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Write code to read data from Delta Lake in S3 and perform upsert based on primary key

Spark/Big Datamedium0.6 min readPremium

**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...

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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
Walmart
Key Concepts Tested
partitionspark

Why This Question Matters

This medium-level Spark/Big Data question appears frequently in data engineering interviews at companies like Walmart. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (partition, spark) will help you answer variations of this question confidently.

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Expert Answer
112 words

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(). Alternative with Spark 3+: new_df.write.format("delta").mode("overwrite").option("overwriteSchema", "true").save("path") plus merge logic. Production: use partition pruning on merge key; run vacuum/optimize periodically; set proper isolation levels; consider merge schema for evolution; monitor merge duration and file count.

Scalability Trade-offs: Partition on merge key; avoid full table scan. Run OPTIMIZE/VACUUM post-merge. Monitor file count growth.

Cost Implications: MERGE without partition pruning = full scan. Right-sized partitions = 10x faster.

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