Why Glue: Serverless ETL on AWS—no cluster management; pay per DPU. Architectural logic: (1) Data Catalog—metadata; table definitions. (2) Crawlers—discover schema; populate catalog. (3) ETL Jobs—Spark or Python; transform. (4) Workflows—orchestrate jobs and crawlers. (5) DataBrew—visual prep. (6) Schema Registry—streaming schema evolution. (7) Interactive Sessions—notebook dev. Scalability: Serverless; auto-scales. Cost: DPU hours; crawlers and jobs separate....
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 EY. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According 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.