Data.Algorithms.Recipes.for.Scaling.Up.with.Hadoop.and.Spark.1

Learn the algorithms and tools you need to build MapReduce applications with Hadoop and Spark 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-stepthrough 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-Seq Use the most relevant regression/analytical algorithms used for different biological data types Apply t-test, joins, top-10, and correlation algorithms using MapReduce/Hadoop and Spark Table of Contents Chapter 1 Secondary Sort: Introduction Chapter 2 Secondary Sorting: Detailed Example Chapter 3 Top 10 List Chapter 4 Left Outer Join in MapReduce Chapter 5 Order Inversion Pattern Chapter 6 Moving Average Chapter 7 Market Basket Analysis Chapter 8 Common Friends Chapter 9 Recommendation Engines using MapReduce Chapter 10 Content-Based Recommendation: Movies Chapter 11 Smarter Email Marketing with Markov Model Chapter 12 K-Means Clustering Chapter 13 kNN: k-Nearest-Neighbors Chapter 14 Naive Bayes Chapter 15 Sentiment Analysis Chapter 16 Finding, Counting and Listing all Triangles in Large Graphs Chapter 17 K-mer Counting Chapter 18 DNA-Sequencing Chapter 19 Cox Regression Chapter 20 Cochran-Armitage Test for Trend Chapter 21 Allelic Frequency Chapter 22 The T-Test Chapter 23 Computing Pearson Correlation Chapter 24 DNA Base Count Chapter 25 RNA-Sequencing Chapter 26 Gene Aggregation Chapter 27 Linear Regression Chapter 28 MapReduce and Monoids Chapter 29 The Small Files Problem Chapter 30 Huge Cache for MapReduce Chapter 31 Bloom Filter