Essential cookies keep authentication working. With your permission, we also use analytics cookies to understand and improve the product. Read our Privacy Policy

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

Triggers in ADF, especially tumbling window triggers.

SQLmedium0.5 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**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...

🤖 Analyze Your Answer
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.

⚡
Pro Tip

RED FLAG: Saying 'tumbling windows run at fixed intervals' without discussing idempotency or max concurrency. PRO MOVE: 'We set max concurrency to 2 with 1hr windows to cap cost while meeting our 4hr SLA; each window is idempotent so backfills are safe.'

Want all answers as a PDF for offline study?
Seven focused volumes with 750+ in-depth answers — Answer Vault →

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

Level up your prep

Recommended
Educative
Educative Unlimited

800+ hands-on courses — Grokking System Design, Coding Patterns, and AI mock interviews for your DE loop.

Start learning →

Some links below are affiliate links. If you buy through them we may earn a small commission at no extra cost to you — it helps keep DataEngPrep free.

According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 2 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

← Back to all questionsMore SQL questions →
Categories
All QuestionsSQLSpark / Big DataPython / CodingSystem DesignCloud / ToolsBehavioral
By Company
AmazonGoogleDatabricksSnowflakeAWSAzureMicrosoftNetflixUberTCS
Interview Guides
All GuidesTop SQL QuestionsTop Spark QuestionsPySpark QuestionsTop Python QuestionsTop System DesignKafka QuestionsAirflow QuestionsSQL Window FunctionsETL QuestionsData Modeling
Products
AI Interview CoachAnswer AnalyzerSQL PlaygroundResume AnalyzerAnswer Vault PDFsPricing
Company
About & Editorial PolicyContact UsAI DisclosureDisclaimerTerms of ServicePrivacy Policy
© 2026 DataEngPrep.tech. All rights reserved.
AboutBlogContactDisclaimer