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

Pig


Hadoop had MapReduce as a processing engine when it first started and Java was the primary language that was used for writing MapReduce jobs. Since Hadoop was mostly used as an analytics processing framework, large chunks of use cases involved data mining on legacy data warehouses. These data warehouse applications were migrated to use Hadoop. Most users using legacy data warehouses had SQL and that was their core expertise. Learning a new programming language was time-consuming. Therefore, it is better to have a framework that can help SQL skilled people to write MapReduce jobs in an SQL-like language. Apache Pig was invented for this purpose. It also solved the complexity of writing multiple MapReduce pipeline jobs where output of one job becomes the input to another.  the

Note

Apache Pig is a distributed processing tool that is an abstraction over MapReduce and is used to process large datasets representing data flows. Apache Pig on Apache Spark is also an option that the open source...