In today's digital-first economy, real-time responsiveness is the name of the game, especially in advertising technology (AdTech), where decisions often need to be made within milliseconds. During my tenure at ROIads, I was tasked with engineering a backend system that could process real-time ad traffic at scale, with sub-10ms latency, while remaining cost-effective, resilient, and observable. This blog post shares my journey designing and deploying a real-time data pipeline using modern AWS cloud-native tools and offers a roadmap for others tackling similar challenges.
The Challenge: Latency Meets Throughput
The AdTech domain demands a unique balance of speed and scale. Platforms must ingest and evaluate thousands of bid requests per second. Any system delay can translate into missed opportunities and revenue loss.
Our goals were:
- <10ms end-to-end latency for processing ad events
- Scalable infrastructure to handle unpredictable spikes in traffic
- Fault tolerance and graceful degradation
- Observability for system health and debugging
Architectural Overview
We chose AWS for its mature serverless ecosystem and cost-efficiency under bursty loads. Here's a breakdown of our architecture:
- AWS Lambda for stateless, on-demand compute
- AWS Step Functions to coordinate multi-step workflows
- Amazon SQS for decoupling producers and consumers
- CloudWatch for metrics, logging, and alerts
- AWS CDK/SAM for Infrastructure-as-Code (IaC)
Lambda functions ingested the traffic, processed requests in parallel, and passed metadata via SQS to a state machine that performed downstream validations and analytics logging.
Why Event-Driven Architecture?
Event-driven systems scale naturally under load. By decoupling components via queues and triggering compute on events, you eliminate idle resources and optimize compute cost. The asynchronous design also allowed us to batch certain operations for downstream analytics.
Benefits:
- Automatic scaling with Lambda
- Built-in retry policies
- Easier reasoning about system boundaries
Infrastructure-as-Code: Enabling Repeatability
To ensure consistent deployments and version control, we relied on AWS CDK and SAM. This enabled us to:
- Roll back to known-good configurations
- Deploy full infrastructure from scratch in dev/test environments
- Track changes across infrastructure code
Observability: The Silent Hero
You can't fix what you can't see. Our observability stack included:
- Structured JSON logs (parsed by CloudWatch Insights)
- Custom metrics for latency, error rates, and queue depth
- Alarms and dashboards to monitor SLAs
When latency spiked, we used structured logs to identify whether a specific event type, downstream service, or data condition was at fault.
Resilience: Embracing Failure
We baked in resilience with the following tactics:
- Automated retries with exponential backoff
- Fallback logic in Lambda to cache stale results if a downstream service failed
- Dead Letter Queues (DLQs) for tracking and remediating failed events
Cost Considerations
One of the overlooked benefits of serverless is cost granularity. With proper memory tuning and cold-start minimization, our entire pipeline cost just a few hundred dollars per month while supporting millions of events.
We also used reserved concurrency and regional settings to avoid hitting throttling limits.
Lessons Learned
- Cold-starts matter: Keep Lambdas warm with scheduled pings if ultra-low latency is critical.
- Time-bound retries: Avoid retry storms by limiting how long retries are allowed.
- Use IaC from day one: It pays dividends in every stage of development.
Final Thoughts
Designing this real-time system gave me hands-on insights into cloud-native scalability and fault-tolerance. More importantly, it reinforced that performance isn't just about faster code—it's about intelligent architecture. If you're working in AdTech or any domain where real-time decisions are vital, consider embracing event-driven pipelines and serverless patterns.