# A 200-store pizza chain has three reporting needs from one Postgres orders database and one Kafka to Canonical URL: Domain: Pipeline Design ยท Difficulty: medium ## Problem A 200-store pizza chain has three reporting needs from one Postgres orders database and one Kafka topic of in-store kitchen events. The canvas has all five endpoints. Apply the entire beginner tier: (1) pick the rhythm per consumer using the three-question test (s0, s4) - the CFO revenue chart is daily (Tier 4, batch), the store-ops kitchen dashboard is sub-15-min (Tier 1 or 2, streaming), the marketing retention chart is weekly (Tier 5, batch); (2) build the canonical batch shape (s1) for the daily and weekly paths with a shared raw zone in object storage, a curated transform, a warehouse, an orchestrator, and slaFreshness < 24h on the daily warehouse table; (3) build the canonical streaming shape (s2) for the kitchen-ticket path with a streaming engine (Flink, Spark Structured Streaming, Kafka Streams, or Beam, not plain Spark or dbt), a serving store, and slaFreshness real-time or < 1min on the streaming side; (4) keep the slowest-hop rule (s3) honest so no consumer's slaFreshness exceeds its upstream's. The two sources must share a single raw zone in object storage (S3, GCS, or ADLS) so a schema change in either source breaks at most one extract. Three distinct freshness tiers must be visible across the canvas. ## Related - [All practice problems](https://datadriven.io/problems) - [Mock interview mode](https://datadriven.io/interview/a-200-store-pizza-chain-has-three-reporting-needs-from-one-p-2f392321) - [System Design Interview Questions](https://datadriven.io/data-engineering-system-design) - [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep) - [Daily Challenge](https://datadriven.io/daily) --- Source: DataDriven (https://datadriven.io). 100% free data engineering interview prep. Live code execution against Postgres 16, Python 3.11, and Spark sandboxes. No paywall, no premium tier, no signup gate.