Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Scientific Computing and Big Data Analysis with Python and Hadoop

The MapReduce framework

MapReduce is a framework used to compute a large amount of data in a Hadoop cluster. MapReduce uses YARN to schedule the mappers and reducers as tasks, using the containers. The MapReduce framework enables you to write distributed applications to process large amounts of data from a filesystem, such as a Hadoop Distributed File System (HDFS), in a reliable and fault-tolerant manner. When you want to use the MapReduce framework to process data, it works through the creation of a job, which then runs on the framework to perform the tasks needed. A MapReduce job usually works by splitting the input data across worker nodes, running the mapper tasks in a parallel manner.

At this time, any failures that happen, either at the HDFS level or the failure of a mapper task, are handled automatically, to be fault-tolerant. Once the mappers have completed, in the results are copied over the network to other machines running the reducer tasks.

An example of using a MapReduce job...