Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**%run**: Executes notebook inline in the same Spark session. Variables and imports are shared. Child notebook can modify parent's state. Use for quick composition, shared setup (imports, config). **dbutils.notebook.run()**: Executes in separate context. No variable sharing....
This easy-level Cloud/Tools question appears frequently in data engineering interviews at companies like TCS. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (spark) will help you answer variations of this question confidently.
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.
%run: Executes notebook inline in the same Spark session. Variables and imports are shared. Child notebook can modify parent's state. Use for quick composition, shared setup (imports, config). dbutils.notebook.run(): Executes in separate context. No variable sharing. Returns last expression. Supports timeout and arguments. Use for job workflows, parallel runs, parameterized execution. When to use which: %run for development, ad-hoc composition. dbutils.run for production jobs, workflows, passing parameters. Scalability: dbutils.run spawns isolated execution; %run shares resources. For 50 notebooks in a job, dbutils.run with explicit params is clearer. Best practice: Use dbutils.run in job workflows; use %run for local development and shared utilities.
Pro-Move: 'We use dbutils.notebook.run for all job steps—each returns a status dict; parent assembles results and fails fast on non-zero.' Red Flag: Using %run in scheduled jobs—hard to pass params and get status; dbutils.run is correct.
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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.