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Data Algorithms: Recipes for Scaling Up with Hadoop and Spark, by Mahmoud Parsian
Fee Download Data Algorithms: Recipes for Scaling Up with Hadoop and Spark, by Mahmoud Parsian
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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)
- Sales Rank: #383986 in eBooks
- Published on: 2015-07-13
- Released on: 2015-07-13
- Format: Kindle eBook
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).
Most helpful customer reviews
19 of 19 people found the following review helpful.
Lots of code snippets, not too good in concepts.
By Dimitri K
The book provides examples of how to implement some simple data analysis jobs using Map-reduce and to some extent Spark. Hadoop is probably 3/4 of the content. As for data analysis, the author does not show any deep knowledge or interest in explaining the methods. They are however pretty basic. The author concentrates on the code, which is in Java. Some examples are in Ruby, though, don't know why. There are lots of code snippets, which mostly fill in 700 pages of the book. The map-reduce patterns themselves are pretty simple, and are much better and more thoroughly explained in other books. In general, the book is like a large hadoop project with simple analytics, decomposed into snippets, with some not-too-clear discussion, which takes 700 pages.
To summarize, if you have enough brain to understand analytics and map-reduce patterns, I would rather recommend reading other books. If you have the same problems to implement as those in the book, and like copy-pasting as the method of development, you can use this book as a source.
0 of 0 people found the following review helpful.
Good stuff
By sri
its a pretty good book covering all day to day use cases which we come across in Hadoop real world projects, I totally recommend this book for someone who is looking for real world solutions.
0 of 0 people found the following review helpful.
Great book to understand MapReduce
By Razvan Popovici
The book focuses on MapReduce programming, with a lot of examples of distributed computing implemented for Spark and Hadoop. It provides tangible problems as well as their solutions. The book is a great read to upgrade the Spark skills. Also I assume the book is good if you try to learn Hadoop with no prior knowledge of MapReduce.
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