# A logistics company runs three pipelines (shipments hourly, inventory every 6 hours, analytics daily Canonical URL: Domain: Pipeline Design ยท Difficulty: medium ## Problem A logistics company runs three pipelines (shipments hourly, inventory every 6 hours, analytics daily) glued by time offsets. On Black Friday a slow upstream broke the analytics output. Apply the entire L4 intermediate tier: (1) two orchestrators (one for the upstream cadence DAGs, one for the daily analytics) so each cadence runs separately; (2) replace any plain Snowflake mart between the upstream and analytics DAGs with a lakehouse format (Iceberg, Delta, or Hudi) so the analytics DAG fires on the asset's snapshot freshness rather than the clock; (3) add an observability_tool node monitoring the analytics output with an alert_destination so SLA misses route to on-call. ## Related - [All practice problems](https://datadriven.io/problems) - [Mock interview mode](https://datadriven.io/interview/a-logistics-company-runs-three-pipelines-shipments-hourly-c30e5409) - [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.