In this video, AWS Senior Solution Architect Dean demonstrates how to set up data quality checks in ETL pipelines using AWS Glue Data Quality. He shows how to create rules for customer and sales data, including row count validation, primary key checks, and custom SQL rules. The demo covers adding data quality transforms, authoring rules, and configuring actions based on evaluation results. Dean explains how to filter and separate good and bad records, view data quality results, and generate reusable code for different data sources. This capability helps prevent bad data from entering repositories and improves overall data quality for business teams.