Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Spark read modes: spark.read loads data (default overwrites for file sources). Write modes: (1) overwrite—replaces target; (2)...
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Spark read modes: spark.read loads data (default overwrites for file sources). Write modes: (1) overwrite—replaces target; (2) append—adds data; (3) ignore—no-op if exists; (4) errorIfExists—fail if exists. Example: df.write.mode('append').parquet(path). For Delta: overwriteSchema; replaceWhere for conditional overwrite. Best practices: use dynamic partition overwrite for partitioned tables; use Delta merge for upserts; validate schema on read when using append.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: Append without schema check. Pro-Move: 'Dynamic overwrite; Delta merge; schema validate.'
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.