01 · The problem
Fixed server pools struggle with bursty ETL workloads. Teams needed elastic throughput, monitoring suitable for SLAs, and APIs for downstream consumers.
A production-grade serverless ETL system on AWS that processes large volumes of records with orchestrated workflows and downstream APIs.
02 · My role
Software Engineer · serverless data pipeline
03 · The result
Built high-volume processing with Lambda, S3, and Step Functions; multi-stage transforms with production monitoring; API Gateway endpoints for internal and downstream consumers.
04 · Architecture
- 01
S3 as landing and intermediate storage
- 02
Lambda for transform and processing stages
- 03
Step Functions for workflow orchestration
- 04
API Gateway for downstream consumers
Implementation
- High-volume serverless processing
- Multi-stage transforms with monitoring
- API Gateway endpoints for consumers
- Elastic throughput without fixed server pools
Challenges & outcomes
Challenges
- Designing multi-stage transforms that fail safely
- Observability for production SLAs on serverless paths
Outcomes
- Elastic throughput without managing fixed server pools
- Production-grade orchestration suitable for daily high-volume loads
Lessons
- Serverless ETL succeeds when orchestration and retries are first-class
- Downstream APIs should not expose raw pipeline internals
Related writing
- Lessons from Serverless ETL on AWS
What matters when Lambda, S3, and Step Functions process high-volume data under production SLAs.