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

Managing resources


Resource management is a continuous process for either on-premise infrastructures or infrastructure on the cloud. The instance you deploy, the cluster you spin up, and the storage you use all have to be continuously monitored and managed by the infrastructure team. Sometimes it may happen that you need to attach a volume to the already running instance, you may need to add an extra node to your distributed processing databases, or you may need to add instances to handle large traffic coming to the load balancer. The other resource management work includes configuration management, such as changing firewall rules, adding new users to access the resources, adding new rules to access other resources from the current resource, and so on. 

Initially due to a lack of good GUI interfaces and available tools for monitoring and managing resources, it was managed using custom scripts or commands. Today almost all cloud providers have well augmented graphical user interfaces and tools...