PySpark has no native API source; the pattern is driver fetch or executor fetch. **Approaches**: (1) **Driver + parallelize**: data = requests.get(url).json(); df = spark.createDataFrame(data). Scales to API response size (typically MBs); driver is bottleneck. (2)...
This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Altimetrik, Infosys. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (airflow, 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.
PySpark has no native API source; the pattern is driver fetch or executor fetch. Approaches: (1) Driver + parallelize: data = requests.get(url).json(); df = spark.createDataFrame(data). Scales to API response size (typically MBs); driver is bottleneck. (2) mapPartitions on executor: Pass partition of IDs to each task; each calls API. Scales to many IDs but risks rate limiting and API abuse. (3) Orchestrator + landing: Airflow/Prefect fetches API → lands to S3/GCS → Spark reads. Decouples API from Spark; supports retries, backfill, idempotency. Scalability trade-off: Executor-based fetch can DDoS the API; use rate limiting, backoff, and connection pooling. Cost: API rate limits may require smaller clusters; landing approach adds storage cost. Architectural logic: Prefer landing raw to object storage; keeps lineage, enables reprocessing, and respects API contracts. Best practice: Land raw JSON; validate schema; use exponential backoff; never fetch in a tight loop.
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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 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.