Learning Apache Cassandra - Second Edition

Yarabarla, Sandeep

ISBN 10: 178712729X / ISBN 13: 9781787127296

Published by Packt Publishing, 2017

About this Item

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Seller Inventory # ERICA765178712729X5

Bibliographic Details

• Title: Learning Apache Cassandra - Second Edition
• Publisher: Packt Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 178712729X
• ISBN 13: 9781787127296
• Binding: Paperback
• Edition: 2nd Edition
• Condition: New
• Book Type: book

About this title

Synopsis

Key Features

• Install Cassandra and set up multi-node clusters
• Design rich schemas that capture the relationships between different data types
• Master the advanced features available in Cassandra 3.x through a step-by-step tutorial and build a scalable, high performance database layer

Book Description

Cassandra is a distributed database that stands out thanks to its robust feature set and intuitive interface, while providing high availability and scalability of a distributed data store. This book will introduce you to the rich feature set offered by Cassandra, and empower you to create and manage a highly scalable, performant and fault-tolerant database layer.

The book starts by explaining the new features implemented in Cassandra 3.x and get you set up with Cassandra. Then you'll walk through data modeling in Cassandra and the rich feature set available to design a flexible schema. Next you'll learn to create tables with composite partition keys, collections and user-defined types and get to know different methods to avoid denormalization of data. You will then proceed to create user-defined functions and aggregates in Cassandra. Then, you will set up a multi node cluster and see how the dynamics of Cassandra change with it. Finally, you will implement some application-level optimizations using a Java client.

By the end of this book, you'll be fully equipped to build powerful, scalable Cassandra database layers for your applications.

What you will learn

• Install Cassandra
• Create keyspaces and tables with multiple clustering columns to organize related data
• Use secondary indexes and materialized views to avoid denormalization of data

About the Author

Sandeep Yarabarla is a professional software engineer working for Verizon Labs, based out of Palo Alto, CA. After graduating from Carnegie Mellon University, he has worked on several big data technologies for a spectrum of companies. He has developed applications primarily in Java and Go.His experience includes handling large amounts of unstructured and structured data in Hadoop, and developing data processing applications using Spark and MapReduce. Right now, he is working with some cutting-edge technologies such as Cassandra, Kafka, Mesos, and Docker to build fault-tolerant and highly scalable applications.