Serverless Data Processing Architecture

The Problem This Blueprint Solves

Your team wants serverless to eliminate server management, but the proof-of-concept that worked for a single Lambda function falls apart at 40+ functions. Cold starts cause timeout errors on downstream services. Step Functions workflows become untraceable spaghetti. Your monthly Lambda bill is somehow higher than the EC2 instances you replaced because nobody optimized memory allocation or understood provisioned concurrency pricing.

This blueprint documents the serverless platform I architected for a fintech startup processing 8M daily API calls with a total infrastructure bill under $3,200/month — less than two senior engineers' time managing servers.

What You Get

• Architecture diagrams — Event-driven flow from API Gateway through Lambda, SQS, DynamoDB, and EventBridge with cold start mitigation points marked (Draw.io)
• Terraform + SAM templates — API Gateway with custom domain, Lambda functions with layers, SQS dead letter queues, DynamoDB with on-demand scaling, EventBridge rules, X-Ray tracing configuration
• Cost optimization guide — Memory/CPU profiling methodology, provisioned concurrency break-even calculator, and reserved capacity recommendations
• Observability stack — CloudWatch dashboards, X-Ray service map, custom metrics for cold start tracking, and alerting thresholds

Key Architecture Decisions

• EventBridge over SNS for event routing — EventBridge gives you content-based filtering, schema discovery, and archive/replay. SNS requires topic-per-event-type which creates topic sprawl at 20+ event types. EventBridge handles hundreds of event patterns on a single bus with cent-level costs.
• DynamoDB single-table design over multi-table — Multiple tables mean multiple connections, multiple capacity units, and complex transaction coordination. Single-table design with composite keys and GSIs handles all access patterns with one provisioned connection per Lambda execution environment.
• Lambda Layers for shared dependencies — Common libraries (AWS SDK overrides, shared validators, logging utilities) ship as layers. This cuts deployment package size by 60-80%, reduces cold start initialization time, and lets you update shared code without redeploying every function.
• SQS between every async boundary — Direct Lambda-to-Lambda invocation creates tight coupling and cascading failures. SQS absorbs traffic spikes, provides built-in retry with exponential backoff, and dead letter queues give you a recovery path for failed messages without data loss.

Who This Blueprint Is For

• Backend Engineers building their first production serverless application beyond a tutorial
• Architects evaluating serverless vs containers for a new product launch
• FinOps teams trying to understand and optimize serverless spend
• CTOs at startups who want infrastructure costs that scale linearly with usage

Your First 48 Hours

Deploy the API Gateway + single Lambda + DynamoDB Terraform stack into a sandbox account. Run the included artillery load test to generate 1,000 requests per second for 5 minutes. Open the X-Ray service map and identify cold start frequency. On day two, enable provisioned concurrency on the critical-path Lambda and rerun the load test — compare P99 latency before and after. This gives you concrete data on whether provisioned concurrency is worth the cost for your traffic pattern.

Limitations and Trade-offs

Lambda has a 15-minute execution timeout. Long-running processes (report generation, video encoding, ML inference) need Step Functions orchestration or Fargate. The DynamoDB single-table design requires upfront access pattern analysis — if your access patterns change frequently during early product development, a multi-table approach may be more practical until your data model stabilizes. API Gateway WebSocket support is included but limited to 500 concurrent connections per route without custom scaling configuration.