What this book covers
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).
