# Two DAGs are glued by a time offset: the orders DAG schedules at 2am and usually finishes by 2:45am, Canonical URL: Domain: Pipeline Design ยท Difficulty: medium ## Problem Two DAGs are glued by a time offset: the orders DAG schedules at 2am and usually finishes by 2:45am, and the marketing DAG schedules at 3am hoping the upstream is done. When the orders DAG runs slow and finishes at 3:15am, the marketing DAG already started at 3am and read stale state. Apply the cross-DAG framing this section just taught and replace the time offset with an asset trigger. The asset is structurally a lakehouse-format table (Iceberg, Delta, or Hudi) that the orders DAG writes and the marketing DAG reads; the lakehouse format gives the table snapshot semantics that asset-aware orchestrators trigger on. Replace the existing plain Snowflake mart with a lakehouse-format table; add a second orchestrator (or use the same orchestrator's asset-trigger feature) so the marketing DAG fires when the asset is fresh. ## Related - [All practice problems](https://datadriven.io/problems) - [Mock interview mode](https://datadriven.io/interview/two-dags-are-glued-by-a-time-offset-the-orders-dag-schedule-bfb6218e) - [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.