**Deequ**—AWS data quality library. **Checks:** Completeness (non-null), uniqueness, consistency (constraints), statistics. **Example:** customer_id unique; age 0–120. **Output:** CloudWatch metrics. **Glue:** Profiling integration. **Why:** Code-defined checks; CI integration....
Pro-Move: Anomaly detection + CI integration. Red Flag: Ad-hoc checks without automation.
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like Citi. While less common, it tests deeper understanding that distinguishes strong candidates.
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.
Deequ—AWS data quality library. Checks: Completeness (non-null), uniqueness, consistency (constraints), statistics. Example: customer_id unique; age 0–120. Output: CloudWatch metrics. Glue: Profiling integration. Why: Code-defined checks; CI integration. Scalability: AnomalyDetection for drift. Cost: Minimal—runs with Glue. Production: Checks in code; lineage in catalog.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding 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.