Data Algorithms with Spark

Recipes and Design Patterns for Scaling Up using PySpark

Author: Mahmoud Parsian

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9789355420787
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    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
    Books
    Author
    Mahmoud Parsian
    Binding
    Paperback
    Condition Type
    New
    Country Origin
    India
    Edition
    First
    Gift Wrap
    N
    Lead time to ship in days (if not in stock)
    ships in 12-15 days
    Leadtime to ship in days (default)
    ships in 1-2 days
    Pages
    440
    Publication Date
    27/04/2022
    Publisher
    Shroff/O'Reilly
    Year
    2022
    Dimensions
    Dimensions (W x H x D)
    7 x 9 x 0.85 inch

    Table of Contents (9789355420787_toc.pdf, 242 Kb) [Download]

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