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How do you store streaming data in Delta Lake and handle schema evolution?

Spark/Big Datahard0.5 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Meesho
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
102 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Stream data to Delta Lake with `spark.readStream.format('delta')` and write using `.writeStream.format('delta').option('checkpointLocation', path).start()`. Schema evolution: Enable with `option('mergeSchema', 'true')` or `option('overwriteSchema', 'true')`. For additive columns, use `mergeSchema`; new columns get null for old records....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Meesho. The answer also includes follow-up discussion points that interviewers commonly explore.

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