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Leverage Phoenix as an ANSI SQL engine built on top of the highly distributed and scalable NoSQL framework HBase. Learn the basics and best practices that are being adopted in Phoenix to enable a high write and read throughput in a big data space. This book includes real-world cases such as Internet of Things devices that send continuous streams to Phoenix, and the book explains how key features such as joins, indexes, transactions, and functions help you understand the simple, flexible, and powerful API that Phoenix provides. Examples are provided using real-time data and data-driven businesses that show you how to collect, analyze, and act in seconds. Pro Apache Phoenix covers the nuances of setting up a distributed HBase cluster with Phoenix libraries, running performance benchmarks, configuring parameters for production scenarios, and viewing the results. The book also shows how Phoenix plays well with other key frameworks in the Hadoop ecosystem such as Apache Spark, Pig, Flume, and Sqoop. You will learn how to: Handle a petabyte data store by applying familiar SQL techniques Store, analyze, and manipulate data in a NoSQL Hadoop echo system with HBase Apply best practices while working with a scalable data store on Hadoop and HBase Integrate popular frameworks (Apache Spark, Pig, Flume) to simplify big data analysis Demonstrate real-time use cases and big data modeling techniques Who This Book Is For Data engineers, Big Data administrators, and architects.
This is a beginner level class for data analysts with RDBMS backgrounds looking to learn more about Big Data NoSQL solutions and the available SQL layers for Big Data. The course begins with a review of the performance characteristics of SQL systems vs. NoSQL systems, so you'll know how to structure data to get maximum performance from NoSQL solutions. It then moves into a detailed tutorial on how to use Apache Phoenix, the easy-to-use SQL "skin" layer for NoSQL HBase. Understand the performance characteristics of relational SQL systems vs. NoSQL systems Explore the use of Apache Phoenix, the SQL "skin" layer for working with NoSQL HBase Learn the fundamentals of HBase, and how to obtain and configure Apache Phoenix Discover why Phoenix interacts with HBase much easier than native HBase tools Learn to create Phoenix tables, load data, and execute queries against that data See how to retrieve data from Phoenix by using a JDBC connection Understand how to structure data to get maximum performance from NoSQL solutions Tom Hanlon is a professional technical trainer with 15+ years of experience teaching Hadoop, MapReduce, YARN, NoSQL, Big Data, distributed systems, machine learning, SQL, and more. He is a Certified Cloudera Developer for Apache Hadoop; he's held senior level training positions at Cloudera, Hortonworks, and Sun Microsystems; and is the author of the O'Reilly titles Introduction to Apache Hive and Learning Apache Pig.
Learn advanced analytical techniques and leverage existing tool kits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation. Pro Hadoop Data Analytics emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples. What You'll Learn Build big data analytic systems with the Hadoop ecosystem Use libraries, tool kits, and algorithms to make development easier and more effective Apply metrics to measure performance and efficiency of components and systems Connect to standard relational databases, noSQL data sources, and more Follow case studies with example components to create your own systems Who This Book Is For Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.
This book constitutes the refereed proceedings of the 21st International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2019, held in Linz, Austria, in September 2019. The 12 full papers and 10 short papers presented were carefully reviewed and selected from 61 submissions. The papers are organized in the following topical sections: Applications; patterns; RDF and streams; big data systems; graphs and machine learning; databases.
* Illustrates each concept with code samples in Java language; provides guidelines for different application-specific needs. * Describes the techniques to distribute the logging activity—critical to implement in an enterprise-wide logging framework. * The only Java Logging book on the market, and includes Best Practices guidelines.
This book consists of papers on the recent progresses in the state of the art in natural computation, fuzzy systems, and knowledge discovery. The book is useful for researchers, including professors, graduate students, as well as R & D staff in the industry, with a general interest in natural computation, fuzzy systems, and knowledge discovery. The work printed in this book was presented at the 2022 18th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery (ICNC-FSKD 2022), held from 30 July to 1 August 2022, in Fuzhou, China. All papers were rigorously peer-reviewed by experts in the areas.