Book Image

Serverless ETL and Analytics with AWS Glue

By : Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga, Ishan Gaur
Book Image

Serverless ETL and Analytics with AWS Glue

By: Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga, Ishan Gaur

Overview of this book

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.
Table of Contents (20 chapters)
1
Section 1 – Introduction, Concepts, and the Basics of AWS Glue
5
Section 2 – Data Preparation, Management, and Security
13
Section 3 – Tuning, Monitoring, Data Lake Common Scenarios, and Interesting Edge Cases

Glue ML transformations

As mentioned previously, ML is not just an entity that reads the output data from ETL processes, but also one that powers its transformations. ML models enable a wide variety of operations that were not possible before due to computer intelligence limitations.

Because of this, Glue started to offer ML powered-operations with specific purposes under the ML transforms feature. As the name suggests, ML transforms are specific kinds of Glue transforms that are powered by ML models but must be trained and prepared before they can be used. Once they are ready, they can be called from your ETL job’s code, just like other Glue transforms.

At the time of writing, Glue has only released one ML transform, FindMatches, which will automatically find duplicated records within a dataset. Even though this seems like a simple task (most ETL engines could provide this by simply comparing records and checking if they are equal, or if they share a primary key), ML...