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

Data compression


Many of us have been working on many big data projects and have used a wide range of frameworks and tools to solve customer problems. Bringing the data to distributed storage is the first step of data processing. If you have ever observed that in the case of Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT), the first step is to extract the data and bring it in for processing. A storage system has a cost associated with it and we always want to store more data in less storage space. The big data processing happens over massive amounts of data, which may cause I/O and network bottlenecks. The shuffling of data across the network is always a painful, time-consuming process that burns significant amounts of processing time.

 

 

Here is how compression can help us in different ways:

  • Less storage: A storage system comes with a significant amount of cost associated with it. Companies are moving toward the cloud, and even if we have to pay less for storage over the cloud...