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Data Algorithms with Spark : Recipes and Design Patterns for Scaling up Using Pyspark by Mahmoud Parsian (2022, Trade Paperback)

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About this product

Product Identifiers

PublisherO'reilly Media, Incorporated

ISBN-101492082384

ISBN-139781492082385

eBay Product ID (ePID)7050106159

Product Key Features

Number of Pages500 Pages

LanguageEnglish

Publication NameData Algorithms with Spark : Recipes and Design Patterns for Scaling Up Using Pyspark

Publication Year2022

SubjectProgramming / Open Source, General, Data Processing, Programming Languages / Python

TypeTextbook

Subject AreaMathematics, Computers

AuthorMahmoud Parsian

FormatTrade Paperback

Dimensions

Item Height0.9 in

Item Weight26.5 Oz

Item Length9.2 in

Item Width7.3 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2022-300414

Dewey Edition23

IllustratedYes

Dewey Decimal005.1

SynopsisApache 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

LC Classification NumberQA76.9.A43