Building a Modular Serverless ETL Pipeline on AWS with Terraform & Lambda

Source: Dev.to

Overview

Many applications, even small ones, receive data as raw CSV files (customer exports, logs, partner data dumps). Without automation to clean, validate, and store that data in a standard format, teams end up with messy data, duplicated effort, inconsistent formats, and manual steps each time new data arrives.

This pipeline provides:

• Automated processing of raw CSV uploads
• Basic data hygiene (cleaning / validation)
• Ready‑to‑use outputs for analytics or downstream systems
• Modular, reproducible, and extendable infrastructure

By combining Terraform, AWS Lambda, and Amazon S3, the solution is serverless, scalable, and easy to redeploy.

Architecture & Design

High‑level flow:

Raw CSV file
   ↓
S3 raw‑bucket
   ↓ (S3 event trigger)
Lambda function (Python)
   ↓
Data cleaning / transformation
   ↓
Save cleaned CSV to S3 clean‑bucket
   ↓ (optional)
Push cleaned data to DynamoDB / RDS

How It Works

1. A user (or another system) uploads a CSV file into the raw S3 bucket.
2. S3 triggers the Lambda function automatically on object creation.
3. The Lambda reads the CSV, parses rows, and applies validation and transformation logic (e.g., remove invalid rows, normalize text, enforce schema).
4. Cleaned data is written back to a clean S3 bucket — optionally also sent to a database (DynamoDB, RDS, etc.).

Because everything is managed via Terraform, you can version your infrastructure, redeploy consistently across environments (dev / staging / prod), and manage permissions cleanly.

Example Use Cases

• Customer data ingestion: Partners or internal teams export user data; the pipeline cleans, standardizes, and readies it for analytics or import.
• Daily sales / transaction reports: Automate processing of daily uploads into a clean format ready for dashboards or billing systems.
• Log / event data processing: Convert raw logs or CSV exports into normalized data for analytics or storage.
• Pre‑processing for analytics or machine learning: Clean and standardize raw data before loading into a data warehouse or data lake.
• Archival + compliance workflows: Maintain clean, versioned, and validated data sets for audits or record‑keeping.

Infrastructure as Code with Terraform

• Event‑driven serverless architecture with Lambda
• Secure IAM policies and resource permissions
• Modular, reusable Terraform modules
• Clean, maintainable ETL logic in Python

Possible Enhancements

• Schema validation and error logging
• Deduplication logic using DynamoDB or file hashes
• Multiple destinations (S3, DynamoDB, RDS)
• Monitoring and CloudWatch metrics
• Multi‑format support (CSV, JSON, Parquet)
• CI/CD integration
• Multi‑environment deployment (dev, staging, prod)

Conclusion

This project demonstrates how to build a real‑world, production‑inspired ETL pipeline on AWS. It’s a small but powerful example of combining serverless computing, IaC, and automation. Experimenting with these tools provides an excellent way to learn best practices while building something tangible for a portfolio.

GitHub repository: