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

Summary

In this chapter, you learned about some of the best practices for dealing with real-world problems and how to run highly selective queries on big fact tables. After that, you learned how to run highly selective queries by experimenting with the Glue partition indexing technique, which allows you to query humongous fact tables and make data retrieval smooth. Next, you learned how to deal with join performance issues between a large fact table and a small dimension table. Here, you learned how to use the broadcast mechanism to optimize the join operation.

After that, you learned how to deal with dimension tables when something goes wrong and you don’t have a way to partition the workloads into smaller workloads. Here, you applied a Glue bounded execution with Glue bookmarks to restrict the number of files that can be processed with incremental workloads. For the edge case scenario, where you read a large-dimension table, you learned how to configure Glue jobs to use...