Book Image

Hadoop Essentials

By : Shiva Achari
Book Image

Hadoop Essentials

By: Shiva Achari

Overview of this book

This book jumps into the world of Hadoop and its tools, to help you learn how to use them effectively to optimize and improve the way you handle Big Data. Starting with the fundamentals Hadoop YARN, MapReduce, HDFS, and other vital elements in the Hadoop ecosystem, you will soon learn many exciting topics such as MapReduce patterns, data management, and real-time data analysis using Hadoop. You will also explore a number of the leading data processing tools including Hive and Pig, and learn how to use Sqoop and Flume, two of the most powerful technologies used for data ingestion. With further guidance on data streaming and real-time analytics with Storm and Spark, Hadoop Essentials is a reliable and relevant resource for anyone who understands the difficulties - and opportunities - presented by Big Data today. With this guide, you'll develop your confidence with Hadoop, and be able to use the knowledge and skills you learn to successfully harness its unparalleled capabilities.
Table of Contents (15 chapters)
Hadoop Essentials
About the Author
About the Reviewers
Pillars of Hadoop – HDFS, MapReduce, and YARN


Streaming and real-time analysis are required in many systems in big data. Batch processing is very well handled by Hadoop and integration of frameworks like Storm and Spark elevates their streaming and real-time capability.

We discussed that Storm is an open source, fast, stream processing, scalable, fault-tolerant, and reliable system that is easy to use and deploy. Storm's physical architecture comprises Nimbus, Supervisor, Worker, and Zookeeper processes. The data architecture of Storm comprises a spouts, bolts, and topology-based data flow system.

Spark is an extremely popular framework which provides in-memory data handling capability and makes it much faster than the MapReduce framework. Spark frameworks have some libraries such as Spark SQL, GraphX, MLib, Spark Streaming, and others to process specialized data and requirements. Spark Architecture is based on RDDs and the DAG engine, which provides capability of in-memory data processing and optimizes the processing, according...