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Mat Brown - Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data

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Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data: summary, description and annotation

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Key Features
  • Learn how to install and use Cassandra from the ground up
  • Design rich schemas that capture the relationships between different data types using compound primary keys
  • Master the advanced features available in Cassandra 2.0 through a step-by-step tutorial that builds a real-world applications database layer.
Book Description

Cassandra is a distributed database that stands out for its robust feature set and intuitive interface, while still providing the high availability and scalability of a distributed store.

Starting from installing Cassandra and creating your first keyspace, to mastering the different table structures Cassandra offers and exploring the latest and most powerful features of the Cassandra Query Language, CQL3, this book explores each topic through the lens of a real-world example application. With plenty of examples, tips, and clear explanations, youll master compound primary keys, collection columns, lightweight transactions, and many other advanced aspects of Cassandra.

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

What you will learn
  • Install Cassandra and create your first keyspace
  • Choose the right table structure for the task at hand in a variety of scenarios
  • Use range slice queries for efficient data access
  • Effortlessly handle concurrent updates with collection columns
  • Ensure data integrity with lightweight transactions and logged batches
  • Understand eventual consistency and use the right consistency level for your situation
  • Implement best practices for data modeling and access
About the Author

Mat Brown is a professional software engineer in Brooklyn, New York. In his career, he has focused on building consumer-facing web and mobile applications for several companies; he currently works at Genius.

Table of Contents
  1. Getting up and Running with Cassandra
  2. The First Table
  3. Organizing Related Data
  4. Beyond Key-value Lookup
  5. Establishing Relationships
  6. Denormalizing Data for Maximum Performance
  7. Expanding Your Data Model
  8. Collections, Tuples, and User-defined Types
  9. Aggregating Time-series Data
  10. How Cassandra Distributes Data

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Learning Apache Cassandra

Table of Contents
Learning Apache Cassandra

Learning Apache Cassandra

Copyright 2015 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: February 2015

Production reference: 1190215

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78398-920-1

www.packtpub.com

Credits

Author

Mat Brown

Reviewers

N. Bharadwaj

Mark Kerzner

Michael Klishin

Sanjay Sharma

Commissioning Editor

Akram Hussain

Acquisition Editor

Rebecca You

Content Development Editor

Ritika Singh

Sharvari Tawde

Technical Editor

Ruchi Desai

Copy Editors

Pranjali Chury

Merilyn Pereira

Project Coordinator

Judie Jose

Proofreaders

Simran Bhogal

Stephen Copestake

Kelly Hutchinson

Indexer

Monica Ajmera Mehta

Graphics

Sheetal Aute

Disha Haria

Production Coordinator

Conidon Miranda

Cover Work

Conidon Miranda

About the Author

Mat Brown is a professional software engineer in Brooklyn, New York. In his career, he has focused on building consumer-facing web and mobile applications for several companies; he currently works at Genius.

Mat is an enthusiastic contributor to the Ruby open source ecosystem. He is the maintainer of Cequel, a Ruby object mapper for Cassandra, and was the original author of Sunspot, a library that seamlessly integrates Solr search into Rails applications.

I would like to thank my wife, Pamela, and my parents for their love and support. I'd also like to thank my friends, especially those who seemed impressed when I told them I was writing a book. My cat, Taco, though not good for much, did keep me company during some writing sessions and thus deserves a mention here as well.

About the Reviewers

N. Bharadwaj is a software developer at Glassbeam Inc. He has over 12 years of experience in enterprise product development and managing development teams. His experience covers software design and development in the areas of network-data and application-data analysis. He counts Scala and Java among his primary development languages, and PostgreSQL and Cassandra among the databases he is experienced with. Bharadwaj's interests include building high scalability systems, distributed computing, and machine learning. He received his bachelor of engineering degree in electronics and communication systems from Visvesvaraya Technological University, Karnataka, in 2002 and his MBA from the Indian Institute of Management in 2012.

Mark Kerzner holds degrees in law, math, and computer science. He has been designing software for many years and has been working on Hadoop-based systems since 2008. He is the president of SHMsoft, a provider of Hadoop applications for various verticals, a cofounder of Elephant Scale, a training and consulting company, as well as the coauthor of the open source book, Hadoop Illuminated . His book HBase Designed Patterns , written together with Sujee Maniyam, was recently published by Packt Publishing.

I would like to acknowledge the help of my colleagues, in particular, Sujee Maniyam, and last but not least, my multitalented family.

Michael Klishin is an experienced software engineer primarily working on data infrastructure. A long-time open source contributor, he maintains over 50 open source projects.

Sanjay Sharma has been building enterprise-grade solutions in the software industry for around 16 years and using Big Data and Cloud technologies over the past 5 or 6 years to solve complex business problems. He has extensive experience with cardinal technologies, including Cassandra, Hadoop, Hive, MongoDB, MPP DW, and Java/J2EE/SOA, which allowed him to pioneer the LinkedIn group Hadoop India.

I would like to thank my employerImpetus and iLabs, especially its R&D department, which invests in cutting-edge technologies and allowed me to become a pioneer in mastering Cassandra and Hadoop-like technologies early on.

Most importantly, I want to acknowledge my wonderful familymy wife and sonwho have always supported and encouraged me in all my endeavors in life.

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Preface

The crop of distributed databases that has come to the market in recent years appeals to application developers for several reasons. Their storage capacity is nearly limitless, bounded only by the number of machines you can afford to spin up. Masterless replication makes them resilient to adverse events, handling even a complete machine failure without any noticeable effect on the applications that rely on them. Log-structured storage engines allow these databases to handle high-volume write loads without blinking an eye.

But compared to traditional relational databases, not to mention newer document stores, distributed databases are typically feature-poor and inconvenient to work with. Read and write functionality is frequently confined to simple key-value operations, with more complex operations demanding arcane map-reduce implementations. Happily, Cassandra provides all of the benefits of a fully-distributed data store while also exposing a familiar, user-friendly data model and query interface.

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