**Example 1 (Parse date)**: `@udf("date")`; `def parse_date(s): return datetime.strptime(s, "%Y-%m-%d").date()` **Example 2 (Pandas UDF, vectorized)**: `@pandas_udf("double")`; `def scale(s: pd.Series) -> pd.Series: return s * 2` **Example 3 (UDAF)**: Custom aggregate;...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like LTIMindtree. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python) will help you answer variations of this question confidently.
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.
Example 1 (Parse date): @udf("date"); def parse_date(s): return datetime.strptime(s, "%Y-%m-%d").date()
Example 2 (Pandas UDF, vectorized): @pandas_udf("double"); def scale(s: pd.Series) -> pd.Series: return s * 2
Example 3 (UDAF): Custom aggregate; GroupedData.agg(custom_udaf(col)).
Production: Prefer built-in. Pandas UDF for performance. Register and reuse. Test null handling.
Scalability Trade-offs: Python UDF slow; Pandas UDF 5–20x faster. Document purpose.
Cost Implications: UDF cost; avoid when built-in exists.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data 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.