**Why Explode:** Nested JSON (items array per order) → one row per item for joins/aggregation. Normalization for star schema. **Functions:** explode()—one-to-many; null/empty arrays drop row. explode_outer()—keeps row, null for empty. posexplode()—adds position index. For...
This medium-level Python/Coding question appears frequently in data engineering interviews at companies like Lumiq. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, partition, spark) will help you answer variations of this question confidently.
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.
Why Explode: Nested JSON (items array per order) → one row per item for joins/aggregation. Normalization for star schema.
Functions: explode()—one-to-many; null/empty arrays drop row. explode_outer()—keeps row, null for empty. posexplode()—adds position index. For multiple array cols: arrays_zip then explode.
Scalability Risk: Large arrays cause row explosion—1 row with 10K items → 10K rows. Can cause skew. Use explode then repartition or broadcast the small side for joins. Monitor for OOM.
df.withColumn('item', explode('items'))
df.withColumn('item', explode_outer('items')) # keep nulls
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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.