Serverless ETL/ELT Architecture with S3, EventBridge, Lambda, Step Functions, and Glue

In this post, I will walk through a production-style serverless ETL/ELT architecture on AWS using Amazon S3, Amazon EventBridge, AWS Lambda, AWS Step Functions, and AWS Glue . I will cover the full flow from event-driven ingestion to validation, quarantine handling, orchestration, schema drift handling, data quality checks, and replay. I am intentionally designing this as a pattern that can support both ETL and ELT : ETL when I perform transformations in Glue before landing curated outputs ELT when I land validated/raw data first and defer transformation to downstream query engines or warehouse jobs This architecture is a strong fit for data lake ingestion pipelines where I want: event-driven automation low operational overhead clear failure handling replayability observability and enough flexibility to survive real-world data messiness A few implementation choices in this post are deliberate: I use Step Functions Standard (not Express) because I want durable, auditable executions and