This Book is in Good Condition. Clean Copy With Light Amount of Wear. 100% Guaranteed. Summary: Learn the algorithms and tools you need to build MapReduce applications with Hadoop for processing gigabyte, terabyte, or petabyte-sized datasets on clusters of commodity hardware. With this practical book, Author Mahmoud Parsian, head of the big data team at Illumina, takes you step-by-step through the design of machine-learning algorithms, such as Naive Bayes and Markov Chain, and shows you how apply them to clinical and biological datasets, using MapReduce design patterns.Apply MapReduce algorithms to clinical and biological data, such as DNA-Seq and RNA-SeqUse the most relevant regression/analytical algorithms used for different biological data types. Bookseller Inventory #
If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects.
Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark.
About the Author:
Mahmoud Parsian, Ph.D. in Computer Science, is a practicingsoftware professional with 30 years of experience as a developer,designer, architect, and author. For the past 15 years, he hasbeen involved in Java server-side, databases, MapReduce, anddistributed computing. Dr. Parsian currently leads Illumina'sBig Data team, which is focused on large-scale genome analyticsand distributed computing. He leads and develops scalableregression algorithms; DNA sequencing and RNA sequencing pipelinesusing Java, MapReduce, Hadoop, HBase, and Spark; and open sourcetools. He is also the author of JDBC Recipes and JDBC Metadata (bothfrom Apress).
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