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How do you handle bad data in Databricks?

Spark/Big Dataeasy0.5 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-25

**Situation**: Faced competing demands—multiple pipelines, stakeholders, deadlines. **Task**: Deliver impact while maintaining quality and preventing burnout. **Action**: (1) Prioritized by business impact and SLA risk. (2) Used ROI (value/time); WIP limits; timeboxing. (3)...

🤖 Analyze Your Answer

Frequency

Low

Asked at 1 company

Why This Question Matters

This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like PWC. While less common, it tests deeper understanding that distinguishes strong candidates.

How to Approach This

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.

Expert Answer

105 words

Situation: Faced competing demands—multiple pipelines, stakeholders, deadlines. Task: Deliver impact while maintaining quality and preventing burnout. Action: (1) Prioritized by business impact and SLA risk. (2) Used ROI (value/time); WIP limits; timeboxing. (3) Communicated trade-offs—'Adding X pushes Y by N days.' (4) Maintained backlog with tech-debt capacity. Result: Shipped on time; zero incidents; stakeholder alignment on deferrals. Detail: Bad data in Databricks: (1) Validate with expectations; (2) expectOrDrop to quarantine; (3) Log to bad_records table; (4) Schema enforcement; (5) Data quality dashboards. Example: .expectOrDrop('valid', 'col IS NOT NULL'). Best practice: fail fast on critical; quarantine for review; monitor rejection rates; fix at source when possible.

⚡

Pro Tip

Red Flag: Fail all on bad row. Pro-Move: 'expectOrDrop; quarantine table; monitor rates.'

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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

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