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Problem Understanding
Restate the problem in your own words.
Design Ad Click Aggregation
Design an ad-click aggregation pipeline: 10B+ clicks/day flow in from edge servers, get deduplicated against bots and double-clicks, fraud-filtered, and aggregated by (ad_id, campaign_id, publisher_id) at minute / hour / day granularity. Advertiser dashboards query the aggregates with p99 < 200 ms. Every click is money — accuracy is non-negotiable, and the lambda architecture (speed + batch reconciliation) is the canonical answer.
- Google AdsThe canonical ad platform — click attribution + reporting at hyperscale, with batch reconciliation against the source-of-truth log.
- Facebook Ads ManagerSame problem inside the Meta stack; a heavy lambda pipeline behind a real-time-feeling dashboard.
- Amazon DSPProgrammatic ad buying with billions of bid + click events; the same shape with stricter latency on the bid path.
- The Trade DeskIndependent DSP — a reference implementation of the lambda architecture for ad analytics.
Your task: read the problem above, then write what the system is, who uses it, the rough scale, and the headline UX expectation — in your own words. Submit for AI review when you're ready.
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