Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Fetch API → DataFrame: Use `requests` or `urllib` to call API, parse JSON, create RDD/DataFrame. Example: `import requests; r =...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Fetch API → DataFrame: Use requests or urllib to call API, parse JSON, create RDD/DataFrame. Example: import requests; r = requests.get(url); data = r.json(); df = spark.createDataFrame(data['results']). For pagination: loop with next_page; collect pages; concatenate. For large APIs: use async, batch, or streaming. Best practice: Handle rate limits; cache tokens; use connection pooling; validate schema; consider incremental ingestion.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: API without rate limits. Pro-Move: 'Retry; cache; pagination; incremental.'
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