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Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it.Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications.  What You Will Learn  Understand Spark unified data processing platformHow to run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functionsBuild real-time applications using Spark Structured StreamingDevelop intelligent applications with the Spark Machine Learning libraryWho This Book Is ForProgrammers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it.Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications.  What You Will Learn  Understand Spark unified data processing platformHowto run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functionsBuild real-time applications using Spark Structured StreamingDevelop intelligent applications with the Spark Machine Learning libraryWho This Book Is ForProgrammers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.  

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 LearnMaster the Spark unified data analytics engine and its various componentsWork in tandem to provide a scalable, fault tolerant and performant data processing engineLeverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQLDevelop machine learning applications using Spark MLlibManage the machine learning development lifecycle using MLflowWho This Book Is ForData scientists, data engineers and software developers.

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 componentsWork in tandem to provide a scalable, fault tolerant and performant data processing engineLeverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQLDevelop machine learning applications using Spark MLlibManage the machine learning development lifecycle using MLflow

Who This Book Is For

Data scientists, data engineers and software developers.

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll LearnGain an understanding of the MLOps disciplineKnow the MLOps technical stack and its componentsGet familiar with the MLOps adoption strategyUnderstand feature engineering Who This Book Is ForMachine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production