qlik AutoML

  5. Creating a reload task in Qlik Sense Enterprise on Windows

  6. Qlik Sense Tutorial for Beginners

COMMENTS

  1. Managing tasks and triggers

    Execution of a task is initiated by a trigger or manually from the tasks overview page. You can create additional triggers to execute the task, and there are two types of triggers: Scheduled. Task event. Scheduled triggers can be applied to both reload tasks and user synchronization tasks. Task event triggers can only be applied to reload tasks.

  2. How to create an external program task in Qlik Sense ...

    The functionality is exposed in the Qlik Sense Management Console beginning with the May 2021 release. See Creating and editing external program tasks. Previous versions allow you to create external tasks in a command shell process behind the scenes; which in turns means you can run more or less anything you can run on the command prompt.

  3. How do I create tasks in QMC?

    Second, once your Publisher is licensed, you will see a "Documents" tab with two options: "Source documents" and "User Documents". Click on any Source Document you will see a "+" (plus) button, where a wizard will let you create new tasks.

  4. Creating a task chain

    Select Tasks on the QMC start page or from the Start drop-down menu to display the overview. Click Create new in the action bar. The Reload task edit page is displayed. Type Task 1 in the Name field. Click Select app in the App name field. In the dialog that opens double-click app A. The dialog closes and the App name field displays app A.

  5. Qlik Reload and Task Best Practices

    General information: Incremental Reload. Qlik Sense loads all records inserted into the database or updated in the database after the last script execution. Better performance (faster) than a full reload. This mitigates performance degradation that might otherwise occur with a full reload while users are on the system.

  6. How To Create Custom Scheduled Triggers in Qlik Sense

    Open the Qlik Sense Management Console. Navigate and open Tasks. Create a new Scheduled Trigger. Create a new scheduled trigger under actions. In this new trigger, select Custom. Select Custom in the schedule options. Review Qlik Help (Reload tasks - custom option) on how to set a custom schedule. An example of a schedule that reloads every 15 ...

  7. Creating and editing external program tasks

    In the QMC, open Tasks. Click External program task to create a new external program task, or double-click an existing task to edit it. Enter a task name. Enter a path to the file to trigger. Enter input parameters for the file to trigger, if any. Optionally, edit the settings in the section EXECUTION.

  8. Creating a reload task in Qlik Sense Enterprise on Windows

    Learn how to create and manage reload tasks in Qlik Sense, a powerful data analytics platform that lets you explore and visualize your data. Watch this video tutorial to see how you can schedule, monitor, and troubleshoot reload tasks from the Qlik Management Console or the Qlik Sense Hub.

  9. Task chaining

    Create a reload task chain. Continuing with the same reload automation from above, add another reload block to create a task chain to reload two apps. Set the run mode for the Do reload block to Wait for reload to complete. Drag a second Do reload block onto the canvas and attach it to the existing Do reload block. Click Run.

  10. Viewing task chains

    Open the QMC: https://<QPS server name>/qmc. Select Tasks on the QMC start page or from the Start drop-down menu to display the overview. Tip note. You can filter a column by using the filtering option: Click on a selected task. The Task chain dialog opens. The selected task is highlighted and the arrow on the left side of the dialog points to ...

  11. Examples: Trigger a Qlik Sense task externally

    The steps are: Get app id from QMC. Build the API request from the App ID and API calls (example below) Use the POST method to run it. Note: For details, see. How to configure Postman (desktop app) to connect to Qlik Sense. Qlik Sense: Security rules needed to call task start and result endpoints. Postman: Get task status with QRS API.

  12. Creating a reload task in Qlik Sense Enterprise on Windows

    This video shows you how to create reload tasks in Qlik Sense Enterprise on Windows

  13. Task: Start

    Start a task (for example, a reload task), identified by {id}, so that it runs on a Qlik Sense Scheduler Service ( QSS ). The call returns an executionsession ID when the task has successfully started. If the call returns an empty GUID ( 00000000-0000-0000-0000-000000000000 ), the task did not start, for example, it is disabled or there is no ...

  14. Task Chaining with qlik-cli

    Task Chaining with qlik-cli Overview . Reloading data into Qlik applications is a necessary component of the BI lifecycle. In this tutorial, you are going to learn how to use the reload API to create tasks, loop to catch status changes in a reload, and how to think about setting up task chaining using a script.

  15. Tutorial

    Qlik Sense is a data visualization and discovery product in Qlik Cloud Analytics. It allows you to create flexible, interactive visualizations that lead to meaningful decisions. For additional information about Qlik Sense, see Introducing Qlik Cloud. What you will learn. When you have completed the tutorial, you will understand the basics of ...

  16. Qlik Sense QRS API: Create a task triggered after ...

    Qlik Sense QRS API: Create a task triggered after another task (PowerShell) This is a sample to create a task triggered after another one with Qlik Sense Repository API. This example is provided as is. Further questions should be directed to the Qlik Sense API and Integration forums.

  17. How to start on-premise Qlik Sense tasks from anywhere

    2: Secure site-to-site VPN tunnel to Sense. Let's say you have systems running in Azure (or AWS, Google Cloud or elsewhere) and these systems should be able to start Sense reload tasks. I.e. one site in the cloud and one site locally/on-premise. One solution is to create a site-to-site VPN tunnel between the two sites.

  18. create schedule task on qlik Cloud SaaS

    I have all the permissions and license of professional type but in the programming section it does not allow me to create or edit the existing ones. Any comment will be very helpful, I have already reviewed the documentation but do not comment on creating tasks. Labels.

  19. How to create a task with as trigger a successful task in Qlik Sense

    hi, Reload Test is created and the trigger is an event trigger but if i start the father task at the end of that doesn't start Reload Test so seems that the trigger doesn't work. In the qmc is equal to another one that i created from the qmc but the one created from qmc work in the right way, the other one created from api no.

  20. marcusspitzmiller/QlikSenseTask: Active

    With QlikSenseTask,exe, QlikView Publisher can be used to call reload tasks in Qlik Sense. Below, QlikSenseTask.exe is set up as a supporting task in Publisher which reloads a task in Qlik Sense named "Reload Sales Dashboard" and waits 90 seconds before terminating.

  21. How to Trigger Tasks Remotely from Somewhere Else (Sense ...

    After another event completes (Sense, Nprinting, etc. task), how can one remotely launch a task in Qlikview? Environments: Qlikview 11.20 and higher; Resolution. If you want to trigger Qlikview tasks remotely, in-general you would use the QMS (SOAP) API.

  22. Tasks in the Qlik Sense Management Console don't ...

    Executing tasks or modifying tasks (changing owner, renaming an app) in the Qlik Sense Management Console and refreshing the page does not update the correct task status. Issue affects Content Admin and Deployment Admin roles. The behaviour began after an upgrade of Qlik Sense Enterprise on Windows. Fix version:

  23. Landing data from data sources

    Create and configure a landing data task. This describes how to create a landing data task. The quickest way to create a data pipeline is to onboard data which creates a landing data task and a storage data task, ready to prepare and run. For more information, see Onboarding data. Click Add new in Qlik Cloud Data Integration home, and select ...

  24. Monitoring app reload failed / Multinode + February 2024

    Qlik Sense works fine on all the nodes. I've installed (via import) all the monitoring apps and follow all the steps described here : Configuring the Monitoring apps | Qlik Sense for administrators Help => So, all the REST_API url have been updated and the loadind scripts of the monitoring apps too

  25. AI Analytics: What It is, Why It Matters, & Use Cases

    AI analytics (also known as augmented analytics) enhances each aspect of data analysis by automating processes, improving accuracy and efficiency, enabling advanced techniques, and delivering specific insights and recommended actions.. These key elements include: Data Collection and Preparation: AI enhances data collection and preparation by automating the process of gathering, cleaning, and ...

  26. PostgreSQL

    PostgreSQL ON THIS PAGE. Setting connection properties; Prerequisites; Limitations and considerations; Data types; This section explains how to configure connectivity to a PostgreSQL target using the PostgreSQL Target connector. PostgreSQL can be used as a target in a replication task only.