Data Algorithms with Spark - Recipes and Design Patterns for Scaling Up using PySpark

Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark.
In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script.
With this book, you will:

[*]Learn how to select Spark transformations for optimized solutions
[*]Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions()
[*]Understand data partitioning for optimized queries
[*]Design machine learning algorithms including Naive Bayes, linear regression, and logistic regression
[*]Build and apply a model using PySpark design patterns
[*]Apply motif-finding algorithms to graph data
[*]Analyze graph data by using the GraphFrames API
[*]Apply PySpark algorithms to clinical and genomics data (such as DNA-Seq)

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