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

Parsian, Mahmoud

ISBN 10: 1492082384 / ISBN 13: 9781492082385

Verlag: O'Reilly Media, 2022

Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Bestandsnummer des Verkäufers 00079247471

Inhaltsangabe:

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

Über die Autorin bzw. den Autor: Mahmoud Parsian, Ph.D. in Computer Science, is a practicing software professional with 30 years of experience as a developer, designer, architect, and author. For the past 15 years, he has been involved in Java server-side, databases, MapReduce, Spark, PySpark, and distributed computing.

Bibliografische Details

Titel: Data Algorithms with Spark: Recipes and ...
Verlag: O'Reilly Media
Erscheinungsdatum: 2022
Einband: Softcover
Zustand: Good