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Data Factory vs. Databricks: When to use which?

Cloud/Toolseasy0.3 min readPremium

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Asked at 1 company

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Interview Pro Tip

Red Flag: Using ADF for complex Spark logic. Pro-Move: 'ADF for copy + schedule; Databricks for all transforms—clean separation, team knows where logic lives.'

Key Concepts Tested

Expert AnswerPremium

65 wordsInterview-ready

Why differentiate: Wrong tool = slow, expensive, or unmaintainable. ADF: Orchestration, simple copy, low-code, diverse sources, on-prem connectors. Databricks: Massive Spark processing, custom Python/Scala, ML, performance-critical, fine-grained compute control. Architectural logic: ADF = plumbing; Databricks = heavy lifting. Together: ADF orchestrates, triggers Databricks for transforms. Scalability: ADF scales with DIU; Databricks with cluster....

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Capgemini. The answer also includes follow-up discussion points that interviewers commonly explore.

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