Synopsis
Learn effective tools and techniques to separate big data into manageable and logical components for efficient data visualization
About This Book
- This unique guide teaches you how to visualize your cluttered, huge amounts of big data with ease
- It is rich with ample options and solid use cases for big data visualization, and is a must-have book for your shelf
- Improve your decision-making by visualizing your big data the right way
Who This Book Is For
This book is for data analysts or those with a basic knowledge of big data analysis who want to learn big data visualization in order to make their analysis more useful. You need sufficient knowledge of big data platform tools such as Hadoop and also some experience with programming languages such as R. This book will be great for those who are familiar with conventional data visualizations and now want to widen their horizon by exploring big data visualizations.
What You Will Learn
- Understand how basic analytics is affected by big data
- Deep dive into effective and efficient ways of visualizing big data
- Get to know various approaches (using various technologies) to address the challenges of visualizing big data
- Comprehend the concepts and models used to visualize big data
- Know how to visualize big data in real time and for different use cases
- Understand how to integrate popular dashboard visualization tools such as Splunk and Tableau
- Get to know the value and process of integrating visual big data with BI tools such as Tableau
- Make sense of the visualization options for big data, based upon the best suited visualization techniques for big data
In Detail
When it comes to big data, regular data visualization tools with basic features become insufficient. This book covers the concepts and models used to visualize big data, with a focus on efficient visualizations.
This book works around big data visualizations and the challenges around visualizing big data and address characteristic challenges of visualizing like speed in accessing, understanding/adding context to, improving the quality of the data, displaying results, outliers, and so on. We focus on the most popular libraries to execute the tasks of big data visualization and explore "big data oriented" tools such as Hadoop and Tableau. We will show you how data changes with different variables and for different use cases with step-through topics such as: importing data to something like Hadoop, basic analytics.
The choice of visualizations depends on the most suited techniques for big data, and we will show you the various options for big data visualizations based upon industry-proven techniques. You will then learn how to integrate popular visualization tools with graphing databases to see how huge amounts of certain data. Finally, you will find out how to display the integration of visual big data with BI using Cognos BI.
Style and approach
With the help of insightful real-world use cases, we ll tackle data in the world of big data. The scalability and hugeness of the data makes big data visualizations different from normal data visualizations, and this book addresses all the difficulties encountered by professionals while visualizing their big data.
About the Author
James D. Miller is an IBM certified expert, creative innovator, and accomplished Director, Sr. Project Leader, and Application/System Architect with more than 35 years of extensive applications, system design, and development experience across multiple platforms and technologies. His experiences and specialties include introducing customers to new and sometimes disruptive technologies and platforms, integrating with IBM Watson Analytics, cloud migrations, Cognos BI, TM1 and web architecture design, systems analysis, GUI design and testing, data and database modeling and systems analysis, design, and the development of OLAP, Client/Server, Web and Mainframe applications and systems utilizing IBM Watson Analytics, IBM Cognos BI and TM1 (TM1 rules, TI, TM1Web and Planning Manager), Cognos Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, IIS, MS Visual Basic and VBA, Visual Studio, Perl, Splunk, WebSuite, MS SQL server, ORACLE, SYBASE Server, and more. His responsibilities have also included all aspects of Windows and SQL solution development and design, including analysis; GUI (and Web site) design; data modeling; table, screen/form and script development; SQL (and remote stored procedures and triggers) development/testing; test preparation; and the management and training of programming staff. His other experience includes the development of ETL infrastructure such as data transfer automation between mainframe (DB2, Lawson, Great Plains, and so on) systems and client/server SQL server and web-based applications and integration of enterprise applications and data sources. Mr. James D. Miller has acted as an Internet Applications Development manager responsible for the design, development, QA, and delivery of multiple websites, including online trading applications, warehouse process control, scheduling systems, and administrative and control applications. He was also responsible for the design,
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