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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Introduction


One of the most valuable means through which we can make sense of big data, and thus make it more useful to most people, is data visualization. Visualization of data depends a lot on the use cases. Graphs and charts are visual representations of data. They provide a powerful means of summarizing and presenting data in a way that most people find easier to comprehend. Charts and graphs enable us to see the main features or characteristics of some data. They not only enable us to present the numerical findings of a study but also provide the shape and pattern of the data, which is critical in data analysis and decision making. There are many key considerations you need to keep in mind when developing data visualizations:

  • What type of graphical representation to use for which type of data
  • How to design a visualization approach that allows interactive features
  • How to search and modify datasets graphically
  • How to differentiate between data and the resultant insights
  • How to develop a visualization...
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