**Why It Matters (Architectural Logic)**: MERGE enables CDC and incremental loads—update existing, insert new. Partition pruning on merge key is critical for performance.
Delta Lake MERGE supports upsert by primary key. Read existing: `from delta.tables import DeltaTable; delta = DeltaTable.forPath(spark, "s3://bucket/table")`. Merge: `delta.alias("t").merge(new_df.alias("s"), "t.id = s.id").whenMatchedUpdate(set={"col": "s.col"}).whenNotMatchedInsertAll().execute()`....
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 Walmart. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.