Book Image

Hands-on DevOps

By : Sricharan Vadapalli
Book Image

Hands-on DevOps

By: Sricharan Vadapalli

Overview of this book

<p>DevOps strategies have really become an important factor for big data environments.</p> <p>This book initially provides an introduction to big data, DevOps, and Cloud computing along with the need for DevOps strategies in big data environments. We move on to explore the adoption of DevOps frameworks and business scenarios. We then build a big data cluster, deploy it on the cloud, and explore DevOps activities such as CI/CD and containerization. Next, we cover big data concepts such as ETL for data sources, Hadoop clusters, and their applications. Towards the end of the book, we explore ERP applications useful for migrating to DevOps frameworks and examine a few case studies for migrating big data and prediction models.</p> <p>By the end of this book, you will have mastered implementing DevOps tools and strategies for your big data clusters.</p>
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
11
DevOps Adoption by ERP Systems
12
DevOps Periodic Table
13
Business Intelligence Trends
14
Testing Types and Levels
15
Java Platform SE 8

Big data Hadoop ecosystems


Apache Hadoop is an open source software platform built from commodity hardware and used to scale clusters up to terabytes or petabytes for big data, spanning across thousands of servers. It is highly popular and efficient for distributed data storage and distributed processing of very large datasets. Hadoop offers a full scale of services such as data persistence, data processing, data access, data governance, data security, and operations. A few of the benefits associated with Hadoop clusters are listed as follows:

  • Data scalability: Big data volumes can grow exponentially to accommodate these large data volumes, Hadoop enables distributed processing of data; each node in the data cluster participates in storing, managing, processing, and analyzing data. The addition of nodes enables quick scaling of clusters to store data at a scale of petabytes.
  • Data reliability: Hadoop cluster configurations provide data redundancy. For example, in case of accidental failure...