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Home/Questions/Cloud/Tools/Discuss the key differences between AWS Glue, Lambda, and Data Pipeline for orchestrating data workflows.

Discuss the key differences between AWS Glue, Lambda, and Data Pipeline for orchestrating data workflows.

Cloud/Toolseasy0.4 min readPremium

Architectural comparison: Glue = serverless Spark, Catalog, complex ETL. Lambda = event-driven, 15-min max, lightweight. Data Pipeline = templated, EC2/EMR-managed. When to use: Glue for data lake ETL; Lambda for triggers, small tasks; Data Pipeline for simple, cost-sensitive...

🤖 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
EPAM
Key Concepts Tested
etlspark

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.

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
75 words

Architectural comparison: Glue = serverless Spark, Catalog, complex ETL. Lambda = event-driven, 15-min max, lightweight. Data Pipeline = templated, EC2/EMR-managed. When to use: Glue for data lake ETL; Lambda for triggers, small tasks; Data Pipeline for simple, cost-sensitive batch. Scalability: Glue via DPU; Lambda concurrency; Data Pipeline via instances. Cost: Lambda cheapest for small; Glue for Spark; Data Pipeline for simple batch. Best practice: Glue for ETL; Lambda for events; Data Pipeline only if legacy.

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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