Mastering Qlik Sense

You've probably done some beginner work on Qlik Sense, so you know how easy it is to work with the interface. However, there is always more than what we see on the Qlik Sense dashboard. Work done by developers in the extremity plays a very important role while making an appealing and well-performing dashboard.

If your data model is not designed appropriately, you will face issues not only with the performance of the app but also with the quality of its output.

In this course, we look at everything that goes into creating a data model to give you the most optimized performance, including how to load data from different data sources, fix synthetic keys, and other issues. Also included are topics such as creating link tables, the master calendar script, and applying set analysis to your data. Thus, you will master the art of creating dashboards that are not only visually appealing and accurate but also very informative and a BIG bonus to your team while making those tough decisions.

What You Will Learn

• Dive into schemas in data modeling and Qlik Sense preferences when creating dashboards with accelerated performance
• Load data from different data sources and know about the various connectors available within Qlik Sense
• Fix real-world issues you face, such as synthetic keys while loading your data
• Modify data in existing tables to obtain values required for your dashboard design as per business requirements
• Transform data to generate new columns and achieve desired outcomes on the dashboard
• Understand the use and benefits of including Preceding Load and Mapping Load in your data model
• Take a look at the different types of join and use them to optimize data models
• Realize the significance of Link Tables and how to create them so you can get better insights
• Master the Calendar script and modifications that we can make to obtain fields for timeline analysis

Audience

This course is for Business Intelligence professionals and Data Analysts who want to become experts in using Qlik Sense. If you have extensively used QlikView in the past and are looking to transition to Qlik Sense, this course will also help you.

A fundamental idea of how Qlik Sense works, its architecture, and its features is all you need to get started with this course.

About The Author

Colibri Digital: Colibri is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas like big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to make better sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

James Cross is a Big Data Engineer and certified AWS Solutions Architect with a passion for data-driven applications. He's spent the last 3-5 years helping his clients to design and implement huge-scale, streaming big data platforms, cloud-based analytics stacks, and serverless architectures.

He started his professional career in Investment Banking, working with well-established technologies such as Java and SQL Server, before moving into the Big Data space. Since then he's worked with a huge range of big data tools including most of the Hadoop eco-system, Spark, and many No-SQL technologies such as Cassandra, MongoDB, Redis, and DynamoDB. More recently his focus has been on cloud technologies and how they can be applied to data analytics, culminating in his work at Scout Solutions as CTO, and more recently with Mckinsey.

James is an AWS certified solutions architect with several years' experience designing and implementing solutions on this cloud platform. As CTO of Scout Solutions Ltd, he built a fully serverless set of APIs and an analytics stack based around Lambda and Redshift.