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. Schema evolution in Delta Lake streaming: (1) Enable `mergeSchema` or `overwriteSchema` in write. (2) `mergeSchema=true`—add new columns;...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Meesho. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Schema evolution in Delta Lake streaming: (1) Enable mergeSchema or overwriteSchema in write. (2) mergeSchema=true—add new columns; old records get null. (3) Use option('mergeSchema', 'true') in writeStream. (4) For breaking changes: Alter table with ALTER TABLE ADD COLUMN; or write to new table. (5) Validate schema in streaming: use schema_of_json or enforce. Best practice: Version schemas; use evolution for additive changes only; test in dev first; document schema changes.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: overwriteSchema for additive. Pro-Move: 'mergeSchema; version; test first.'
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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.