Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library, 2nd Edition

Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library
Author: Hien Luu

Publisher Finelybook 出版社:Apress; 2nd ed. edition (October 23, 2021)
Language 语言:English
pages 页数:456 pages
ISBN-10 书号:1484273826
ISBN-13 书号:9781484273821

Book Description
Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications.

Beginning Apache Spark 3 begins Author: explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed Author: an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section.

After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications.

What You Will Learn

Master the Spark unified data analytics engine and its various components
Work in tandem to provide a scalable, fault tolerant and performant data processing engine
Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL
Develop machine learning applications using Spark MLlib
Manage the machine learning development lifecycle using MLflow

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