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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)
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Mastering Apache Cassandra - Second Edition

Mastering Apache Cassandra - Second Edition

4.6 (10)
By: Nishant Neeraj

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

Note

This chapter does not cover Cassandra integration with Hive and Oozie. To learn about Cassandra integration with Oozie, visit http://wiki.apache.org/cassandra/HadoopSupport#Oozie.

There are ongoing efforts to bring Hive integration to Cassandra as its native part. If you are planning to use Cassandra with Hive, visit https://issues.apache.org/jira/browse/CASSANDRA-4131.

DataStax Enterprise editions have built-in Cassandra-enabled Hive MapReduce clients. Check them out at http://www.datastax...

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