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Mastering Hadoop 3

You're reading from  Mastering Hadoop 3

Product type Book
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Pages 544 pages
Edition 1st Edition
Languages
Authors (2):
Chanchal Singh Chanchal Singh
Profile icon Chanchal Singh
Manish Kumar Manish Kumar
Profile icon Manish Kumar
View More author details
Toc

Table of Contents (23) Chapters close

Title Page
Dedication
About Packt
Foreword
Contributors
Preface
1. Journey to Hadoop 3 2. Deep Dive into the Hadoop Distributed File System 3. YARN Resource Management in Hadoop 4. Internals of MapReduce 5. SQL on Hadoop 6. Real-Time Processing Engines 7. Widely Used Hadoop Ecosystem Components 8. Designing Applications in Hadoop 9. Real-Time Stream Processing in Hadoop 10. Machine Learning in Hadoop 11. Hadoop in the Cloud 12. Hadoop Cluster Profiling 13. Who Can Do What in Hadoop 14. Network and Data Security 15. Monitoring Hadoop 1. Other Books You May Enjoy Index

Hive


The Hadoop ecosystem has helped organizations to save costs working with large datasets. Most Hadoop implementations use commodity hardware for storage and processing. This helps companies build low-cost infrastructures to provide high availability and scalable processing power. However, Hadoop's MapReduce processing model was mostly written in Java. The existing data storage infrastructure was mostly developed on traditional relational databases that uses SQL for data processing. Thus, it is necessary to have a tool that can provide similar functionality in the Hadoop ecosystem. 

Hive is a data warehouse tool that can process huge amounts of data stored over a distributed storage system, like HDFS using SQL-like queries. The user uses Hive query language, which is very much similar to other SQL-like languages. Hive was developed with the purpose of easing the job of data warehouse users who have strong knowledge of SQL queries and who find it difficult to adopt Java or other languages...

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