Reviewed by Aditya Kumar · Last reviewed 2026-03-24
IR is the compute layer that executes ADF activities—data movement, transformations, and external calls. Types: (1) Azure IR: Managed, cloud-native—for cloud-to-cloud copies, Data Flows, Databricks calls. No maintenance. (2) Self-hosted IR: Install on your VM or container—for...
Red Flag: Not knowing the difference between Azure and Self-hosted IR. Pro-Move: Explaining when to use each and HA for Self-hosted—shows production experience.
This easy-level Cloud/Tools question appears frequently in data engineering interviews at companies like EY, Incedo, Tech Mahindra. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
IR is the compute layer that executes ADF activities—data movement, transformations, and external calls. Types: (1) Azure IR: Managed, cloud-native—for cloud-to-cloud copies, Data Flows, Databricks calls. No maintenance. (2) Self-hosted IR: Install on your VM or container—for on-prem, VNet, or private data sources. You manage scaling and uptime. (3) Azure-SSIS IR: For running SSIS packages in Azure. Why it matters: Data never flows through ADF; it flows through IR. Network topology (cloud vs. on-prem) dictates which IR to use. Scalability: Azure IR auto-scales; Self-hosted IR scales by adding nodes. Cost: Azure IR is per-activity; Self-hosted IR is your VM cost. Trade-off: Self-hosted adds ops but enables secure on-prem access; Azure IR simplifies but can't reach private networks. At scale, use a Self-hosted IR in a high-availability setup for critical hybrid pipelines.
Red Flag: Not knowing the difference between Azure and Self-hosted IR. Pro-Move: Explaining when to use each and HA for Self-hosted—shows production experience.
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According to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 3 companies. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.