The concepts and models necessary to efficiently and effectively visualize big data can be daunting but are not unobtainable. Unfortunately, when it comes to big data, many of the available data visualization tools, with their rudimentary functions and features, are somewhat ineffective.
Using basic analytical concepts (reviewed in this book), you’ll learn to use some of the most popular open source tools (and others) to meet these challenges and approach the task of big data visualization to support better decision making.
Chapter 1, Introduction to Big Data Visualization, – starts out by providing a simple explanation of just what data visualization is and then provides a quick overview of various generally accepted data visualization concepts.
Chapter 2, Access, Speed, and Storage with Hadoop, aims to target the challenge of storing and accessing large volumes and varieties (structured or unstructured) of data offering working examples demonstrating solutions for effectively addressing these issues.
Chapter 3, Understanding Your Data Using R, explores the idea of adding context to the big data you are working on with R.
Chapter 4, Addressing Big Data Quality, talks about categorized data quality and the challenges big data brings to them. In addition, examples demonstrating concepts for effectively addressing these areas are covered.
Chapter 5, Displaying Results Using D3, explores the process of visualizing data using a web browser and Data-Driven Documents (D3) to present results from your big data analysis projects.
Chapter 6, Dashboards for Big Data - Tableau, introduces Tableau as a data visualization tool that can be used to construct dashboards and provides working examples demonstrating solutions for effectively presenting results from your big data analysis in a real-time dashboard format.
Chapter 7, Dealing with Outliers Using Python, focuses on the topic of dealing with outliers and other anomalies as they relate to big data visualization, and introduces the Python language with working examples of effectively dealing with data.
Chapter 8, Big Data Operational Intelligence with Splunk, offers working examples demonstrating solutions for valuing big data by gaining operational intelligence (using Splunk).
Most of the tools and technologies used in this book are open source and available for no charge. All of the others offer free trials for evaluation. With this book, and some basic exposure to data analysis (or basic programming concepts) the reader will be able to gain valuable insights to the world of big data visualization!
The target audience of this book are data analysts and those with at least a basic knowledge of big data analysis who now want to learn interesting approaches to big data visualization in order to make their analysis more valuable. Readers who possess adequate knowledge of big data platform tools such as Hadoop or have exposure to programming languages such as R can use this book to learn additional approaches (using various technologies) for addressing the inherent challenges of visualizing big data.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code reads the link and assigns it to the to the BeautifulSoup function."
A block of code is set as follows:
for row in reader:
if (row['Denomination']) == 'Penny':
if int(row['Coin-in'])<2000:
x += int(row['Coin-in'])
row_count += 1When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
row_count = 0
aver_coin_in = 0.0
x = 0.0
y = 999
z = 0.0New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."
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