DataEngPrep.tech
QuestionsPracticeAI CoachDashboardPacksBlog
ProLogin
Home/Questions/SQL/Triggers in ADF, especially tumbling window triggers.

Triggers in ADF, especially tumbling window triggers.

SQLmedium0.5 min read

**Architectural Logic:** ADF triggers orchestrate pipeline runs; tumbling window triggers partition time into fixed, non-overlapping intervals (e.g., 15min, 1hr). **Why tumbling over schedule:** Idempotency—each window processes mutually exclusive data, enabling safe retries and...

🤖 Practice this in AI Interview
Frequency
Low
Asked at 2 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
AccentureYash Technologies
Key Concepts Tested
partitionwindow

Why This Question Matters

This medium-level SQL question appears frequently in data engineering interviews at companies like Accenture, Yash Technologies. 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
100 words

Architectural Logic: ADF triggers orchestrate pipeline runs; tumbling window triggers partition time into fixed, non-overlapping intervals (e.g., 15min, 1hr). Why tumbling over schedule: Idempotency—each window processes mutually exclusive data, enabling safe retries and backfills. Scalability: Max concurrency controls parallel window execution; oversubscription can exhaust DTU/IR capacity. Cost: Higher concurrency = more runs = higher IR cost; tune window size vs. SLA. Trade-offs: Large windows reduce trigger overhead but increase latency; small windows improve granularity but multiply orchestration cost. Other triggers: schedule (cron), storage events (blob created), manual. Production pattern: Use dependency conditions for chained pipelines; parameterize paths for environment promotion.

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

This answer is partially locked

Unlock the full expert answer with code examples and trade-offs

Recommended

Start AI Mock Interview

Practice real interviews with AI feedback, track progress, and get interview-ready faster.

  • Unlimited AI mock interviews
  • Instant feedback & scoring
  • Full answers to 1,800+ questions
  • Resume analyzer & SQL playground
Create Free Account

Pro starts at $19/mo - cancel anytime

Just need answers for quick revision?

Download curated PDF interview packs

Interview Packs
R
P
A
S

Trusted by 10,000+ aspiring data engineers

AmazonGoogleDatabricksSnowflakeMeta

Free: Top 20 SQL Interview Questions (PDF)

Get the most asked SQL questions with expert answers. Instant download.

No spam. Unsubscribe anytime.

Related SQL Questions

mediumWrite an SQL query to find the second-highest salary from an employee table.FreemediumDemonstrate the difference between DENSE_RANK() and RANK()FreemediumDiscuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.FreemediumExplain the differences between Data Warehouse, Data Lake, and Delta LakeFreemediumExplain the differences between Repartition and Coalesce. When would you use each?Free

According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

← Back to all questionsMore SQL questions →