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Opis

This book gives a deep insight into leveraging Phoenix as an ANSI SQL engine built on top of highly distributed scalable No-SQL framework HBase. The author takes deep dive into phoenix while starting with basics and describes the best practices that are being adopted in Phoenix to enable a high write and read throughput in big data space. The book will be written with many real world use cases like IOT devices sending continuous streams to Phoenix and through which explain how key features like Joins, Indexes, Transactions and Functions enable readers understand the simple, flexible yet powerful API that Phoenix provides. The examples will look into use cases like Real time data, Data driven businesses, which need to collect, analyze and act in seconds. The book will also delve into the nuances of setting up a distributed HBase cluster with Phoenix libraries, running performance benchmarks, configuring parameters for production scenarios and the ability to view the results. Finally, We will be covering how Phoenix plays well with other key frameworks in the Hadoop ecosystem like Apache Spark, Pig, Flume and Sqoop. Readership Data Engineers, Big Data Administrators and Architects What you will learn: 1. How to handle petabyte data store by applying familiar SQL techniques 2. Storing, Analyzing and manipulating data in NoSQL Hadoop echo system with HBase 3. Applying Best Practices while working with scalable data store on Hadoop and HBase 4. Integrating popular framework (Apache Spark, Pig, Flume) to simplify big data analysis 5. Demonstrating Real time use cases and big data modeling techniques

Szczegóły książki

Kategoria Książki po angielsku Computing & information technology Computer programming / software development

• Pełny tytuł: Pro Apache Phoenix
• Podtytuł: An SQL Driver for HBase
• Autor: Shakil Akhtar, Ravi Magham
• Język: Angielski
• Oprawa: Miękka
• Liczba stron: 140
• EAN: 9781484223697
• ISBN: 1484223691
• ID: 14429892
• Wydawca: APress
• Waga: 76 g
• Wymiary: 197 × 134 × 7 mm
• Data wydania: 30. December 2016

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