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
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
3
Pillars of Hadoop – HDFS, MapReduce, and YARN
Index

Data ingestion in Hadoop


In Hadoop, storage is never an issue, but managing the data is the driven force around which different solutions can be designed differently with different systems, hence managing data becomes extremely critical. A better manageable system can help a lot in terms of scalability, reusability, and even performance. In a Hadoop ecosystem, we have two widely used tools: Sqoop and Flume, both can help manage the data and can import and export data efficiently, with a good performance. Sqoop is usually used for data integration with RDBMS systems, and Flume usually performs better with streaming log data.

We will cover the concept of Sqoop and Flume in greater detail in Chapter 6, Data Ingestion in Hadoop—Sqoop and Flume.