Data Algorithms: Recipes for Scaling Up with Hadoop and by Mahmoud Parsian

By Mahmoud Parsian

While you're able to dive into the MapReduce framework for processing huge datasets, this functional e-book takes you step-by-step during the algorithms and instruments you must construct dispensed MapReduce purposes with Apache Hadoop or Apache Spark. every one bankruptcy presents a recipe for fixing a major computational challenge, similar to construction a advice process. You'll how one can enforce the precise MapReduce resolution with code so that you can use on your projects.

Dr. Mahmoud Parsian covers simple layout styles, optimization suggestions, and knowledge mining and computer studying options for difficulties in bioinformatics, genomics, records, and social community research. This booklet additionally contains an outline of MapReduce, Hadoop, and Spark.

Topics include:
•Market basket research for a wide set of transactions
•Data mining algorithms (K-means, KNN, and Naive Bayes)
•Using large genomic facts to series DNA and RNA
•Naive Bayes theorem and Markov chains for information and marketplace prediction
•Recommendation algorithms and pairwise rfile similarity
•Linear regression, Cox regression, and Pearson correlation
•Allelic frequency and mining DNA
•Social community research (recommendation structures, counting triangles, sentiment analysis)

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Example 1-5. append(","); 10 } 11 emit(key, sortedTemperatureList); 12 } Hadoop Implementation Classes The classes shown in Table 1-1 are used to solve the problem. Table 1-1. Classes used in MapReduce/Hadoop solution Class name Class description SecondarySortDriver The driver class; defines input/output and registers plug-in classes SecondarySortMapper Defines the map() function SecondarySortReducer Defines the reduce() function DateTemperatureGroupingComparator Defines how keys will be grouped together DateTemperaturePair Defines paired date and temperature as a Java object DateTemperaturePartitioner Defines custom partitioner How is the value injected into the key?

Phanen‐ dra Babu, Willy Bruns, and Mohan Reddy. Your comments were useful, and I have incorporated your suggestions as much as possible. Special thanks to Cody for pro‐ viding detailed feedback. A big thank you to Jay Flatley (CEO of Illumina), who has provided a tremendous opportunity and environment in which to unlock the power of the genome. Thank you to my dear friends Saeid Akhtari (CEO, NextBio) and Dr. Satnam Alag (VP of Engineering at Illumina) for believing in me and supporting me for the past five years.

Parallelism Computations are executed on a cluster of nodes in parallel. Hadoop is designed mainly for batch processing, while with enough memory/RAM, Spark may be used for near real-time processing. To understand basic usage of Spark RDDs (resilient distributed data sets), see Appendix B. So what are the core components of MapReduce/Hadoop? xxviii | Preface • Input/output data consists of key-value pairs. ). • Data is partitioned over commodity nodes, filling racks in a data center. • The software handles failures, restarts, and other interruptions.

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