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

Dimensional data modeling


There are several methodologies for implementing a data warehouse or data mart that might be useful to consider when implementing QlikView in an organization. However, for me, the best approach is dimensional modeling—often called Kimball dimensional modeling—as proposed by Ralph Kimball and Margy Ross in the book The Data Warehouse Toolkit, John Wiley & Sons, now available in its third edition.

Some other methodologies, most noticeably that proposed by Bill Inmon, offer a "top-down" approach to data warehousing whereby a normalized data model is built that spans the entire enterprise, then data marts are built off this to support lines of business or specific business processes. Now, QlikView can sit very readily in this model as the data mart tool, feeding off the Enterprise Data Warehouse (EDW). However, QlikView cannot implement the normalized EDW.

In my opinion, Kimball dimensional modeling, on the other hand, is right up QlikView's street. In fact, I would...