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"Data Algorithms with Spark" Book By Mahmoud Parsian

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A hands-on guide offering practical PySpark algorithms and examples for anyone new to Spark.
Learn recipes and design patterns for scaling data processing with Apache Spark clusters.

Data Algorithms with Spark shows how to solve data problems using sets of Spark transformations and algorithms. This book is a resource for data engineers and scientists who need practical knowledge of the Apache Spark cluster-computing framework. It provides detailed, real-world examples in PySpark for tackling problems in ETL, machine learning, graph data analysis, and genomics analysis. The goal is to provide simple, readable, and maintainable code solutions that can be easily reused in your projects.

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Key Features

Practical Algorithms: Learn practical algorithms and hands-on examples using the PySpark API to solve various data problems.
Optimized Solutions: Understand how to select the best Spark transformations for optimized solutions and improve algorithm efficiency through techniques like controlling data partitioning.
Real-World Applications: The book covers diverse applications, including motif-finding algorithms for graph data, clinical and genomics data analysis, and feature engineering for machine learning models.
Design Patterns: Learn practical and pragmatic data design patterns for effective use and application in large-scale data analytics.
Code Examples: The book provides working code examples written in PySpark that can be directly used as a starting point for similar computations.