**Semantics:** explode = one-to-many (array → rows). flatten = array of arrays → single array (no new rows). collect_list = many-to-one (aggregate to array per group). **Use Cases:** explode: normalize nested JSON. flatten: merge nested array before processing. collect_list:...
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like TCS. 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.
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.
Semantics: explode = one-to-many (array → rows). flatten = array of arrays → single array (no new rows). collect_list = many-to-one (aggregate to array per group).
Use Cases: explode: normalize nested JSON. flatten: merge nested array before processing. collect_list: group by key, collect values (e.g., order items per order_id).
Scalability: explode multiplies rows—risk of skew. collect_list can create huge arrays—monitor group sizes. flatten keeps row count; watch for very nested structures. explode_outer to preserve nulls.
explode('items') # 1 row → N rows
flatten(array_of_arrays)
collect_list('item').alias('items')
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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.