Book Image

Mastering Hadoop 3

By : Chanchal Singh, Manish Kumar
Book Image

Mastering Hadoop 3

By: Chanchal Singh, Manish Kumar

Overview of this book

Apache Hadoop is one of the most popular big data solutions for distributed storage and for processing large chunks of data. With Hadoop 3, Apache promises to provide a high-performance, more fault-tolerant, and highly efficient big data processing platform, with a focus on improved scalability and increased efficiency. With this guide, you’ll understand advanced concepts of the Hadoop ecosystem tool. You’ll learn how Hadoop works internally, study advanced concepts of different ecosystem tools, discover solutions to real-world use cases, and understand how to secure your cluster. It will then walk you through HDFS, YARN, MapReduce, and Hadoop 3 concepts. You’ll be able to address common challenges like using Kafka efficiently, designing low latency, reliable message delivery Kafka systems, and handling high data volumes. As you advance, you’ll discover how to address major challenges when building an enterprise-grade messaging system, and how to use different stream processing systems along with Kafka to fulfil your enterprise goals. By the end of this book, you’ll have a complete understanding of how components in the Hadoop ecosystem are effectively integrated to implement a fast and reliable data pipeline, and you’ll be equipped to tackle a range of real-world problems in data pipelines.
Table of Contents (23 chapters)
Title Page
Dedication
About Packt
Foreword
Contributors
Preface
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...