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How to Upsert Your Data Daily Using Spark

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
Nagarro
Key Concepts Tested
optimizationpartitionpythonspark
Expert AnswerPremium
93 wordsIncludes code examplesInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Daily upsert with Spark: Use Delta Lake `MERGE` or `foreachBatch` for streaming. Batch example: ```python deltaTable.alias('t').merge(df.alias('s'), 't.id = s.id') .whenMatchedUpdateAll().whenNotMatchedInsertAll().execute() ``` For partitioned tables, include partition columns in merge condition....
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 Nagarro. The answer also includes follow-up discussion points that interviewers commonly explore.

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