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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Managing fault-tolerant, scalable data with high performance

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Product type Paperback
Published in Apr 2017
Publisher
ISBN-13 9781787127296
Length 360 pages
Edition 2nd Edition
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Author (1):
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Sandeep Yarabarla Sandeep Yarabarla
Author Profile Icon Sandeep Yarabarla
Sandeep Yarabarla
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Table of Contents (15) Chapters Close

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 11. Cassandra Multi-Node Cluster 12. Application Development Using the Java Driver 13. Peeking under the Hood 14. Authentication and Authorization

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...

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