Design a real‑time attribution system that assigns incremental revenue to marketing channels across multiple touchpoints, ensuring data consistency and low latency. What components would you include and how would you handle data drift?

CIRCLES framework + step‑by‑step strategy (120‑150 words, no story)

First, clarify the goal: real‑time incremental revenue attribution with <1 s latency. Investigate data sources: click, view, conversion events from web, mobile, email. Recommend an event‑streaming layer (Kafka) feeding a real‑time processing engine (Flink) that aggregates per‑user sessions and applies a weighted multi‑touch attribution model. Store raw events in a data lake (S3) and processed aggregates in a data warehouse (Snowflake) for batch reporting. Communicate by designing a REST API for downstream dashboards. Execute by implementing schema‑registry for data consistency, using feature flags for model rollouts, and setting up monitoring (Prometheus, Grafana) to detect data drift. Finally, iterate: run A/B tests on attribution weights and adjust based on revenue lift. This architecture balances low latency, scalability, and data quality while providing observability for drift detection.