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...
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.
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.
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.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Cloud/Tools interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.