Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features

Review
"This book gives an excellent introduction to the Glue service and how to use its power for analytics and ETL tasks. Even as a long-time Glue user, it helped me discover useful features that I overlooked in day-to-day business. Especially the hands-on exercises and the focus on practical challenges, such as data sharing in large organizations or troubleshooting transformation jobs, makes it a valuable read for all data engineers and analysts." --
Simon Kern, Lead Engineer at BMW Group
"Serverless data integration and ETL is a complex topic. First, this book sets up the right context and then dives deep into solving data integration problems using AWS Glue, a leading serverless data integration service. The book successively goes deeper into advanced topics such as data preparation, ingestion, meta-data, and data lake management. It also covers key topics, such as security and data sharing, which one needs to get right from the get-go. Finally, it provides instructions on how to build reliable data pipelines and offers insights on troubleshooting, monitoring, and recovery for building reliable data pipelines. For all the topics, the book offers instructions with code snippets that will help you relate to real-world use cases. I would strongly recommend you read this book as a reference to learn data integration using AWS Glue." --
Santosh Chandrachood, General Manager, AWS Glue at Amazon Web Services
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