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)

Data analytics with Apache Spark

Apache Spark offers a blazing fast processing engine based out of Apache Hadoop. It provides in-memory cluster processing of the data, thereby providing analytics at high speeds. Apache Spark evolved in AMPLab (U. C. Berkeley) in 2009 and it was made open source through the Apache Software Foundation. Apache Spark is based out of YARN. Following are key features of Apache Spark:

  • Fast: Due to in-memory processing capability, Spark is fast in processing
  • Multiple language support: You can write Spark programs in Java, Scala, R, and Python
  • Deep analytics: It provides truly distributed analytics, which includes machine learning, streaming data processing, and data querying
  • Rich API support: It provides a rich API library for interaction in multiple languages
  • Multi-processing engine support: Apache Spark can be deployed on MapReduce, YARN, and Mesos...