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Beginning Apache Spark 3 - With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library (Paperback, 2nd ed.)
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Beginning Apache Spark 3 - With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library (Paperback, 2nd ed.)
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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 by
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 by 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 Who This Book Is For Data scientists, data engineers and
software developers.
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