Hands on Guide Apache Spark by Antolínez García (15 results)

Condition: New.

Condition: New. SUPER FAST SHIPPING.

Condition: As New. Unread book in perfect condition.

Condition: New. In.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. In.

Condition: New.

Paperback. Condition: Brand New. 416 pages. 10.00x7.01x0.86 inches. In Stock.

Taschenbuch. Condition: Neu. Neuware -This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark¿s structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 420 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark's structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming's execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and not Elektronisches Buch hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use.Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark.What You Will LearnMaster the concepts of Spark clusters and batch data processingUnderstand data ingestion, transformation, and data storageGain insight into essential stream processing concepts and different streaming architecturesImplement streaming jobs and applications with Spark StreamingWho This Book Is ForData engineers, data analysts, machine learning engineers, Python and R programmers 420 pp. Englisch.

Condition: New. Print on Demand.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark s structured APIs to perform complex data transformations and analyses you can use to implement end-to-end.

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark's structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming's execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and not Elektronisches Buch hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use.Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark.What You Will LearnMaster the concepts of Spark clusters and batch data processingUnderstand data ingestion, transformation, and data storageGain insight into essential stream processing concepts and different streaming architecturesImplement streaming jobs and applications with Spark StreamingWho This Book Is ForData engineers, data analysts, machine learning engineers, Python and R programmers.