Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Table of Contents (12 chapters)


MapReduce is a concept that is borrowed from functional programming. The data processing is broken down into a map phase, where data preparation occurs, and a reduce phase, where the actual results are computed. The reason MapReduce has played an important role is the massive parallelism we can achieve as the data is sharded into multiple distributed servers. Without this advantage, MapReduce cannot really perform well.

Let's take up a simple example to understand how MapReduce works in functional programming:

  • The input data is processed using a mapper function of our choice
  • The output from the mapper function should be in a state that is consumable by the reduce function
  • The output from the mapper function is fed to the reduce function to generate the necessary results

Let's understand these steps using a simple program. This program uses the following text...