Book Image

Mastering Apache Cassandra

By : Nishant Neeraj
Book Image

Mastering Apache Cassandra

By: Nishant Neeraj

Overview of this book

<p>Apache Cassandra is the perfect choice for building fault tolerant and scalable databases. Implementing Cassandra will enable you to take advantage of its features which include replication of data across multiple datacenters with lower latency rates. This book details these features that will guide you towards mastering the art of building high performing databases without compromising on performance.</p> <p>Mastering Apache Cassandra aims to give enough knowledge to enable you to program pragmatically and help you understand the limitations of Cassandra. You will also learn how to deploy a production setup and monitor it, understand what happens under the hood, and how to optimize and integrate it with other software.</p> <p>Mastering Apache Cassandra begins with a discussion on understanding Cassandra’s philosophy and design decisions while helping you understand how you can implement it to resolve business issues and run complex applications simultaneously.</p> <p>You will also get to know about how various components of Cassandra work with each other to give a robust distributed system. The different mechanisms that it provides to solve old problems in new ways are not as twisted as they seem; Cassandra is all about simplicity. Learn how to set up a cluster that can face a tornado of data reads and writes without wincing.</p> <p>If you are a beginner, you can use the examples to help you play around with Cassandra and test the water. If you are at an intermediate level, you may prefer to use this guide to help you dive into the architecture. To a DevOp, this book will help you manage and optimize your infrastructure. To a CTO, this book will help you unleash the power of Cassandra and discover the resources that it requires.</p>
Table of Contents (17 chapters)
Mastering Apache Cassandra
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Hadoop and Cassandra


In the age of Big Data analytics, there are hardly any data-rich companies that do not want their data to be extracted, evaluated, and inferred to provide more business inside. In the past, analyzing large data sets (structured or unstructured) that span terabytes or petabytes used to be expensive and a technically challenging task to a team; distributed computing was harder to keep track of, and hardware to support this kind of infrastructure was not financially feasible to everyone.

What changed the demography completely in favor of medium and small companies are a couple of things. Hardware prices dropped down to earth. Memories and processing powers of computing units increased dramatically at the same time. Hardware on demand came into the picture. You can spend about 20 dollars to rent about a 100 virtual machines with quad-core (virtual) processors, 7.5 GB RAM, and 840 GB of ephemeral storage (you can plug in gigantic network-attached storages that are permanent...