Book Image

QlikView: Advanced Data Visualization

By : Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover
Book Image

QlikView: Advanced Data Visualization

By: Miguel Angel Garcia, Barry Harmsen, Stephen Redmond, Karl Pover

Overview of this book

QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: • QlikView for Developers by Miguel Ángel García, Barry Harmsen • Mastering QlikView by Stephen Redmond • Mastering QlikView Data Visualization by Karl Pover
Table of Contents (25 chapters)
QlikView: Advanced Data Visualization
Contributors
Preface
Index

Dealing with multiple fact tables in one model


In data models designed around business processes, we will often have just one source fact table. If we have additional fact tables, they tend to be at a similar grain to the main fact table, which is easier to deal with. Line-of-business documents may have fact tables from lots of different sources that are not at the same grain level at all, but we are still asked to deal with creating the associations. There are, of course, several methods to deal with this scenario.

Joining the fact tables together

If the fact tables have an identical grain, with the exact same set of primary keys, then it is valid to join, using a full outer join, the two tables together. Consider the following example:

Fact:
Load * Inline [
Date, Store, Product, Sales Value
2014-01-01, 1, 1, 100
2014-01-01, 2, 1, 99
2014-01-01, 1, 2, 111
2014-01-01, 2, 2, 97
2014-01-02, 1, 1, 101
2014-01-02, 2, 1, 98
2014-01-02, 1, 2, 112
2014-01-02, 2, 2, 95
];

Join (Fact)
Load * Inline...