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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

What this book covers

Chapter 1, Introduction to Hadoop, introduces you to the world of Hadoop and its core components, namely, HDFS and MapReduce.

Chapter 2, Overview of Big Data Analytics, introduces the process of examining large datasets to uncover patterns in data, generating reports, and gathering valuable insights.

Chapter 3, Big Data Processing with MapReduce, introduces the concept of MapReduce, which is the fundamental concept behind most of the big data computing/processing systems.

Chapter 4, Scientific Computing and Big Data Analysis with Python and Hadoop, provides an introduction to Python and an analysis of big data using Hadoop with the aid of Python packages.

Chapter 5, Statistical Big Data Computing with R and Hadoop, provides an introduction to R and demonstrates how to use R to perform statistical computing on big data using Hadoop.

Chapter 6, Batch Analytics with Apache Spark, introduces you to Apache Spark and demonstrates how to use Spark for big data analytics based on a batch processing model.

Chapter 7, Real-Time Analytics with Apache Spark, introduces the stream processing model of Apache Spark and demonstrates how to build streaming-based, real-time analytical applications.

Chapter 8, Batch Analytics with Apache Flink, covers Apache Flink and how to use it for big data analytics based on a batch processing model.

Chapter 9, Stream Processing with Apache Flink, introduces you to DataStream APIs and stream processing using Flink. Flink will be used to receive and process real-time event streams and store the aggregates and results in a Hadoop cluster.

Chapter 10, Visualizing Big Data, introduces you to the world of data visualization using various tools and technologies such as Tableau.

Chapter 11, Introduction to Cloud Computing, introduces Cloud computing and various concepts such as IaaS, PaaS, and SaaS. You will also get a glimpse into the top Cloud providers.

Chapter 12, Using Amazon Web Services, introduces you to AWS and various services in AWS useful for performing big data analytics using Elastic Map Reduce (EMR) to set up a Hadoop cluster in AWS Cloud.

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