Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architecture**: Logic Apps for triggers, approvals, lightweight orchestration; ADF for data movement and heavy transforms. Logic Apps calls ADF via HTTP (Execute Pipeline). **Latency**: Logic App adds 1–5+ seconds per action. Chaining many steps = additive latency. Use Logic...
This hard-level Cloud/Tools question appears frequently in data engineering interviews at companies like Virtusa. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (etl) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Architecture: Logic Apps for triggers, approvals, lightweight orchestration; ADF for data movement and heavy transforms. Logic Apps calls ADF via HTTP (Execute Pipeline). Latency: Logic App adds 1–5+ seconds per action. Chaining many steps = additive latency. Use Logic App only for trigger + optional approval; let ADF do the work. Async: ADF pipeline runs async; Logic App gets run ID, doesn't wait. For status, poll or use webhooks. Scalability: Logic App concurrency limits; ADF scales independently. Cost: Logic App charges per action; ADF charges per activity run. Don't use Logic App for data transform—use ADF. Best practice: Logic App triggers ADF for ETL; avoid sync waits; use Logic App for human steps (approval) only.
Pro-Move: 'We use Logic App for file-drop trigger + approval gate, then async ADF—Logic App is <10 actions, ADF does 50+ activities.' Red Flag: Putting data transformation in Logic App—use ADF; Logic App is for workflow, not ETL.
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According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.