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Home/Questions/SQL/Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.

Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.

SQLmedium0.5 min read

Schedule: Fixed cadence (cron, every N mins). Predictable batch windows; simple ops. Tumbling window: Fixed non-overlapping intervals; fires once per window. Ideal for idempotent, exactly-once semantics—no overlap means no double-processing. Event-based: Fires on blob created,...

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Frequency
Low
Asked at 3 companies
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
FedEx DataworksNihilentVirtusa
Interview Pro Tip

Red Flag: Describing trigger types without mentioning concurrency limits or cost implications. Pro-Move: 'We use tumbling windows for exactly-once idempotency; event triggers for CDC with a cap of 10 concurrent pipelines to avoid ADF throttling'—shows operational awareness.

Key Concepts Tested
partitionwindow

Why This Question Matters

This medium-level SQL question appears frequently in data engineering interviews at companies like FedEx Dataworks, Nihilent, Virtusa. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, window) will help you answer variations of this question confidently.

How to Approach This

Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
106 words

Schedule: Fixed cadence (cron, every N mins). Predictable batch windows; simple ops. Tumbling window: Fixed non-overlapping intervals; fires once per window. Ideal for idempotent, exactly-once semantics—no overlap means no double-processing. Event-based: Fires on blob created, queue message, etc. Enables near real-time pipelines. Why each matters: Schedule = predictable cost and SLAs; tumbling = deterministic boundaries for windowed aggregations; event = low latency but variable cost and concurrency. Scalability: Event triggers can spike concurrency; configure max pipeline runs to avoid throttling. Cost: More frequent triggers = more pipeline runs = higher cost. Best practice: Use trigger parameters for dynamic table/partition; set retry with backoff for transient failures.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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