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Write a transformation in PySpark to join and clean multiple raw input sources

Spark/Big Datamedium0.7 min readPremium

**Why It Matters (Architectural Logic)**: Multi-source joins require consistent keys, null handling, and skew mitigation. Netflix-scale = partition by business dims, broadcast small tables. Join and clean multiple sources with consistent keys and null handling: ```python...

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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
Netflix
Key Concepts Tested
joinpartitionpythonspark

Why This Question Matters

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

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Expert Answer
132 wordsIncludes code

Why It Matters (Architectural Logic): Multi-source joins require consistent keys, null handling, and skew mitigation. Netflix-scale = partition by business dims, broadcast small tables.

Join and clean multiple sources with consistent keys and null handling:

df1_clean = df1.dropDuplicates(["id"]).na.fill({"region": "Unknown"})
df2_clean = df2.filter(F.col("amount") > 0).dropDuplicates(["id"])
joined = df1_clean.join(df2_clean, "id", "left").drop(df2_clean.id)
final = joined.withColumn("valid", F.when(F.col("amount").isNull(), False).otherwise(True))

Best practices: use consistent join keys; handle skew with salt/broadcast; validate row counts before/after; log join key match rates; use coalesce for nullable columns; define cleaning rules as config. For Netflix-scale: partition by business dimensions, avoid cross-joins, use broadcast for small dimension tables.

Scalability Trade-offs: Broadcast small dims; salt for skew. Validate row counts; log join match rate. Avoid cross-joins.

Cost Implications: Shuffle on large tables = 70%+ of cost. Broadcast = 10-100x faster when applicable.

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