Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

Data Algorithms: Recipes for Scaling Up with Hadoop and Spark
  • eBook:
    Data Algorithms: Recipes for Scaling Up with Hadoop and Spark
  • Author:
    Mahmoud Parsian
  • Edition:
    1 edition
  • Categories:
  • Data:
    August 1, 2015
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    778 pages
  • Format:
    PDF, ePUB

Book Description
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.
Topics include:
  • Market basket analysis for a large set of transactions
  • Data mining algorithms (K-means, KNN, and Naive Bayes)
  • Using huge genomic data to sequence DNA and RNA
  • Naive Bayes theorem and Markov chains for data and market prediction
  • Recommendation algorithms and pairwise document similarity
  • Linear regression, Cox regression, and Pearson correlation
  • Allelic frequency and mining DNA
  • Social network analysis (recommendation systems, counting triangles, sentiment analysis)


1. Secondary Sort: Introduction
2. Secondary Sort: A Detailed Example
3. Top 10 List
4. Left Outer Join
5. Order Inversion
6. Moving Average
7. Market Basket Analysis
8. Common Friends
9. Recommendation Engines Using MapReduce
10. Content-Based Recommendation: Movies
11. Smarter Email Marketing with the Markov Model
12. K-Means Clustering
13. k-Nearest Neighbors
14. Naive Bayes
15. Sentiment Analysis
16. Finding, Counting, and Listing All Triangles in Large Graphs
17. K-mer Counting
18. DNA Sequencing
19. Cox Regression
20. Cochran-Armitage Test for Trend
21. Allelic Frequency
22. The T-Test
23. Pearson Correlation
24. DNA Base Count
25. RNA Sequencing
26. Gene Aggregation
27. Linear Regression
28. MapReduce and Monoids
29. The Small Files Problem
30. Huge Cache for MapReduce
31. The Bloom Filter

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