Real-Time Ad Measurement Data Pipeline Blueprint
A scalable ingestion and stream-processing pattern for billions of daily ad events with sub-minute attribution lag.
Design targets, not measured client results.
The Problem
Why teams need this pattern
Exploding impression and conversion telemetry overwhelms batch pipelines, causing hours of lag, ballooning warehouse bills, and inaccurate campaign pacing.
Ideal for
- Ad networks and measurement startups
- Teams moving from batch to streaming analytics
Our Approach
Architectural approach
Rust telemetry collectors, Apache Flink for stream aggregation, and Apache Iceberg on S3 queried through ClickHouse for low-cost, fast analytics.
System Design
Architectural layers & components
How data and control flow from the edge of the system to the people who use it.
- 01
Rust Event Collector
Low-latency HTTP pixel collector built for very high request rates per node.
- 02
Stream Aggregation
Apache Flink computing real-time impression and click counts.
- 03
Data Lake Storage
Apache Iceberg on S3 with automated partition compaction.
- 04
Analytical Engine
ClickHouse cluster powering real-time advertiser dashboards.
Design Targets
What this architecture is built to achieve
Targets we design toward. We confirm them against your own data and workload before you commit to a build.
- Cost savings depend on current warehouse pricing and query patterns.
- Capacity targets are validated with load tests on your traffic shape.
What You Get
Artifacts tailored to your environment
The blueprint is a starting point. These are the working documents and code we adapt for you.
Learn about our Data & Analytics services- Rust collector template
- Flink streaming SQL
- ClickHouse table schemas
- S3 partitioning strategy
How We Work
From first call to working prototype
- 130–45 min
Discovery call
We review your constraints, existing systems, and success criteria, and tell you honestly whether this blueprint fits.
- 21–2 weeks
Fit & feasibility workshop
We adapt the reference architecture to your stack, validate the design targets against your real data, and produce a scoped plan.
- 3Scoped per project
Prototype, then build
We ship a working slice first so you can judge the approach before committing to a full build.
Technology Stack
Default tools & infrastructure
We swap components to fit your stack.
- Rust
- Apache Flink
- ClickHouse
- Apache Iceberg
- AWS S3
- Kafka
- Grafana
FAQ
Common questions
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Want an architecture like this?
Book a call and we'll tell you honestly whether this blueprint fits, and how we'd adapt it to your stack and constraints.