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

An introduction to Spark

Spark is a cluster computing framework, which was developed in AMPLab at UC Berkley and contributed as an open source project to Apache. Spark is an in-memory based data processing framework, which makes it much faster in processing than MapReduce. In MapReduce, intermediate data is stored in the disk and data access and transfer makes it slower, whereas in Spark it is stored in-memory. Spark can be thought of as an alternative to MapReduce due to the limitations and overheads of the latter, but not as a replacement. Spark is widely used for streaming data analytics, graph analytics, fast interactive queries, and machine learning. It has attracted the attention of many contributors due to its in-memory nature and actually was one of the top-level Apache projects in 2014 with over 200 contributors and 50+ organizations. Spark utilizes multiple threads instead of multiple processes to achieve parallelism on a single node.

Spark's main motive was to develop a processing...