Book Image

Apache Hadoop 3 Quick Start Guide

By : Hrishikesh Vijay Karambelkar
Book Image

Apache Hadoop 3 Quick Start Guide

By: Hrishikesh Vijay Karambelkar

Overview of this book

Apache Hadoop is a widely used distributed data platform. It enables large datasets to be efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and introduce you to the main technical topics, including MapReduce, YARN, and HDFS. The book begins with an overview of big data and Apache Hadoop. Then, you will set up a pseudo Hadoop development environment and a multi-node enterprise Hadoop cluster. You will see how the parallel programming paradigm, such as MapReduce, can solve many complex data processing problems. The book also covers the important aspects of the big data software development lifecycle, including quality assurance and control, performance, administration, and monitoring. You will then learn about the Hadoop ecosystem, and tools such as Kafka, Sqoop, Flume, Pig, Hive, and HBase. Finally, you will look at advanced topics, including real time streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations of the Hadoop 3 cluster.
Table of Contents (10 chapters)

Understanding Hadoop APIs and packages

Now let's go through some of the key APIs that you will be using while you program in MapReduce. First, let's understand the important packages that are part of Apache Hadoop MapReduce APIs and their capabilities:


Java API Packages



Primarily provides interfaces for MapReduce, input/output formats, and job-related classes. This is an older API.


Contains libraries for Mapper, Reducer, partitioners, and so on. To be avoided—use mapreduce.lib.


Job submitter-related classes.

Command-line tools associated with MapReduce.


The org.apache.Hadoop.mapred.uploader package contains classes related to the MapReduce framework upload tool.