Glue Catalog: Central metadata store (Hive-compatible)—enables querying S3 data via Athena/Redshift Spectrum without moving it. Glue Crawlers: Schema discovery and Catalog population—useful for ad-hoc sources; at scale, prefer schema-as-code to avoid crawler cost and drift. Glue...
Red Flag: Describing Glue as 'just ETL'—misses Catalog, Schema Registry, and orchestration. Pro-Move: Discussing crawler cost vs. schema-as-code—shows cost awareness.
This hard-level Cloud/Tools question appears frequently in data engineering interviews at companies like EY, Incedo, Tech Mahindra. 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.
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.
Glue Catalog: Central metadata store (Hive-compatible)—enables querying S3 data via Athena/Redshift Spectrum without moving it. Glue Crawlers: Schema discovery and Catalog population—useful for ad-hoc sources; at scale, prefer schema-as-code to avoid crawler cost and drift. Glue ETL Jobs: Serverless Spark for transforms; auto-scaling, pay-per-DPU. Glue DataBrew: Visual prep for non-engineers. Glue Schema Registry: Schema evolution for streaming (Kafka, Kinesis). Flow: Crawler/Manual schema -> Catalog -> ETL Job reads, transforms, writes -> Catalog updated. Why it matters: Decouples storage (S3) from compute; Catalog enables schema-on-read. Scalability: Jobs scale with DPU; Catalog has limits (e.g., table count). Cost: Crawlers and jobs charge per run; over-crawling drives cost up. Trade-off: Crawlers are convenient for discovery; for production, define schemas in IaC and use crawlers sparingly.
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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 3 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.