This blog post discusses optimizing serverless data pipelines on AWS for efficient data analysis. It describes a typical architecture using AWS Glue, Amazon S3, and Amazon QuickSight. The author explains how to improve ETL processes with AWS Glue by scaling resources, optimizing job configurations, and using workflows. To enhance data insights, the post recommends using QuickSight’s SPICE in-memory caching. Finally, it demonstrates how to automate the entire pipeline using AWS Step Functions and CloudWatch event triggering. This approach ensures that QuickSight datasets are refreshed in the correct order immediately after the Glue workflow completes, resulting in up-to-date and quickly accessible insights for business users.