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Mastering Apache Cassandra 3.x

You're reading from   Mastering Apache Cassandra 3.x An expert guide to improving database scalability and availability without compromising performance

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789131499
Length 348 pages
Edition 3rd Edition
Languages
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Authors (2):
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Aaron Ploetz Aaron Ploetz
Author Profile Icon Aaron Ploetz
Aaron Ploetz
Tejaswi Malepati Tejaswi Malepati
Author Profile Icon Tejaswi Malepati
Tejaswi Malepati
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Table of Contents (12) Chapters Close

Preface 1. Quick Start FREE CHAPTER 2. Cassandra Architecture 3. Effective CQL 4. Configuring a Cluster 5. Performance Tuning 6. Managing a Cluster 7. Monitoring 8. Application Development 9. Integration with Apache Spark 10. References 11. Other Books You May Enjoy

Spark

Spark is a powerful, open source, general-purpose, unified cluster-computing analytics framework for large-scale data-processing. It's known for high performance, in-memory processing with an efficient engine and query optimizer. The four most widely-used interpreters for Spark are Python, Scala, Java, and R, including their interactive CLI. Spark is built on a foundation of Resilient Distributed Dataset (RDD) spread across the cluster of nodes. This eliminates computational limitations due to a cap of maximum resources that can be on a single machine, theoretically making it an infinitely scalable system. With all this, it is no surprise that it is the largest open source project in the data-processing community. Refer to Apache Spark docs for further information at Spark-Spark Overview: http://spark.apache.org/docs/2.3.1/.

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