Qlik & Python – Made for Each Other

[jptneumann]

Qlik Embedded Analytics offers a powerful solution to embed analytics capabilities directly into custom systems and applications, providing a more integrated and fluid experience for users. By allowing data, insights, and visualizations to be directly accessed in an already familiar interface, this technology expands the possibilities of data-driven analysis and decision-making, without the need to leave the usual work environment.

In the context of systems developed in Python language, the potential of Qlik Embedded Analytics is even more promising. Python is widely used in the development of data science, machine learning, and automation applications, and its integration with Qlik allows developers to combine the best of both worlds: Qlik's robust analytics capabilities with the flexibility and computational power of Python.

Potential of Qlik Embedded Analytics for Python

1. Automation of analytical processes: With the integration of Qlik Embedded Analytics, systems developed in Python can automate the collection, processing, and visualization of data, utilizing Qlik's infrastructure to provide visual insights directly in the system. This reduces the need to create custom dashboards from scratch.
2. Machine Learning and Artificial Intelligence: Python is widely used to develop machine learning models. Qlik Embedded Analytics can display the results of these models, enabling predictive analytics and advanced insights within the application, without the need for external analytics platforms.
3. Customization flexibility: Qlik allows developers to use its API to create custom interfaces and widgets. By integrating this with Python code, it is possible to generate solutions tailored to specific use cases, creating a tailored analytical workflow.
4. Scalability and performance: Qlik is designed to handle large volumes of data and fast queries, while Python offers a vast array of libraries for data processing. Together, these technologies make it possible to build scalable applications with robust data visualizations, even in big data scenarios.
5. Custom Algorithm Integration: Python allows for the creation of custom algorithms for data analysis. Using Qlik Embedded Analytics, these algorithms can be applied in real-time to the data visualized in the application, enriching the decision-making process.

Embedding Qlik Sense Dashboards in Python

One of the most important aspects of incorporating Qlik Sense dashboards into systems developed in Python is ensuring user security and correct authentication. Several ways of authentication can be applied to ensure that only authorized users have access to the data and embedded dashboards.

When choosing the authentication method to embed Qlik Sense dashboards into a Python application, it is important to consider the existing infrastructure, the level of security required, and the end-user experience. Methods such as JWT and SAML are widely recommended for modern systems that need robust security and Single Sign-On support. Methods such as Qlik Ticketing and API Key can be used in simpler or more controlled situations. In this article's example, I used JWT to perform automatic authentication without the need for user interference.

The following is a screenshot of an application developed in Python doing Embedded Analytics with a dashboard available in a Qlik Sense SaaS environment.

See in the image that the incorporation is complete and transparent, giving the impression that they are a single system.

Generating Real-Time Qlik Sense Reports With Python

Reports can be created and shared with stakeholders in real time, using data directly from Qlik dashboards. This approach is especially useful in environments where quick decisions need to be made.

In the following screenshot, we show the preview of a report generated in PDF on the Python system, using the Qlik platform:

See in the following video the Python system interacting with Qlik Sense SaaS:

Other opportunities to use Qlik with Python

In addition to doing Embedded Analytics and Generating Reports, we can integrate Qlik with Python in a variety of ways and purposes, showing how Qlik has a fantastic ecosystem capable of integrating with systems and tools powering your applications. Qlik Sense, with its powerful APIs, offers several options for integration with external systems, and the use of Python further enhances this capability, providing a robust platform for reporting automation and data analysis.

With Qlik Sense APIs and SDKs we can interact with visualizations, data objects, and dashboards directly from Python scripts. These APIs allow, for example, to extract data and graphical objects, where you can use the Engine API to extract data directly from Qlik objects (graphs, tables, and KPIs) and use them in reports generated by Python.

Another opportunity is to include the features of Qlik Insight Advisor in your Python application, allowing conversational analytics to be part of the user experience in your system developed in Python. We may also interact with Qlik App Automation by running or interacting with Qlik automations and other marketplace tools.

It is also possible to interact with the Qlik platform and use Python as a powerful DevOps tool allowing the management of the Qlik platform and automate the creation of users and spaces, perform reloads, analyze logs, export objects and others.

Conclusion

The use of Qlik Embedded Analytics with systems developed in Python opens up a range of possibilities for developers who want to incorporate powerful data analysis capabilities into their applications, creating more robust, scalable solutions adapted to the needs of users. The synergy between these technologies increases productivity, promotes more informed decisions, and offers an integrated and efficient experience.

[AkashPohare_HB]

Hello @jptneumann ,

How about connecting to Qlik Engine API using Python and extracting script from data load editor or fetching chart details such as values, measures etc?

Any source of information is available on that.

Thanks.