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Hadoop Real World Solutions Cookbook - Second Edition


Key Features

  • Implement outstanding Machine Learning use cases on your own analytics models and processes.
  • Solutions to common problems when working with the Hadoop ecosystem.
  • Step-by-step implementation of end-to-end big data use cases.

Who This Book Is For

Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes.

Book Description

Big data is the current requirement. Most organizations produce huge amount of data every day. With the arrival of Hadoop-like tools, it has become easier for everyone to solve big data problems with great efficiency and at minimal cost. Grasping Machine Learning techniques will help you greatly in building predictive models and using this data to make the right decisions for your organization.

Hadoop Real World Solutions Cookbook gives readers insights into learning and mastering big data via recipes. The book not only clarifies most big data tools in the market but also provides best practices for using them. The book provides recipes that are based on the latest versions of Apache Hadoop 2.X, YARN, Hive, Pig, Sqoop, Flume, Apache Spark, Mahout and many more such ecosystem tools. This real-world-solution cookbook is packed with handy recipes you can apply to your own everyday issues. Each chapter provides in-depth recipes that can be referenced easily. This book provides detailed practices on the latest technologies such as YARN and Apache Spark. Readers will be able to consider themselves as big data experts on completion of this book.

This guide is an invaluable tutorial if you are planning to implement a big data warehouse for your business.

What you will learn

  • Installing and maintaining Hadoop 2.X cluster and its ecosystem.
  • Write advanced Map Reduce programs and understand design patterns.
  • Advanced Data Analysis using the Hive, Pig, and Map Reduce programs.
  • Import and export data from various sources using Sqoop and Flume.
  • Data storage in various file formats such as Text, Sequential, Parquet, ORC, and RC Files.
  • Machine learning principles with libraries such as Mahout
  • Batch and Stream data processing using Apache Spark

About the Author

Tanmay Deshpande is a Hadoop and big data evangelist. He's interested in a wide range of technologies, such as Apache Spark, Hadoop, Hive, Pig, NoSQL databases, Mahout, Sqoop, Java, cloud computing, and so on. He has vast experience in application development in various domains, such as finance, telecoms, manufacturing, security, and retail. He enjoys solving machine-learning problems and spends his time reading anything that he can get his hands on. He has a great interest in open source technologies and promotes them through his lectures. He has been invited to various computer science colleges to conduct brainstorming sessions with students on the latest technologies. Through his innovative thinking and dynamic leadership, he has successfully completed various projects. Tanmay is currently working with Schlumberger as the lead developer of big data. Before Schlumberger, Tanmay worked with Lumiata, Symantec, and Infosys.

He currently blogs at http://hadooptutorials.co.in.

Table of Contents

  1. Getting Started with Hadoop 2.X
  2. Exploring HDFS
  3. Mastering Map Reduce Programs
  4. Data Analysis Using Hive, Pig, and Hbase
  5. Advanced Data Analysis Using Hive
  6. Data Import/Export Using Sqoop and Flume
  7. Automation of Hadoop Tasks Using Oozie
  8. Machine Learning and Predictive Analytics Using Mahout and R
  9. Integration with Apache Spark
  10. Hadoop Use Cases
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