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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Build an efficient, scalable, fault-tolerant, and highly-available data layer into your application using Cassandra

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Product type Paperback
Published in Feb 2015
Publisher
ISBN-13 9781783989201
Length 246 pages
Edition 1st Edition
Languages
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Author (1):
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 Brown Brown
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Brown
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Table of Contents (19) Chapters Close

Learning Apache Cassandra
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Up and Running with Cassandra FREE CHAPTER 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 Peeking Under the Hood Authentication and Authorization Index

Chapter 10. How Cassandra Distributes Data

Much of Cassandra's power lies in the fact that it is a distributed database: rather than storing all of your data on a single machine, it is designed to distribute data across multiple machines. A distributed architecture is hugely beneficial for scalability since you're not bound by the hardware capacity of a single machine; if you need more storage or more processing power, you can simply add more nodes to your Cassandra cluster. It's also a boon for availability: by storing multiple copies of your data on multiple machines, Cassandra is resilient to the failure of a particular node.

The beauty of a distributed database such as Cassandra is that, as application developers, we rarely need to think about the fact that we're working with data that's spread across multiple servers. We've spent the last nine chapters exploring a wide range of Cassandra's functionality, and the interfaces we've worked with never require us to explicitly account for...

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