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. Read CSV, filter, write: `df = spark.read.option('header',True).csv('path'); filtered = df.filter(col('status')=='active');...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Read CSV, filter, write: df = spark.read.option('header',True).csv('path'); filtered = df.filter(col('status')=='active'); filtered.write.mode('overwrite').saveAsTable('target_table'). Or .parquet('path'). Best practice: Use predicate pushdown; validate row count.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Full scan filter. Pro-Move: 'Predicate pushdown; validate count.'
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