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Mastering QlikView

By : Stephen Redmond

4.5 (10)

Mastering QlikView

4.5 (10)

By: Stephen Redmond

Overview of this book

If you are a business application developer or a system analyst who has learned QlikView and Qlik Sense and now want to take your learning to a higher level, then this book is for you. It is assumed that you are aware of the fundamentals of QlikView and have working knowledge of development and in-memory analytics.

Preface

Preface

Qlik Sense

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Free Chapter

1. Performance Tuning and Scalability

1. Performance Tuning and Scalability

Reviewing basic performance tuning techniques

Generating test data

Understanding how QlikView stores its data

Strategies to reduce the data size and improve performance

Testing chart performance for different load options

Optimizing chart calculation times

The QlikView calculation engine

Using Direct Discovery

Testing scalability with JMeter

Running an example execution

Summary

2. QlikView Data Modeling

2. QlikView Data Modeling

Reviewing basic data modeling

Dimensional data modeling

Handling slowly changing dimensions

Dealing with multiple fact tables in one model

Drilling across with document chaining

Summary

3. Best Practices for Loading Data

3. Best Practices for Loading Data

Reviewing data loading concepts

Understanding why you should use an ETL approach

Using an ETL approach to create QVD data layers

Mastering loading techniques

Using QlikView Expressor for ETL

Summary

4. Data Governance

4. Data Governance

Reviewing basic concepts of data governance

Establishing descriptive metadata

Extracting metadata

Deploying the QlikView Governance Dashboard

Summary

5. Advanced Expressions

5. Advanced Expressions

Reviewing basic concepts

Understanding Dollar-sign Expansion

Using advanced Set Analysis

Calculating vertically

Summary

6. Advanced Scripting

6. Advanced Scripting

Reviewing the basic concepts

Counting records

Loading data quickly

Applying variables and the Dollar-sign Expansion in the script

Using control structures

Examining advanced Table File Wizard options

Looking at data from different directions

Reusing code

Summary

7. Visualizing Data

7. Visualizing Data

Reviewing the history of data visualization

Understanding the audience

Designing effective visualizations

Summary

Index

Index

Customer Reviews

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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...

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