Book Image

Qlik Sense Cookbook - Second Edition

By : Pablo Labbe, Philip Hand, Neeraj Kharpate
Book Image

Qlik Sense Cookbook - Second Edition

By: Pablo Labbe, Philip Hand, Neeraj Kharpate

Overview of this book

Qlik Sense allows you to explore simple and complex data to reveal hidden insight and data relationships that help you make quality decisions for overall productivity. An expert Qlik Sense user can use its features for business intelligence in an enterprise environment effectively. Qlik Sense Cookbook is an excellent guide for all aspiring Qlik Sense developers and will empower you to create featured desktop applications to obtain daily insights at work. This book takes you through the basics and advanced functions of Qlik Sense February 2018 release. You’ll start with a quick refresher on obtaining data from data files and databases, and move on to some more refined features including visualization, and scripting, as well as managing apps and user interfaces. You will then understand how to work with advanced functions like set analysis and set expressions. As you make your way through this book, you will uncover newly added features in Qlik Sense such as new visualizations, label expressions and colors for dimension and measures. By the end of this book, you will have explored various visualization extensions to create your own interactive dashboard with the required tips and tricks. This will help you overcome challenging situations while developing your applications in Qlik Sense.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
Packt Upsell

Using an extended interval match to handle slowly changing dimensions

Sometimes, while developing the data model for a Business Intelligence application, you encounter dimensional values that tend to change over time. Such dimensions are known as slowly changing dimensions. For example, an employee joins a company at a Junior Executive level and stays in the same position for one year. After one year, the designation changes to Senior Executive and then changes to Project Manager after three years. The position field, in this case, will be treated as a Slowly Changing Dimension. Such Slowly Changing Dimensions can be represented in Qlik Sense, provided the historical data is stored at the source with a proper "Position Start Date" and "Position End Date." In order to match the discrete date values to the date intervals, we will make use of the intervalmatch function. At the same time, we will match the values of the primary key. This will help us to build an optimized data model and properly...