Case study

Serverless DMS Observability Platform

A serverless, event-driven observability platform that turned noisy, manually configured AWS DMS migration monitoring into a zero-touch system.

Problem

Large-scale database migrations on AWS DMS generate a constant stream of task events and logs. Monitoring them manually meant configuring CloudWatch for every new migration task by hand and wading through repetitive alerts — operational noise that buried the signals that actually mattered.

Approach

• Python Lambda functions intercept CloudTrail events as DMS migration tasks are created or changed, and automatically configure the matching CloudWatch subscription filters — no manual setup for new tasks.
• A DynamoDB-backed deduplication layer collapses repeated log events so the same fault never alerts twice.
• A dual-buffer digest system aggregates alerts on separate cadences — a 3-minute buffer for critical events and a 30-minute buffer for warnings — so severity determines urgency.
• Built entirely serverless on AWS Lambda, CloudWatch, DynamoDB, and CloudTrail: nothing to patch, and cost scales with migration activity.

Outcome

• 100% monitoring coverage across DMS migration tasks with zero manual intervention.
• Operational alert noise reduced by 95% through deduplication and automated aggregation.

Stack