Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library - Softcover

Über die Autorin bzw. den Autor

Hien Luu has extensive experience in designing and building big data applications and machine learning infrastructure. He is particularly passionate about the intersection between big data and machine learning. Hien enjoys working with open source software and has contributed to Apache Pig and Azkaban. Teaching is also one of his passions, and he serves as an instructor at the UCSC Silicon Valley Extension school teaching Apache Spark. He has given presentations at various conferences such as Data+AI Summit, MLOps World, QCon SF, QCon London, Hadoop Summit, and JavaOne.

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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.

You will:

• 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