For a data analytics & automation project, building serverless data pipelines is easy, but making them resilient at scale is not. Most production pipelines fail not due to logic errors, but due to hitting service limits—such as the 256KB payload limit in AWS Step Functions—or causing "thundering herd" issues on downstream databases.

This session focuses on advanced patterns for building high-performance, cost-effective serverless ETL using AWS Step Functions, AWS Glue, and Amazon S3. We will analyze real-world failure scenarios and apply production-hardened architectural patterns to solve them.

Key architectural patterns covered:

The Claim Check Pattern: Implementing S3 pointers to bypass payload limits when passing large metadata fragments between workflow states.

High-Performance Parallelism: Using the Distributed Map state to orchestrate millions of items in parallel while optimizing for cost through ItemBatching.

Resilience Mechanics: Implementing exponential backoff with jitter and sophisticated catch blocks for transient versus permanent service failures.

The Payoff: Attendees will walk away with a toolkit of reusable ASL (Amazon States Language) definitions and best practices to build "future-proof" pipelines that are cost-optimized for massive datasets.