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

Chapter 5. Storage Component – HBase

One of the most important components of the Hadoop ecosystem is HBase, which utilizes HDFS very efficiently and can store, manage, and process data at a much better performing scale. NoSQL is emerging, and there is a lot of attention towards different implementations and solutions in Big Data problem solving space. HBase is a NoSQL database which can process the data over and above HDFS to achieve very good performance with optimization, scalability, and manageability. In Hadoop, HDFS is very good as storage for the WORM (Write Once Read Many) paradigm where data is not updated. In many scenarios, the requirements would be updating, ad hoc analysis or random reads. In HDFS, processing these requirements is not very efficient as updating a record in a file is not possible; HDFS has to delete and rewrite the whole file which is resource, memory and I/O intensive. But HBase can manage such processing efficiently in a huge volume of random read and writes...