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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Create dynamic dashboards to bring interactive data visualization to your enterprise using Qlik Sense
Key Features
- Implement various Qlik Sense features to create interactive dashboards
- Analyze data easily and make business decisions faster using Qlik Sense
- Perform self-service data analytics and geospatial analytics using an example-based approach
Book Description
Qlik Sense allows you to explore simple-to-complex data to reveal hidden insights and data relationships to make business-driven decisions.
Hands-On Business Intelligence with Qlik Sense begins by helping you get to grips with underlying Qlik concepts and gives you an overview of all Qlik Sense's features. You will learn advanced modeling techniques and learn how to analyze the data loaded using a variety of visualization objects. You'll also be trained on how to share apps through Qlik Sense Enterprise and Qlik Sense Cloud and how to perform aggregation with AGGR. As you progress through the chapters, you'll explore the stories feature to create data-driven presentations and update an existing story. This book will guide you through the GeoAnalytics feature with the geo-mapping object and GeoAnalytics connector. Furthermore, you'll learn about the self-service analytics features and perform data forecasting using advanced analytics. Lastly, you'll deploy Qlik Sense apps for mobile and tablet.
By the end of this book, you will be well-equipped to run successful business intelligence applications using Qlik Sense's functionality, data modeling techniques, and visualization best practices.
What you will learn
- Discover how to load, reshape, and model data for analysis
- Apply data visualization practices to create stunning dashboards
- Make use of Python and R for advanced analytics
- Perform geo-analysis to create visualizations using native objects
- Learn how to work with AGGR and data stories
Who this book is for
If you're a data analyst, BI developer, or interested in business intelligence and want to gain practical experience of working on Qlik Sense, this book is for you. You'll also find it useful if you want to explore Qlik Sense's next-generation applications for self-service business intelligence. No prior experience of working with Qlik Sense is required.
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Veröffentlichungsjahr: 2019
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Hands-On Business Intelligence with Qlik Sense
Copyright © 2019 Packt Publishing
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Sunith ShettyAcquisition Editor:Devika BattikeContent Development Editor:Rhea HenriquesTechnical Editor:Sayli NikaljeCopy Editor: Safis EditingLanguage Support Editors: Storm Mann, Mary McGowanProject Coordinator:Manthan PatelProofreader: Safis EditingIndexer: Mariammal ChettiyarGraphics:Jisha ChirayilProduction Coordinator: Nilesh Mohite
First published: February 2019
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ISBN 978-1-78980-094-4
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Contributors
About the authors
Pablo Labbe is a BI consultant with over 18 years of experience. In 2008, he was presented with QlikView, the former product by Qlik and the seed for Qlik Sense. Since then, he was focused on delivering BI solutions in a new way. Now, he is the principal of ANALITIKA Inteligencia, delivering BI projects and training that is focused on Qlik products and other technologies that embrace self-service BI. He is an active member of Qlik Community and other social media sites. You can follow him on Twitter @pablolabbe and find him on LinkedIn.
Clever Anjos is an experienced business discovery professional with several years of experience with Qlik, Microsoft, and other business intelligence (BI) technologies. He holds a bachelor's degree in computer science from Universidade Federal de Uberlândia (Minas Gerais State—Brazil). He works at Qlik as a solution architect, helping companies to use Qlik technologies to enable their professionals to be fully data literate. He has several Qlik certifications and awards (such as Qlik Luminary, Qlik MVP, and Presales Rookie of the Year—Americas 2018).
Kaushik Solanki is a computer engineer by profession. He works at Predoole Analytics Pvt Ltd as a Qlik architect and delivery manager. He has nine years of experience working with Qlik technology. His passion is to educate everyone about data literacy and Qlik. He loves to spend time on Qlik Community helping Qlik developers learn and excel.
He has a great understanding of project delivery, right from business requirements to final implementation. His experience in various domains has helped businesses to take valuable business decisions.
Jerry DiMaso is an Analytics Advisory Consultant who has spent the past 10 years developing applications, advising on data and analytics strategies, and coaching organizations on how to build efficient analytics operating models. His work in more than 100 organizations in dozens of different industries has inspired him to take on the mission of improving the world's analytics capabilities through a series of practical frameworks and methodologies, most notably the Analytics Enablement and Axis Academy programs that he has created.
About the reviewer
Nitesh Kumar Sethi has more than a decade of experience in the BI industry and has been widely recognized and accepted as an expert in the field. QlikTech has awarded him Qlik Luminary awards for two years running, in 2018 and 2019. With deep drive, great passion, and plenty of expertise, Nitesh champions the vision of turning data into insights that lead to transformative discoveries.
Nitesh held the first Qlik meet in India, which was even the first in the Asia-Pacific region. He is also an official Qlik captain, as recognized by QlikTech.
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Table of Contents
Title Page
Copyright and Credits
Hands-On Business Intelligence with Qlik Sense
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Qlik Sense and Business Intelligence
Getting Started with Qlik Sense
An overview of the Qlik Sense product
The components of Qlik Sense
In-memory associative database
ETL engine
Data manager
Script
Data model
Visualization platform
The hub
Application overview
Sheets
Objects
API and extensibility capabilities
The Associative Engine
Setting up Qlik Sense Desktop
Setting up Qlik Sense Cloud
Self-service with Qlik Sense
Summary
Section 2: Data Loading and Modeling
Loading Data in Qlik Sense
Technical requirements
Data loading process
Loading data from data sources
Data connections
Data manager
Dragging a data file into your application
Loading a data file from a folder (Qlik Sense Desktop)
Loading a data file from data files (QlikCloud)
Creating calculated fields
Data load editor
Table associations
Data profiling
Profiling using the Data manager
Profiling using the Data model viewer
Summary
Further reading
Implementing Data Modeling Techniques
Technical requirements
An overview of data modeling
Data modeling techniques
Entity relationship modeling
Dimensional modeling
Joining
Types of joins
Join/outer join
Left join
Right join
Inner join
Pitfalls of using joins
Concatenation 
Automatic concatenation
Forced concatenation
The NoConcatenate
Filtering
Filtering data using the Data manager
Filtering data in the script editor
QVDs
Why use QVDs?
Link table
Canonical dates
As-Of Table
Script optimization
Using Applymap instead of joins
Applymap()
Reducing the size of data as much as possible
Optimized QVD load
Non-optimized load
Optimized load
Dropping unwanted tables immediately after use
Summary
Sample questions
Further reading
Section 3: Building an Analytical Application
Working with Application Structure
Technical requirements
Application overview
Toolbars
Understanding the DAR methodology
Creating visualization objects
Getting started
Generating visualizations using Insights Advisor
Generating visualizations using Insights Advisor for selected fields
Creating visualizations using chart suggestions
Creating visualizations manually
Creating Master items
Creating master dimensions
Creating master measures
Creating master visualizations
Calculation expressions
Summary
Questions
Further reading
Creating a Sales Analysis App Using Qlik Sense
Technical requirements
Creating the dashboard sheet
Creating the dashboard
Creating a new sheet for the dashboard
Creating KPI visualizations
Creating a pie chart with Sales $ by Categories
Creating a bar chart with Sales $ by Top 10 Customers
Creating the geographical map of sales by country
Creating a filter pane with Order Year and Order Month fields
Creating the analysis sheets
Creating a customer analysis sheet
Creating a new sheet for customer analysis
Adding a filter pane with main dimensions
Adding KPI visualizations
Creating a combo chart for Pareto (80/20) analysis
Creating a table chart with customer information
Creating a product analysis sheet
Creating a new sheet for product analysis
Adding a filter pane
Adding KPI visualizations
Creating a bar chart with a drill-down dimension
Creating a line chart by OrderMonthYear and Category
Creating a scatter plot
Creating a reporting sheet
Creating a new sheet
Adding a default filter pane
Summary
Interacting with Advanced Expressions
Technical requirements
Creating calculations with conditions
Condition to show a text message
Condition to show a different calculation
Condition to filter data on a measure
Using TOTAL for aggregation scope
Calculating the relative share over the total
Calculating the relative share over a dimension
Using some useful inter-record functions
Calculating sales variance year over year
Using AGGR for advanced aggregation
Calculating the top sales product over each category
Leveraging Set Analysis for in-calculation selection
Selecting a specific country for comparison
Summary
Further reading
Creating Data Stories
An overview of stories
Creating snapshots
Planning and organizing your presentation
Creating stories
Editing your story
Sharing stories
Summary
Further reading
Section 4: Additional Features
Engaging On-Demand App Generation
Technical requirements
How Qlik Sense handles large volumes of data 
Setting up a Google BigQuery account
Configuring Qlik Sense for ODAG applications
Building a summarized application
Creating a connection
Adding a script to retrieve data
Building the detailed application
Binding expressions in on-demand template apps
Recovering a long list of selected (or possible) values
Adding restrictions
Creating a dynamic SQL 
Integrating the summarized and detailed applications
Testing our on-demand application
Summary
Further reading
Creating a Native Map Using GeoAnalytics
Technical requirements
Concepts of GeoAnalytics
Creating a map
Loading geographical data
Adding the base map
Adding layers
Area layer
Heatmap layer
Adding more information to the map
Label
Info Bubble
Summary
Further reading
Working with Self-Service Analytics
Technical requirements
Creating self-service analytics
Publishing an application
Creating a new sheet in a published app
Sharing insights with community sheets
Approving sheets to add them to a baseline
Co-creating applications in Qlik Sense Cloud Business
Managing members
Editing the application with multiple users
Sharing the app with users
Publishing changes to a published application
Summary
Further reading
Data Forecasting Using Advanced Analytics
Technical requirements
Qlik Sense Engine and Server Side Extensions
Qlik approach to data science platforms
How SSE works
SSE functions
Preparing your R environment
Installing R
Installing Rserve()
Installing more packages
Installing the SSE plugin
Configuring Qlik Sense
Qlik Sense Desktop
Qlik Sense Enterprise
Starting all services
Using the R extension in a Qlik Sense application
Preparing your Python environment
Installing Python
Updating Python pip
Installing TensorFlow
Using a Python extension
Configuring Qlik Sense
Qlik Sense Desktop
Qlik Sense Enterprise
Using the Python SSE in your apps
Summary
Questions
Further reading
Deploying Qlik Sense Apps for Mobile/Tablets
Technical requirements
Setting up the Sales Analysis app for mobile usage
Responsive layouts
Responsive object design
Reviewing the responsive design of the Sales Analysis application
The Quick view sheet
Choosing the right client
Preparing the Sales Analysis app for offline usage
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Qlik Sense allows you to explore simple-to-complex data to reveal hidden insights and data relationships to make business-driven decisions. Hands-On Business Intelligence with Qlik Sense begins by helping you get to grips with underlying Qlik concepts and gives you an overview of all Qlik Sense's features. You will learn advanced modeling techniques and understand how to analyze the data loaded using a variety of visualization objects. You'll also be trained on how to share apps through Qlik Sense Enterprise and Qlik Sense Cloud and how to perform aggregation with AGGR. As you progress through the chapters, you'll explore the stories feature to create data-driven presentations and adopt a better way to update an existing story. This book will guide you in exploring the GeoAnalytics feature with the geo-mapping object and GeoAnalytics connector. Furthermore, you'll learn about self-service analytics features and perform data forecasting using advanced analytics. Lastly, you'll deploy Qlik Sense apps for mobile and tablets. By the end of this book, you will be well-equipped to run successful business intelligence projects using Qlik Sense's functionality, data modeling techniques, and visualization best practices.
Who this book is for
If you're a data analyst or interested in business intelligence (BI) and want to gain practical experience of working on Qlik Sense projects, this book is for you. You'll also find it useful if you want to explore Qlik Sense's next-generation applications for self-service BI.
What this book covers
Chapter 1, Getting Started with Qlik Sense, will focus on getting started with Qlik Sense with the Qlik Sense Desktop application and the Qlik Sense Cloud web-based application. We'll take a quick overview of the high-level features of Qlik Sense so it's clear how the Qlik Sense platform can be leveraged for individuals and enterprises, then we will jump right into using Qlik Sense.
Chapter 2, Loading Data in Qlik Sense, will cover a series of tasks to load data from several sources, such as text files and Excel spreadsheets. We will find data quality issues (such as null values in a field) with data profiling and create a data model, associating the data source using key fields to link tables.
Chapter 3, Implementing Data Modeling Techniques, will help you learn about various data modeling techniques along with the best data modeling practices for Qlik Sense. It will also cover topics such as joins, concatenation, filtering, and the use of Qlikview Data (QVD) files, which will help you to build the perfect data model. You will also learn how to handle dates using canonical date, how to handle accumulations and rolling averages in script using As-Of Table, and how to handle multiple fact tables in data models using link tables. Finally, we will focus on improving script performance using optimization techniques.
Chapter 4, Working with Application Structure, will explore the key concepts of a Qlik Sense application design. Along with this, we will look at the principles of building a Qlik Sense app using the Dashboard, Analysis, Reporting (DAR) methodology, learn how to use the visualization objects that are available to the user, and see how to create and use master items to reuse dimensions and metrics across visualizations. Finally, we will learn how to use the Qlik Sense user interface and look at the basics of calculation expressions.
Chapter 5, Creating a Sales Analysis App Using Qlik Sense, is where we will create a sales analysis application to explore and analyze the data model that we created in Chapter 2, Loading Data in Qlik Sense. During the development of the application, we will apply the use of the DAR methodology explained in Chapter 4, Working with Application Structure.
Chapter 6, Interacting with Advanced Expressions, will teach you about the power of the calculation engine. After reading this, you will know how to create a calculation with conditions, as well as how to use aggregation scope, inter-record functions, and advanced aggregation with AGGR. Finally, you will learn how to use set analysis to create a calculation with very specific data selection.
Chapter 7, Creating Data Stories, will look at an effective way to communicate insights using a Qlik Sense application called storytelling. The whole idea of storytelling in BI is to take an idea or an insight and turn it into an appealing story to show what we think about it. The story makes our insight more interesting. This also happens in everyday life; stories have always been the go-to method to grab someone's attraction.
Chapter 8, Engaging On-Demand App Generation, will explore how to create a summarized application, which is a regular Qlik Sense app where the fact table is aggregated. This application is capable of analyzing a database containing the data of a million bike trips without sacrificing too much RAM. By integrating a template, we give the user the capability to dig into detailed information. When the user needs to see detailed data in Qlik Sense, we will use the template to generate another application with the detailed data that the user has requested.
Chapter 9, Creating Maps Using GeoAnalytics, looks at GeoAnalytics, which is an add-on to Qlik Sense and Qlikview. This product has mapping capabilities that leverage Qlik Sense to analyze data that has geospatial naming conventions, exposing geographic relationships between data points. We are going to use those capabilities to analyze vehicular collisions that have occurred in New York City.
Chapter 10, Working with Self-Service Analytics, is where you will discover how to explore the self-service analytics features provided by Qlik Sense Enterprise and Qlik Sense Cloud Business. When using Qlik Sense Enterprise, you will learn how to build new sheets and create new visualizations using the master items library. You will also learn how to share insights with other users, creating community sheets and approving an analysis sheet to act as a baseline for developers. In Qlik Sense Cloud Business, you will learn how to co-create apps with other users in the same workspace.
Chapter 11, Data Forecasting Using Advanced Analytics, is where we will work together to enable Qlik Sense applications to predict how business Key Performance Indicators (KPIs) will perform in the future. This is not is about using technology to predict business behavior, but is instead a matter of using technologies from data science such as machine learning (ML).
Chapter 12, Deploying Qlik Sense Apps for Mobile/Tablets, will show you how to deploy the sales analysis application we will have built for use in mobile devices and tablets. This enables us to freely access information wherever we are, even if we don't have a network connection. You will learn how to craft your dashboard so that it can be visualized on a small screen. We will discuss what we need in order to enable an application to be downloaded to a device and used offline. These activities are important for creating a great experience for users when they interact with the application from a small device.
To get the most out of this book
No prior experience of working with Qlik Sense is required.
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Section 1: Qlik Sense and Business Intelligence
In this section, we shall introduce business intelligence (BI), the modern concepts raised in the early days, and setting up Qlik Sense products for upcoming projects.
This section shall contain only one chapter:
Chapter 1
,
Getting started with Qlik Sense
Getting Started with Qlik Sense
In today's consumer-driven world, with tremendous competition and rapidly-developing technologies, it is imperative for organizations to leverage data to drive decision-making across all aspects of business to lower costs, increase revenues, and mitigate risks. However, this is much easier said than done; there are hundreds of tools and technologies available in today's market that collect, process, and serve data, but choosing the right technologies is often a challenge. In this book, we discuss one particular technology that provides an enterprise solution for processing and serving analytics: Qlik Sense.
Qlik Sense is a data-discovery and analytics platform composed of an in-memory associative database, a data-extraction and transformation engine that connects to dozens of data sources natively, an intuitive self-service data-modeling and visualization tool, and a set of open APIs that allow for complex customizations of workflow and visualizations. Qlik is a nine-time leader in the Gartner Analytics and Business Intelligence Platform Magic Quadrant (https://www.qlik.com/us/gartner-magic-quadrant-business-intelligence) and competes in the business intelligence (BI) market as a complete solution for enterprise analytics. Today, Qlik Sense is being used actively in virtually every industry, in hospitals, banks, manufacturing plants, and everywhere in between, to serve up analytics to users so they can make better decisions that drive better outcomes.
Qlik Sense is a self-contained platform that facilitates all aspects of operating. It is an expanding analytics organization, and as such includes a wide variety of features and functionality, from advanced administrative capabilities to self-service visualization elements to artificial intelligence (AI) engines. This breadth of capabilities, combined with continuing acquisitions of complementary software, such as Podium Data for data cataloging and CrunchBot for natural language processing (NLP), is what helps to differentiate Qlik in the analytics and BI space and provide a truly scalable analytics solution that can serve dozens or hundreds of thousands of users.
In this chapter, we will focus on getting started with Qlik Sense with the Qlik Sense Desktop application and the Qlik Sense Cloud web-based application. We'll provide a quick overview of the high-level features of Qlik Sense, so that it's clear how the Qlik Sense Platform can be leveraged for individuals and in the enterprise, then we will jump into using Qlik Sense.
We will cover the following topics:
An overview of the Qlik Sense product
The Associative Engine
Setting up Qlik Sense Desktop
Setting up Qlik Sense Cloud
Self-service with Qlik Sense
An overview of the Qlik Sense product
This chapter will provide an overview of several of the major facets of the Qlik Sense software and what you need to understand to get started with Qlik Sense. We will not be covering the administration components or delving too deep into the advanced extensibility capabilities, but, by the end of this chapter, you will understand what Qlik Sense is and how you can use it.
The components of Qlik Sense
In the following sections, we will cover four major components of the Qlik Sense software:
In-memory associative database
The
Extract-Transform-Load
(
ETL
) engine:
Data manager
Script
Data model
The visualization platform:
The hub
Applications
Sheets
Objects
API and extensibility capabilities
In-memory associative database
Qlik's in-memory associative database is the proprietary technology that Qlik invented in 1993 in Lund, Sweden, which allows large amounts of data to be compressed, stored in the RAM, and rapidly traversed in the Qlik Sense client. In other words, this is what makes Qlik, Qlik.
This database houses all of the data we need inside the Qlik Engine and allows us to explore the datasets in a way that facilitates analytics in a much better manner. We'll get into how and why this approach is better in the next section, The Associative Engine.
ETL engine
Qlik Sense includes a built-in ETL engine that allows us to connect to many different sources, such as Excel, SQL, and Hadoop, to extract data into Qlik Sense. We can also use this ETL engine to transform the data, to manipulate the data, to clean up dirty data, or to create new data.
This is a very powerful component of the Qlik platform because we don't have to leave Qlik to do mappings, create buckets, or fix bad data; we can do everything we need right inside Qlik Sense, and there is even an intuitive user interface that guides users with no programming knowledge required.
Data manager
The Qlik Sense Data manager provides a way to pull data into Qlik Sense through an intuitive user interface. It contains a way to connect to your data sources, select the data you are looking to analyze, pull the data in, and link it to other data you have pulled in. Using Qlik's Cognitive Engine, and AI created by Qlik, the Qlik Sense Data manager automatically profiles your data and recommends how to connect different tables based on similar data keys. We'll go further into this profiling capability and how to transform data in the Self-servicewith Qlik Sense section at the end of this chapter.
Script
For more advanced users, the Qlik Sense script provides the capability to programmatically extract and transform data from data sources. It uses a scripting language similar to SQL and allows more granular control over how the data is extracted and transformed. Most users will use the Data manager, but it is important to note that this capability exists for users who need to transform data in more complex ways:
The preceding screenshot is of the Qlik Sense script setting a variable with the Today() function and loading a table from a QVD, which is a proprietary Qlik data file that is optimized for use in Qlik. Most users will not leverage QVDs directly, but there are many advanced use cases where they can and should be leveraged.
Data model
The Qlik Sense Data model viewer allows us to see all of the data we have pulled into Qlik Sense. This is similar to the Data manager view, but is intended more for understanding the relationships between the data than adding or transforming data. This view provides information on linkages between tables, including metadata about the tables and fields in our data model, and a preview of the data. This view is very useful for understanding the state of the data we've pulled into Qlik Sense and ensuring that everything is linked together in the way we want it to be linked:
The preceding screenshot shows a sample Qlik Sense data model. The SalesDetails2 table is selected, as can be seen by the dark orange highlight (dark gray), and tables that are directly linked to the SalesDetails2 table are highlighted in light orange (light gray).
Visualization platform
The Qlik Sense client is what we, as users, interact with directly; this is the component that allows us to import and transform data, build charts, and perform analytics. Once data is loaded into the Qlik Sense application, we can use the Qlik Sense client to create visualizations on top of that data, such as line charts, bar charts, tables, and maps. Creating these visualizations is as simple as dragging a chart type onto the canvas and dropping in measures and dimensions. If you're looking to kickstart your vizzing, Qlik's Insight Engine creates visualizations for you at the click of a button. We will go through how to do that in the Self-service with Qlik Sense section.
Here are the components of the Qlik Sense client:
The hub
Application overview
Sheets
Objects
The hub
Here is a view of the hub, which houses applications. Note that the hub may look slightly different depending on whether you are using Qlik Sense Desktop, Qlik Sense Cloud, or Qlik Sense Enterprise. The hub is where you can search through and access all of your applications, create new applications, and find information about your Qlik Sense version:
Application overview
A Qlik Sense application contains several components, most notably a single data model (see the Data model section for more information) and a collection of one or more sheets. Think of an application as a container for data and visualizations; it holds both the data and the visualizations to which we attach the data:
The preceding screenshot shows the
