Building an Event-Driven Data Validation Pipeline on AWS Using S3, Lambda, and SNS

2026-06-21 · Source: Data Engineering on Medium · Field: Technology & Digital — Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Intermediate, quick

Summary

An event-driven data validation pipeline built on AWS automates the processing and quality checking of CSV files immediately upon upload. This serverless framework leverages Amazon S3 for file storage and event triggering, AWS Lambda for executing Python (Boto3) validation logic, and Amazon SNS for email notifications. When a CSV file lands in an S3 "input/" folder, an ObjectCreated event triggers Lambda, which reads the file, performs checks for row count, column count, null values, and duplicate rows. A JSON validation report is then generated, stored in an S3 "reports/" folder, and its results are communicated via SNS. Future enhancements include integrating CloudWatch Custom Metrics and Dashboards to visualize metrics like files processed and data quality issues.

Key takeaway

For Data Engineers building automated data ingestion or validation systems, adopting an event-driven serverless architecture on AWS significantly streamlines workflows. You should consider S3 event triggers with Lambda functions to instantly process file uploads, perform data quality checks, and generate reports. This approach eliminates manual intervention and polling, allowing you to build robust, scalable data pipelines with reduced operational overhead. Explore integrating CloudWatch for comprehensive monitoring of data quality metrics.

Key insights

Event-driven serverless architectures on AWS enable automated, real-time data validation workflows, eliminating manual intervention and polling.

Principles

• Event-driven systems eliminate polling and manual execution.
• Serverless architectures reduce operational overhead.
• S3 and Lambda form a powerful data engineering foundation.

Method

Upload CSV to S3 "input/" folder; S3 triggers Lambda; Lambda reads, validates (row/column count, nulls, duplicates); generates JSON report; stores report in S3 "reports/"; SNS sends email notification.

In practice

• Automate data quality checks for incoming files.
• Build real-time data ingestion pipelines.
• Monitor data quality with CloudWatch metrics.

Topics

• AWS Lambda
• Amazon S3
• Amazon SNS
• Event-Driven Architecture
• Data Validation
• Serverless Computing

Best for: Data Engineer, MLOps Engineer, Software Engineer

Related on AIssential

• Build and deploy an automatic sync solution for Amazon Bedrock Knowledge Bases — Automate Amazon Bedrock Knowledge Base synchronization with S3 while respecting AWS service quotas.
• Bad Data Will Find You: Validation Patterns for Streaming Pipelines — Robust data validation is critical for streaming pipelines to prevent silent corruption and state pollution from inevitable bad data.
• How Event-Driven Architectures Became the Backbone of Real-Time Systems — Event-driven architectures are critical for modern real-time systems, enabling scalable, decoupled, and resilient data processing.
• EP196: Cloud Load Balancer Cheat Sheet — Cloud-native architectures enhance scalability and efficiency through specialized components like load balancers, CQRS, Docker, and serverless functions.
• Full End-to-End Data Engineering Project in Databricks — Automating data ingestion and ETL pipelines in Databricks creates a robust, self-updating data engineering workflow.
• From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick — Amazon Quick now directly queries Apache Iceberg tables in S3, enabling near real-time, AI-powered analytics on data lakes.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.