國立虎尾科技大學 |

Modern Data Engineering with Apache Spark = A Hands-On Guide for Building Mission-Critical Streaming Applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Modern Data Engineering with Apache Spark/ by Scott Haines.
其他題名:	A Hands-On Guide for Building Mission-Critical Streaming Applications /
作者:	Haines, Scott.
面頁冊數:	XXV, 585 p. 59 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data Mining and Knowledge Discovery. -
電子資源:	https://doi.org/10.1007/978-1-4842-7452-1
ISBN:	9781484274521

Modern Data Engineering with Apache Spark = A Hands-On Guide for Building Mission-Critical Streaming Applications /
Haines, Scott.

Modern Data Engineering with Apache SparkA Hands-On Guide for Building Mission-Critical Streaming Applications /[electronic resource] :by Scott Haines. - 1st ed. 2022. - XXV, 585 p. 59 illus.online resource.

Part I. The Fundamentals of Data Engineering with Spark -- 1. Introduction to Modern Data Engineering -- 2. Getting Started with Apache Spark -- 3. Working with Data -- 4. Transforming Data with Spark SQL and the DataFrame API -- 5. Bridging Spark SQL with JDBC -- 6. Data Discovery and the Spark SQL Catalog -- 7. Data Pipelines & Structured Spark Applications -- Part II. The Streaming Pipeline Ecosystem -- 8. Workflow Orchestration with Apache Airflow -- 9. A Gentle Introduction to Stream Processing -- 10. Patterns for Writing Structured Streaming Applications -- 11. Apache Kafka & Spark Structured Streaming -- 12. Analytical Processing & Insights -- Part III. Advanced Techniques -- 13. Advanced Analytics with Spark Stateful Structured Streaming -- 14. Deploying Mission Critical Spark Applications on Spark Standalone -- 15. Deploying Mission Critical Spark Applications on Kubernetes.

Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow. Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes. Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. What You Will Learn Simplify data transformation with Spark Pipelines and Spark SQL Bridge data engineering with machine learning Architect modular data pipeline applications Build reusable application components and libraries Containerize your Spark applications for consistency and reliability Use Docker and Kubernetes to deploy your Spark applications Speed up application experimentation using Apache Zeppelin and Docker Understand serializable structured data and data contracts Harness effective strategies for optimizing data in your data lakes Build end-to-end Spark structured streaming applications using Redis and Apache Kafka Embrace testing for your batch and streaming applications Deploy and monitor your Spark applications.

ISBN: 9781484274521

Standard No.: 10.1007/978-1-4842-7452-1doiSubjects--Topical Terms:

677765
Data Mining and Knowledge Discovery.

LC Class. No.: QA76.73.J38

Dewey Class. No.: 005.133

Modern Data Engineering with Apache Spark = A Hands-On Guide for Building Mission-Critical Streaming Applications /
LDR:04561nam a22003975i 4500 001 1090983
003 DE-He213
005 20220512142303.0
007 cr nn 008mamaa
008 221228s2022 xxu| s |||| 0|eng d
020 $a 9781484274521 $9 978-1-4842-7452-1
024 7 $a 10.1007/978-1-4842-7452-1 $2 doi
035 $a 978-1-4842-7452-1
050 4 $a QA76.73.J38
072 7 $a UMX $2 bicssc
072 7 $a COM051280 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 005.133 $2 23
100 1 $a Haines, Scott. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1398501
245 1 0 $a Modern Data Engineering with Apache Spark $h [electronic resource] : $b A Hands-On Guide for Building Mission-Critical Streaming Applications / $c by Scott Haines.
250 $a 1st ed. 2022.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a XXV, 585 p. 59 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Part I. The Fundamentals of Data Engineering with Spark -- 1. Introduction to Modern Data Engineering -- 2. Getting Started with Apache Spark -- 3. Working with Data -- 4. Transforming Data with Spark SQL and the DataFrame API -- 5. Bridging Spark SQL with JDBC -- 6. Data Discovery and the Spark SQL Catalog -- 7. Data Pipelines & Structured Spark Applications -- Part II. The Streaming Pipeline Ecosystem -- 8. Workflow Orchestration with Apache Airflow -- 9. A Gentle Introduction to Stream Processing -- 10. Patterns for Writing Structured Streaming Applications -- 11. Apache Kafka & Spark Structured Streaming -- 12. Analytical Processing & Insights -- Part III. Advanced Techniques -- 13. Advanced Analytics with Spark Stateful Structured Streaming -- 14. Deploying Mission Critical Spark Applications on Spark Standalone -- 15. Deploying Mission Critical Spark Applications on Kubernetes.
520 $a Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow. Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compile reusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes. Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. What You Will Learn Simplify data transformation with Spark Pipelines and Spark SQL Bridge data engineering with machine learning Architect modular data pipeline applications Build reusable application components and libraries Containerize your Spark applications for consistency and reliability Use Docker and Kubernetes to deploy your Spark applications Speed up application experimentation using Apache Zeppelin and Docker Understand serializable structured data and data contracts Harness effective strategies for optimizing data in your data lakes Build end-to-end Spark structured streaming applications using Redis and Apache Kafka Embrace testing for your batch and streaming applications Deploy and monitor your Spark applications.
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Database Management. $3 669820
650 2 4 $a Data Analysis and Big Data. $3 1366136
650 1 4 $a Java. $3 1115949
650 0 $a Data mining. $3 528622
650 0 $a Database management. $3 557799
650 0 $a Quantitative research. $3 635913
650 0 $a Java (Computer program language). $3 686374
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484274514
776 0 8 $i Printed edition: $z 9781484274538
776 0 8 $i Printed edition: $z 9781484284841
856 4 0 $u https://doi.org/10.1007/978-1-4842-7452-1
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)