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  • Book Overview & Buying Mastering Apache Cassandra - Second Edition
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Mastering Apache Cassandra - Second Edition

Mastering Apache Cassandra - Second Edition

By : Nishant Neeraj
4.6 (10)
Buy this Book
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Mastering Apache Cassandra - Second Edition

Mastering Apache Cassandra - Second Edition

4.6 (10)
By: Nishant Neeraj
Buy this Book

Overview of this book

The book is aimed at intermediate developers with an understanding of core database concepts who want to become a master at implementing Cassandra for their application.
Table of Contents (10 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Quick Start
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1. Quick Start
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Introduction to Cassandra
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A brief introduction to a data model
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Installing Cassandra locally
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Cassandra in action
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Summary
2
2. Cassandra Architecture
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2. Cassandra Architecture
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Problems in the RDBMS world
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Enter NoSQL
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Cassandra
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Understanding the architecture of Cassandra
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Summary
3
3. Effective CQL
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3. Effective CQL
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The Cassandra data model
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CQL shell commands
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Summary
4
4. Deploying a Cluster
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4. Deploying a Cluster
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Evaluating requirements
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System configurations
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The required software
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Installing Cassandra
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Configuring a Cassandra cluster
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Authorization and authentication
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Summary
5
5. Performance Tuning
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5. Performance Tuning
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Stress testing
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Performance tuning
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Summary
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6. Managing a Cluster – Scaling, Node Repair, and Backup
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6. Managing a Cluster – Scaling, Node Repair, and Backup
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Scaling
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Replacing a node
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Backup and restoration
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Load balancing
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DataStax OpsCenter – managing large clusters
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Summary
7
7. Monitoring
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7. Monitoring
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Cassandra's JMX interface
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Cassandra's nodetool utility
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DataStax OpsCenter
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Nagios – monitoring and notification
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Cassandra log
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Troubleshooting
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Summary
8
8. Integration with Hadoop
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8. Integration with Hadoop
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Using Hadoop
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Hadoop and Cassandra
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Cassandra with Hadoop MapReduce
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Cassandra and Hadoop in action
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Hadoop in a Cassandra cluster
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Integration with Pig
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Summary
9
Index
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Index

Using Hadoop

Hadoop is for data processing. You may ask "So are MATLAB, R, Octave, Python (NLTK and many other libraries for data analysis), and SAS, then why Hadoop". They are great tools, but they are good for data that can fit in memory. It means that you can churn a couple of GBs to maybe 10s of GBs, and the rate of processing depends on the CPU on that machine, maybe 16 cores. This poses a big restriction. The data is no more in GB limits at the Internet scale. In the age of billions of mobile phones (there were an estimated 7.7 billion mobile users at the end of 2014, source: http://mobithinking.com/mobile-marketing-tools/latest-mobile-stats/a#subscribers), we are generating humongous amounts of data every second (Twitter reports 143,199 tweets per second, source: http://dazeinfo.com/2014/04/29/7-7-billion-mobile-devices-among-7-1-billion-world-population-end-2014/) by checking in places, tagging photos, uploading videos, commenting, messaging, purchasing, dining, running (fitness...

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Mastering Apache Cassandra - Second Edition
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