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

By Mahmoud Parsian

When you are able to dive into the MapReduce framework for processing huge datasets, this sensible ebook takes you step-by-step during the algorithms and instruments you must construct allotted MapReduce functions with Apache Hadoop or Apache Spark. every one bankruptcy offers a recipe for fixing an incredible computational challenge, equivalent to construction a suggestion method. You'll how to enforce the proper MapReduce answer with code that you should use on your projects.

Dr. Mahmoud Parsian covers uncomplicated layout styles, optimization ideas, and knowledge mining and desktop studying ideas 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 info 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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Additional resources for Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

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Advisor), for his excellent guidance and for providing me with the environment to work on computer science. Thanks to my dear parents (mother Monireh Azemoun and father Bagher Parsian) for making education their number one priority. They have supported me tremen‐ dously. Thanks to my brother, Dr. Ahmad Parsian, for helping me to understand mathematics. Thanks to my sister, Nayer Azam Parsian, for helping me to understand compassion. Last, but not least, thanks to my dear family—Behnaz, Maral, and Yaseen—whose encouragement and support throughout the writing process means more than I can say.

To fully utilize Spark’s API, we have to under‐ stand RDDs. , an RDD of type T) object represents an immutable, par‐ titioned collection of elements (of type T) that can be operated on in parallel. The RDD class contains the basic MapReduce operations available on all RDDs, such as map(), filter(), and persist(), while the JavaPairRDD class contains MapRe‐ duce operations such as mapToPair(), flatMapToPair(), and groupByKey(). In addi‐ tion, Spark’s PairRDDFunctions contains operations available only on RDDs of keyvalue pairs, such as reduce(), groupByKey(), and join().

Likewise, this book will not discuss Hadoop itself in detail; Tom White’s excellent book[31] does that very well. This book will not cover how to install Hadoop or Spark; I am going to assume you already have these installed. Also, any Hadoop commands are executed relative to the directory where Hadoop is installed (the $HADOOP_HOME environment variable). This book is explicitly about presenting distributed algorithms using MapReduce/Hadoop and Spark. For example, I discuss APIs, cover command-line invocations for running jobs, and provide complete working programs (including the driver, mapper, combiner, and reducer).

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