EventBridge streamlined analytics ingestion for customer behavior tracking with improved reliability

Cloud Platforms Amazon Web Services (AWS)
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1

We rebuilt our customer behavior analytics ingestion pipeline using EventBridge and Lambda, moving away from a direct API-to-database approach that was causing data loss during traffic spikes. The old system had event ingestion reliability issues, especially during product launches when we’d see 10x normal traffic.

EventBridge event routing now handles all incoming events from our web and mobile apps. Events are captured through API Gateway, validated, and routed to appropriate Lambda functions based on event type:

{
  "source": "customer.behavior",
  "detail-type": "PageView",
  "detail": {
    "userId": "usr_12345",
    "page": "/product/detail"
  }
}

Lambda integration processes events asynchronously and writes to DynamoDB for real-time analytics and S3 for historical analysis. This architecture improved our real-time analytics ingestion reliability to 99.95% and eliminated the data loss issues we experienced during high-traffic periods.

2

Good question. We include timestamps in event payloads and use DynamoDB’s conditional writes to handle ordering. For session tracking specifically, we use a separate Lambda that aggregates events into session objects. Events arriving out of order get merged based on timestamps. We also implemented idempotency keys to prevent duplicate processing if events are retried.

3

Let me provide a comprehensive overview of our complete implementation addressing the questions raised.

For EventBridge event routing, we use a multi-tier routing strategy based on event patterns. API Gateway receives all events and performs initial authentication and rate limiting. Events are then published to EventBridge with structured event patterns that route to different Lambda functions: PageView events go to one function, UserAction events to another, and ConversionEvents to a third. This separation allows us to scale and optimize each handler independently. We also use EventBridge rules to route failed events to a dead-letter queue for investigation.

The Lambda integration architecture uses several best practices for reliability. Each Lambda function processes events in batches (up to 100 events per invocation when possible) to reduce costs. Functions are configured with reserved concurrency to prevent throttling during spikes. We use Lambda Destinations to handle success and failure cases - successful events trigger downstream processing, while failures go to SQS for retry. All Lambda functions include structured logging with correlation IDs for tracing events through the pipeline.

For real-time analytics ingestion, we implemented a dual-write pattern. Lambda functions simultaneously write to DynamoDB for real-time dashboards (with 5-second aggregation) and to Kinesis Data Firehose for S3/data lake storage. DynamoDB streams trigger additional Lambda functions that update materialized views for common queries. This architecture provides sub-second query performance for real-time metrics while maintaining complete historical data in S3 for deep analysis.

Security and validation happens at multiple layers. API Gateway validates JWT tokens and enforces rate limits per client. Lambda functions validate event schemas using JSON Schema validation and sanitize PII fields before storage. We implemented field-level encryption for sensitive data using AWS KMS, with separate keys for different data classifications. CloudWatch Logs Insights queries run hourly to detect anomalous event patterns that might indicate security issues.

For the 50M events/day scenario mentioned, costs scale roughly linearly. At that volume, expect around $2,500 monthly for EventBridge, $2,000 for Lambda, and $1,500 for data storage/transfer. The key cost optimization is batching events in Lambda and using Kinesis Firehose for S3 writes instead of individual PutObject calls. We also implemented EventBridge Archive selectively - only critical event types are archived for replay, reducing storage costs by 60%.

Results after 12 months: 99.95% ingestion reliability (up from 97%), zero data loss during traffic spikes, 40% reduction in analytics query latency, and the flexibility to add new event consumers in minutes rather than days. The decoupled architecture also simplified our compliance posture - we can easily demonstrate event lineage and implement retention policies per data classification.

4

How’s the cost compared to your previous direct API approach? EventBridge and Lambda invocations can add up with high event volumes. We’re processing about 50 million events daily and trying to estimate costs for a similar migration.