Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features
by Vishal Pathak ，Subramanya Vajiraya ，Noritaka Sekiyama ，Tomohiro Tanaka ，Albert Quiroga，Ishan Gaur
Publisher finelybook 出版社: Packt Publishing (August 30, 2022)
Language 语言: English
Print Length 页数: 434 pages
ISBN-10: 1800564988
ISBN-13: 9781800564985
Book Description
Build efficient data lakes that can scale to virtually unlimited size using AWS Glue
Key Features
Learn to work with AWS Glue to overcome typical implementation challenges in data lakes
Create and manage serverless ETL pipelines that can scale to manage big data
Written by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no time
Book Description
Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes.
Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You’ll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you’ll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options.
By the end of this AWS book, you’ll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.
What you will learn
Apply various AWS Glue features to manage and create data lakes
Use Glue DataBrew and Glue Studio for data preparation
Optimize data layout in cloud storage to accelerate analytics workloads
Manage metadata including database, table, and schema definitions
Secure your data during access control, encryption, auditing, and networking
Monitor AWS Glue jobs to detect delays and loss of data
Integrate Spark ML and SageMaker with AWS Glue to create machine learning models
Who this book is for
This book is for ETL developers, data engineers, and data analysts who want to understand how AWS Glue can help you solve your business problems. Basic knowledge of AWS data services is assumed.
Table of Contents
Data Management – Introduction and Concepts
Introduction to Important AWS Glue Features
Data Ingestion
Data Preparation
Designing Data Layouts
Data Management
Metadata Management
Data Security
Data Sharing
Data Pipeline Management
Monitoring
Tuning, Debugging, and Troubleshooting
Data Analysis
Machine Learning Integration
Architecting Data Lakes for Real-World Scenarios and Edge Cases
