東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Hands-on guide to Apache Spark 3 = b...

Antolinez Garcia, Alfonso.

FindBook

Google Book

Amazon

博客來

Hands-on guide to Apache Spark 3 = build 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 3/ by 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.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Data mining - Computer programs. -
電子資源:	https://doi.org/10.1007/978-1-4842-9380-5
ISBN:	9781484293805

Hands-on guide to Apache Spark 3 = build 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

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

Big data.
Subjects--Topical Terms:

812286
Data mining
--Computer programs.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Hands-on guide to Apache Spark 3 = build scalable computing engines for batch and stream data processing /
LDR:03430nmm a2200325 a 4500 001 2332470
003 DE-He213
005 20230605153625.0
006 m d
007 cr nn 008maaau
008 240402s2023 cau s 0 eng d
020 $a 9781484293805 $q (electronic bk.)
020 $a 9781484293799 $q (paper)
024 7 $a 10.1007/978-1-4842-9380-5 $2 doi
035 $a 978-1-4842-9380-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b A634 2023
100 1 $a Antolinez Garcia, Alfonso. $3 3662390
245 1 0 $a Hands-on guide to Apache Spark 3 $h [electronic resource] : $b build scalable computing engines for batch and stream data processing / $c by Alfonso Antolínez García.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xiii, 403 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a 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.
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.
630 0 0 $a Big data. $3 3662391
650 0 $a Data mining $x Computer programs. $3 812286
650 1 4 $a Data Science. $3 3538937
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Python. $3 3201289
650 2 4 $a Artificial Intelligence. $3 769149
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-9380-5
950 $a Professional and Applied Computing (SpringerNature-12059)