Mahmoud Parsian 
Data Algorithms with Spark 

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 Py Spark.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 Py Spark algorithms using the Py Spark driver and shell script.With this book, you will:Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduce By Key(), combine By Key(), and map Partitions()Understand data partitioning for optimized queries Build and apply a model using Py Spark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the Graph Frames APIApply Py Spark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns