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Home/Questions/Cloud/Tools/Describe AWS Glue components and their functions.

Describe AWS Glue components and their functions.

Cloud/Toolshard0.3 min readPremium

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)...

🤖 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
EY
Interview Pro Tip

Red Flag: 'Glue is like EMR'—different use cases. Pro-Move: 'Glue for serverless; we use Crawlers sparingly (cost)—schema in code for prod; Catalog for discovery.'

Key Concepts Tested
etlpythonspark

Why This Question Matters

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

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
69 words

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. Trade-offs: Less control than EMR; cold start. When: Serverless preference; lake-centric; diverse sources.

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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