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Data Factory vs. Databricks: When to use which?

Cloud/Toolseasy0.3 min read

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...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
179
questions in Cloud/Tools
Difficulty Split
104E|27M|48H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Capgemini
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
pythonspark

Why This Question Matters

This easy-level Cloud/Tools question appears frequently in data engineering interviews at companies like Capgemini. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python, spark) will help you answer variations of this question confidently.

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
65 words

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. Cost: ADF for lightweight; Databricks for compute-heavy. Team fit: Low-code vs. code-first.

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