Serverless ELT with dlt, AWS Lambda and Terraform
We’ve built a fully serverless, Python-native ELT deployment workflow using dlt, AWS Lambda, Docker, and Terraform. This stack eliminates infrastructure management, scales automatically, and runs cost-efficiently—all triggered via events or schedules.
Why go Serverless for ELT?
Traditional ELT tooling can be heavy and infrastructure-bound. By using Lambda and Docker with dlt, we’ve removed cold starts, simplified secrets management, and enabled seamless scaling. It’s a modern alternative to legacy orchestration platforms.
Architecture at a Glance
- AWS Lambda functions built from Docker containers (ECR)
- dlt pipelines for Pythonic, incremental, and S3-native data loading
- S3 for raw data, processed output, and dlt state tracking
- Terraform-managed infrastructure (Lambda, S3, EventBridge, IAM, etc.)
- GitHub Actions for CI/CD: build → push → deploy
- Secrets stored in AWS Secrets Manager with runtime injection
Deployment Highlights
- Container-based Lambda deployment to bypass size limits
- EventBridge scheduling and failure notifications via SNS
- Version-pinned dependencies for fast, deterministic builds
- CloudWatch logging with structured output and alerting
- Flexible partitioning and file formats (CSV, Parquet, JSONL)
What Sets It Apart
- Code-first ELT design with full Python flexibility
- True incremental loading and stateful ingestion
- Zero-infrastructure management with pay-per-use execution
- Terraform automation ensures reproducibility across environments
- Easily extended for multiple destinations (Snowflake, S3, etc.)
What’s Next
We’ll explore why we chose dlt over other ELT frameworks, and how it performed in real-world client deployments.
Interested in going serverless? Read the full guide or reach out to see how we can help launch your ELT pipelines faster.
