Implementing Qlik Sense

Ganapati Hegde, Kaushik Solanki

• Publisher: Packt Publishing
• Year: 2017
• ISBN:
  9781786466600
• Edition: 1
• Topic: Business
• Subtopic: Business Intelligence
• Index: Business

Become a full-fledged Qlik Sense Consultant with the help of this unique guideAbout This Book• Become a successful Qlik consultant with the help of this insightful guide• Build what is in line as well as exceeding your customer's expectations from your Qlik Sense solutions using this highly practical guide• Build result-driven optimized BI solutions using Qlik with the help of industry examplesWho This Book Is ForIf you have basic familiarity with Qlik Sense and want to upgrade your skills to become a full-fledged Qlik Consultant, this book is for you. With this book, you will be able to create efficient business intelligence solutions that would fetch client satisfaction, and in turn, more projects.What You Will Learn• Understand the importance and expectations of a consultant's role• Engage with the customer to understand the ir goals and future objectives• Design the optimum architecture, using the best practices for the development and implementation of your projects• Ensure successful adoption using real-life examples to make your learning complete• Learn about the important stages of a Qlik project's life cycleIn DetailQlik Sense is a leading platform for business intelligence (BI) solutions. Qlik Sense helps organizations in making informed decisions based on the data they have.This book will teach you how to effectively use Qlik for optimum customer satisfaction. You will undergo a metamorphosis from a developer to a consultant who is capable of building the most suitable BI solutions for your clients. The book will take you through several business cases – this will give you enough insight to understand the needs of the client clearly and build a BI solution that meets or exceeds their expectations. Starting from the pre-project activities, you will go to the actual execution of the project, the implementation, and even maintenance. This book will give you all the information you need - from the strategy to requirement gathering to implementing BI solutions using Qlik Sense.The book will empower you to take the right decisions in tricky and diffi cult situations while developing analytics and dashboards.Style and approachThis book will be a hands-on guide that will teach you all the what-to-do's, when-to-do's, and how-to-do's for becoming a successful Qlik Sense Consultant. With the help of various business scenarios, the book will cover real-world problems that you can relate to.Various solutions in the book will be backed up by the thought process of why are these solutions used and how you can implement them in your own business environment.

Development

In the previous chapter, we looked at the importance of good architecture. We understood various aspects of architecture, both from infrastructure and data perspective. The chapter focused on other important aspects as well, such as backup and recovery design. In this chapter, we will learn about the best practices in dashboard designing and the art of developing a quality and user-friendly dashboard, which will cater to all the business requirements and provide business users with an ability to analyze their data, along with flexibility.

Along with this, we will see how to execute the project based on the data architecture we built in the previous chapter. We will also look at various best practices while developing dashboards.

This chapter is going to be the heart of the book as it will take you through the actual development once we have the requirements from the business users. In this chapter, you will learn how to develop quality code, tackle common coding challenges, and write easy maintainable code.

The project sustainability will depend on how best you code your scripts and design the visualization. A poorly coded project may initially look okay, but over a period will cause performance issues, leading to user dissatisfaction and ultimately the end of the adoption.

Let us start the chapter with couple of quotes which inspire us to write good code:

Any fool can write code that a computer can understand. Good programmers write code that humans can understand. - Martin Fowler

Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. - Martin Golding

Both the preceding quotes try to teach us the importance of coding. In the world of information technology, it is said that writing a fresh new code is much easier then changing or modifying an existing code. This is true because at times the codes are written without following the best practices. Same parameter applies to Qlik Sense projects, as it also involves scripting. We will learn how to write quality codes later in this chapter.

In this chapter, we will look at some of the areas in Qlik Sense where we follow good practices:

• Data extraction process
• Building data model
• Common challenges of data modeling
• Script management
• Best practices in data modeling
• Data modeling validation
• Security strategy
• Visualization strategy
• Choices of objects
• Use of extensions
• Visualization best practices
• Story telling

Data extraction process

The process of fetching the data from various source databases for further transformation and utilization in development of dashboard is the Data Extraction Process for Qlik Sense projects.

Data extraction is the starting step towards building the Qlik Sense dashboards. This process involves fetching the data from one or more databases, and merging and transforming them in such a way that it can be visualized graphically and an analysis can be done on that.

Qlik Sense uses the Extract, Transform, and Load (ETL) process to prepare the data for dashboard usage. Let us have a detailed look at the ETL process.

Extract, Transform, and Load (ETL)

ETL is the process used to create a data mart and data warehouse. In this process, the data is pulled from various data sources, then transformed into the required format by applying the business logic, and then stored back in the destination database, which could be the data warehouse.

The main element of this process is the staging area, because the pulled data gets stored in the staging area first and then the required transformation happens on that data. Once the transformation is finished, the output is stored in the destination database. Thus, the staging area plays an important role in ETL, and for the same reason, the staging area needs to be of good capacity for processing and storage.

Following diagram shows the ETL process:

Extract

Extraction is the process of pulling the required data from available data sources. The sources can be any ERP (Enteprise resource planning) systems, such as Oracle Apps, SAP, Microsoft Dynamics, or Tally ERP, or it can be CRM system, such as Salesforce, Zoho, or Bitrix24. The sources can be flat file systems like excels, csv, text files, delimited files, or XML files, or normal databases like Oracle, SQL Server, Mongo DB, or MySQL; it can also be social media data from Facebook, Twitter, and so on.

The extraction from the database can be done in mainly following two ways:

• Full load
• Incremental load

Full Load

In this type of extraction, all the data from the source system is pulled using the extraction scripts. It is like creating replica of the required tables from the database in the staging area. The advantage of full load is that you do not need to worry about any changes happening in the source database, such as insert, update, or delete. Every time you extract the data, you pull the entire data from the source, and thus don't miss any data since the last extraction.

This process allows hassle free scripting, as there is no need to apply any logic while pulling the data. But on the other hand, it is a time-consuming process due to the full extracts of the data. The time required to finish the extraction process depends on the numbers of records and fields a particular table has.

Full load of the data is generally performed on data marts which are small, mainly the dimension tables which have few millions of records, such as product master, customer master, and so on. Apart from this, full load is also done for fact tables at the initial stage of data warehouse, when there is no data in it.

Let us understand the concept using an example. Let us assume that you want to do a full load on sales table. A sales table with data is shown in the following image:

Assuming that the preceding data is stored in excel named Sales.xlsx, the full load script in Qlik Sense will be as follows:

Once this script is reloaded, it will fetch the nine records. Now if new data gets added, modified, or deleted in the table, the same script will be used to get the entire data. Every time the script is reloaded, it will purge the old data which is already fetched and extract full data from the source system and store it in QVD.

Incremental Load

Another type of data extraction method ...

Table of contents

• Title Page
• Copyright
• Credits
• About the Authors
• About the Reviewers
• www.PacktPub.com
• Customer Feedback
• Preface
• Consultant - An Introduction
• Preparing for the Project
• Prerequisites to Start a Project
• Requirement Gathering
• Architecture Design
• Development
• Validation, UAT and Go-Live
• Post Go-Live
• Avoiding Common Pitfalls
• Knowledge Sets
• A Real Life Case-Study