Real-Time Data Processing at Scale

Real-Time Data Processing

Afto processes millions of automation events daily: order updates, inventory changes, customer messages, delivery tracking, payment notifications. We need to process these events in real-time with low latency and high reliability. Here is how we built our stream processing infrastructure.

Architecture Overview: Event producers publish to Kafka topics, stream processors consume and transform events, processed data flows to databases and analytics, webhooks notify external systems. We process 5 million events per day with sub-second latency.

Stream Processing with Kafka: We use Apache Kafka as our event backbone with 20+ topics for different event types, partitioning for parallel processing, replication for fault tolerance, and retention policies for replay. Kafka Streams for stateful processing enables real-time aggregations and joins.

Use Cases: Order tracking with real-time status updates, inventory sync across multiple systems, customer notification triggers, analytics and reporting, audit logging. Example: When an order is placed, we trigger inventory update, send confirmation email, notify kitchen, update analytics, log for audit.

Challenges and Solutions: Handling backpressure with rate limiting and buffering, ensuring exactly-once processing with idempotent consumers, monitoring lag and throughput with Kafka metrics, scaling consumers horizontally. Result: 99.99 percent event delivery, sub-second processing. Learn more at /contact