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Mastering Apache Cassandra 3.x. An expert guide to improving database scalability and availability without compromising performance - Third Edition

(ebook) (audiobook) (audiobook) Książka w języku 1
Mastering Apache Cassandra 3.x. An expert guide to improving database scalability and availability without compromising performance - Third Edition Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj - okladka książki

Mastering Apache Cassandra 3.x. An expert guide to improving database scalability and availability without compromising performance - Third Edition Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj - okladka książki

Mastering Apache Cassandra 3.x. An expert guide to improving database scalability and availability without compromising performance - Third Edition Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj - audiobook MP3

Mastering Apache Cassandra 3.x. An expert guide to improving database scalability and availability without compromising performance - Third Edition Aaron Ploetz, Tejaswi Malepati, Nishant Neeraj - audiobook CD

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With ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features.

Once you’ve covered a brief recap of the basics, you’ll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You’ll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You’ll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apache Spark.

By the end of this book, you’ll be able to analyse big data, and build and manage high-performance databases for your application.

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O autorach książki

Aaron Ploetz is the NoSQL Engineering Lead for Target, where his DevOps team supports Cassandra, MongoDB, and Neo4j. He has been named a DataStax MVP for Apache Cassandra three times and has presented at multiple events, including the DataStax Summit and Data Day Texas. Aaron earned a BS in Management/Computer Systems from the University of Wisconsin-Whitewater, and an MS in Software Engineering from Regis University. He and his wife, Coriene, live with their three children in the Twin Cities area.
Tejaswi Malepati is the Cassandra Tech Lead for Target. He has been instrumental in designing and building custom Cassandra integrations, including a web-based SQL interface and data validation frameworks between Oracle and Cassandra. Tejaswi earned a master's degree in computer science from the University of New Mexico, and a bachelor's degree in electronics and communication from Jawaharlal Nehru Technological University in India. He is passionate about identifying and analyzing data patterns in datasets using R, Python, Spark, Cassandra, and MySQL.
Nishant Neeraj (https://naishe.in) is a software engineer at the BrightContext corporation. He builds software that can handle massive in-stream data, process it, and store it reliably, efficiently, and most importantly, quickly. He also manages the cloud infrastructure and makes sure that things stay up no matter what hit the data center in the middle of hardware failures and sudden surges of data inflow.



He has six years of experience in building web applications in Java as a backend engineer. He has been using Cassandra in production-ready web applications since Version 0.6 in 2010. His interests lie in building scalable applications for large data sets. He works with Java, MySQL, Cassandra, Twitter Storm, Amazon Web Services, JavaScript, and Linux on a daily basis, and he has recently developed an interest in Machine Learning, Data Analysis, and Data Science in general.

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