Design a Serverless Data Processing System (AWS Lambda/Azure Functions)

To design a serverless data pipeline for Amazon, I would start by defining the ingestion point. Let's assume we are processing IoT sensor data arriving as JSON files in an S3 bucket. The trigger would be an S3 Event Notification that immediately invokes an AWS Lambda function. This function parses the data, validates schema integrity, and transforms it into a normalized format suitable for analytics. For high-throughput scenarios where cold starts impact latency, I would implement Provisioned Concurrency to keep instances warm during peak hours, ensuring sub-100ms response times. To handle failures gracefully, any unprocessed records would be routed to a Dead Letter Queue (DLQ) for later inspection rather than crashing the pipeline. The processed data would then be written to DynamoDB for low-latency retrieval or aggregated into Parquet files in S3 for Athena queries. Cost optimization is critical here; I would right-size the Lambda memory allocation based on actual profiling data to balance speed against billing, and utilize reserved capacity for predictable workloads. Finally, CloudWatch alarms would monitor invocation errors and duration to maintain system health.