Design an ad-click aggregation and enrichment pipeline

## Scenario You are designing a data platform to measure advertising performance. Mobile apps and web browsers send **ad impression** and **ad click** events. Analysts need near-real-time dashboards and batch reports. ## Requirements - Ingest impression/click events from **mobile and web** clients. - Produce aggregates such as: - clicks / impressions / CTR - grouped by time window (e.g., 1 min, 1 hour, 1 day) - grouped by dimensions like `campaign_id`, `ad_id`, `publisher_id`, `country`, `device_type` - **Enrich** events by joining with other data sources (examples): - campaign metadata (budget, objective) - ad metadata (creative type) - user/device attributes (coarse geo, OS) - Support both: - **near-real-time** queries (seconds to a few minutes delay) - **historical** queries over months - Event delivery constraints: - clients may be offline and retry - duplicate/out-of-order events can occur ## Scale & SLOs (assume) - Peak 500k events/sec (impressions+clicks), average 100k/sec. - Dashboard freshness: P95 < 2 minutes. - Correctness: exactly-once is not required, but **duplicates should be minimized** and results should be explainable. ## Key discussion prompt Clients can send events: 1) one request per event, or 2) batch multiple events per request. Explain the **trade-offs between number of requests vs latency**, especially for mobile networks. ## Deliverables - High-level architecture and major components - Data model / schemas - How you do enrichment joins (stream-stream vs stream-table vs batch) - How you handle deduplication, late events, and backfills - What you store for serving (OLAP/warehouse) and for near-real-time dashboards