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Describe a scenario where AWS Data Pipeline is preferred over Glue. Why?

Cloud/Toolseasy0.5 min readPremium

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Frequency

Low

Asked at 1 company

Why This Question Matters

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

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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

100 words

Architectural logic: Data Pipeline = templated, EC2/EMR-managed; Glue = serverless Spark, Catalog, modern ETL. When Data Pipeline wins: (1) Cost—simple daily RDS→S3 copy can be cheaper than Glue DPUs; (2) Legacy—existing Pipeline definitions; (3) EC2/EMR coupling—Data Pipeline natively manages clusters for custom non-Spark code; (4) Simplicity—straight copy needs no Spark. Scalability: Data Pipeline scales via instance count; Glue via DPU. Cost: For low-frequency, simple jobs, Data Pipeline can undercut Glue. When to use Glue: Complex Spark ETL, schema evolution, Glue Catalog, serverless preference, or modern data lake. Best practice: Evaluate cost and complexity; Glue is default for new data lakes.

The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations covering performance optimization and real-world examples.

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