What you'll learn

Without a solid understanding of how to structure and connect your data, your Qlik Sense apps can become slow, complex, and unreliable. This can lead to inaccurate insights and a frustrating user experience.

In this course, Model Data in Qlik Sense, you’ll gain the ability to build robust, efficient, and scalable data models.

First, you’ll explore Qlik Sense’s associative data model, learning how key fields connect tables, how selections propagate throughout your data, how to avoid synthetic keys by manually creating composite keys, and the difference between relational joins and Qlik's associative links.

Next, you’ll discover how to analyze and troubleshoot data relationships, use the data model viewer to identify and resolve common issues like circular references and synthetic keys, and investigate field-level relationships using table previews and metadata.

Finally, you’ll learn how to apply advanced modeling techniques to optimize your data, build star and snowflake schemas, create canonical models, link tables to manage complex associations, and strategically combine or split tables to improve performance.

When you’re finished with this course, you’ll have the skills and knowledge of Qlik Sense data modeling needed to design and build high-performing Qlik Sense applications that deliver fast, reliable, and accurate insights.