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Data algorithms with Spark :recipes and design patterns for scaling up using PySpark /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Data algorithms with Spark :/ Mahmoud Parsian.
其他題名:	recipes and design patterns for scaling up using PySpark /
作者:	Parsian, Mahmoud.
出版者:	Sebastopol, CA :O'Reilly Media, Inc.,c2022.
面頁冊數:	xx, 412,[1] p. :ill. ;23 cm.
附註:	Includes index.
標題:	Data mining. -
ISBN:	9781492082385

Data algorithms with Spark :recipes and design patterns for scaling up using PySpark /
Parsian, Mahmoud.

Data algorithms with Spark :recipes and design patterns for scaling up using PySpark /Mahmoud Parsian. - [1st ed.]. - Sebastopol, CA :O'Reilly Media, Inc.,c2022. - xx, 412,[1] p. :ill. ;23 cm.

Includes index.

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.

ISBN: 9781492082385NT2084Subjects--Uniform Titles:

SPARK (Electronic resource)
Subjects--Topical Terms:

147024
Data mining.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.133

Data algorithms with Spark :recipes and design patterns for scaling up using PySpark /
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300 $a xx, 412,[1] p. : $b ill. ; $c 23 cm.
500 $a Includes index.
520 $a 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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650 0 $a Data mining. $3 147024
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