We migrated our customer behavior tracking system from a polling-based architecture to event-driven ingestion using EventBridge and Lambda. Previously, our analytics pipeline polled various data sources every 5 minutes, causing delays and occasional missed events during high traffic periods.
Implemented EventBridge as the central event router with Lambda functions handling real-time ingestion. Here’s our core event handling pattern:
def lambda_handler(event, context):
event_type = event['detail-type']
customer_data = event['detail']['customer']
# Route to appropriate analytics stream
kinesis.put_record(
StreamName=get_stream_name(event_type),
Data=json.dumps(customer_data)
)
EventBridge rules filter and route events to specific Lambda functions based on event patterns, reducing processing overhead. This eliminated the 5-minute polling delay and improved our analytics ingestion reliability from 94% to 99.7%.
Great question. We implemented deduplication at two levels. First, EventBridge content filtering prevents obvious duplicates from even reaching Lambda. Second, each event includes a unique event_id that we check against DynamoDB before processing. For ordering, we use event timestamps and sequence numbers in Kinesis. Out-of-order events within a 30-second window are buffered and reordered before analytics processing. This handles 99% of cases without adding significant complexity.
From a security perspective, how are you securing the EventBridge to Lambda integration? Customer behavior data is often sensitive. Are you using resource policies, encryption, or any specific IAM patterns to ensure events aren’t exposed or tampered with?
Security is critical for us. All EventBridge events are encrypted in transit and at rest. Lambda functions use least-privilege IAM roles with resource-based policies restricting which event sources can invoke them. We also implemented event validation - each event includes a signature that Lambda verifies before processing. Customer PII is tokenized at the event source before entering EventBridge, so the event router never sees raw sensitive data. CloudTrail logs all event routing for audit purposes.
How does this scale during traffic spikes? We’re considering a similar architecture but worried about Lambda concurrency limits and Kinesis throttling during peak periods like Black Friday sales events. Did you need to implement any rate limiting or backpressure mechanisms?
What’s your error handling strategy? With real-time ingestion, failed events can be problematic. Are you using DLQs, retry logic, or something else to ensure you don’t lose customer behavior data when Lambda functions fail?