ApacheSpark的速度、易用性、复杂的分析和多语言支持使得对这个集群计算框架的实用知识成为数据工程师和数据科学家的必备技能。有了这本实践指南，任何想要了解Spark的人都将学习使用PySpark的实用算法和示例。
在每一章中，作者Mahmoud Parsian都会向您展示如何使用一组Spark变换和算法来解决数据问题。您将学习如何解决涉及ETL、设计模式、机器学习算法、数据分区和基因组学分析的问题。每个详细的配方都包括使用PySpark驱动程序和shell脚本的PySpark算法。
有了这本书，你会
•了解如何为优化解决方案选择Spark Transformation
•探索强大的转换和简化，包括reduceByKey（）、combineByKey（）和mapPartitions（）
•了解优化查询的数据分区
•使用PySpark设计模式构建并应用模型
•将motif查找算法应用于图形数据
•使用GraphFrames API分析图形数据
•将PySpark算法应用于临床和基因组数据
•学习如何在ML算法中使用和应用特征工程
•理解并使用实用的数据设计模式
Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up using PySpark (True EPUB)
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
• 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
• Learn how to use and apply feature engineering in ML algorithms
• Understand and use practical and pragmatic data design patterns
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