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Hands-on guide to Apache Spark 3build scalable computing engines for batch and stream data processing /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hands-on guide to Apache Spark 3by Alfonso Antolínez García.
其他題名:	build scalable computing engines for batch and stream data processing /
作者:	Antolinez Garcia, Alfonso.
出版者:	Berkeley, CA :Apress :2023.
面頁冊數:	xiii, 403 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data miningComputer programs.
電子資源:	https://doi.org/10.1007/978-1-4842-9380-5
ISBN:	9781484293805$q(electronic bk.)

Hands-on guide to Apache Spark 3build scalable computing engines for batch and stream data processing /
Antolinez Garcia, Alfonso.

Hands-on guide to Apache Spark 3build scalable computing engines for batch and stream data processing /[electronic resource] :by Alfonso Antolínez García. - Berkeley, CA :Apress :2023. - xiii, 403 p. :ill. (chiefly color), digital ;24 cm.

Part 1: Apache Spark Batch Data Processing -- Chapter 1: Introduction to Apache Spark for Large-Scale Data Analytics -- Chapter 2: Getting Started with Apache Spark -- Chapter 3: Spark Low Level API -- Chapter 4: Spark High-Level APIs -- Chapter 5: Spark Dataset API and Adaptive Query Execution -- Chapter 6: Introduction to Apache Spark Streaming -- Chapter 7: Spark Structured Streaming -- Chapter 8: Streaming Sources and Sinks -- Chapter 9: Event Time Window Operations and Watermarking -- Chapter 10: Future Directions for Spark Streaming -- Bibliography.

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 notebooks 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. You will: Master the concepts of Spark clusters and batch data processing Understand data ingestion, transformation, and data storage Gain insight into essential stream processing concepts and different streaming architectures Implement streaming jobs and applications with Spark Streaming.

ISBN: 9781484293805$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-9380-5doiSubjects--Uniform Titles:

Big data.
Subjects--Topical Terms:

257798
Data mining
--Computer programs.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Hands-on guide to Apache Spark 3build scalable computing engines for batch and stream data processing /
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520 $a 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 notebooks 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. You will: Master the concepts of Spark clusters and batch data processing Understand data ingestion, transformation, and data storage Gain insight into essential stream processing concepts and different streaming architectures Implement streaming jobs and applications with Spark Streaming.
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