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

Chapter 5. SQL on Hadoop

Hadoop is traditionally used as a File System with the capability to process high data volumes using distributed algorithms. However, with its growing popularity among non-programmers and business analysts, there is a need to read and manipulate high volume records using simple, well-known interfaces. SQL is always popular among non-programmers and data analysts because of its simple constructs and easy-to-understand logical syntax. Since Hadoop is used as storage for large volumes of data and because data exploration on top of Hadoop is one of the key use cases, SQL is ideal. Keeping those goals in mind, many SQL engines are developed to process and explore data stored in the Hadoop File System. There are many SQL distributions on Hadoop. Most of them are open source. We will look into those one by one in the following sections.

In this chapter, we will cover the following topics:

  • Presto
  • Hive
  • Impala