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Home/Questions/Cloud/Tools/What is the role of AWS Lambda in a data engineering pipeline?

What is the role of AWS Lambda in a data engineering pipeline?

Cloud/Toolsmedium0.6 min read

Lambda is serverless compute for event-driven, short-lived tasks. Roles: (1) Orchestration: Trigger Glue, EMR, or Step Functions when files land (S3), events arrive (Kinesis), or schedules fire. (2) Light transforms: JSON→Parquet, small aggregations, API calls. (3) Alerts:...

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Frequency
Low
Asked at 3 companies
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
EYIncedoTech Mahindra
Interview Pro Tip

Red Flag: Suggesting Lambda for 'all ETL'—ignores timeout and memory limits. Pro-Move: Describing Lambda as trigger/orchestrator for Glue/EMR—shows correct service boundaries.

Key Concepts Tested
spark

Why This Question Matters

This medium-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 (spark) will help you answer variations of this question confidently.

How to Approach This

Break this problem into components. Identify the core trade-offs involved, then walk the interviewer through your reasoning step by step. Demonstrate awareness of edge cases and production considerations - this is what separates good answers from great ones.

Expert Answer
113 words

Lambda is serverless compute for event-driven, short-lived tasks. Roles: (1) Orchestration: Trigger Glue, EMR, or Step Functions when files land (S3), events arrive (Kinesis), or schedules fire. (2) Light transforms: JSON→Parquet, small aggregations, API calls. (3) Alerts: Validate data, send Slack/PagerDuty on anomalies. Why: No server management; scale to thousands of concurrent invocations; pay per request and duration. Limitations: 15-min timeout, 10GB memory, 6MB payload (sync). Not for heavy Spark or large data—use Glue/EMR. Scalability: Lambda scales automatically; cold starts add latency (mitigate with provisioned concurrency). Cost: Very cheap for sporadic, short jobs; can spike with high event volume. Trade-off: Use Lambda for glue logic and triggers; keep heavy processing in dedicated compute.

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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According 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.

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