Multi-Column Pipeline Transformations

How do we chain multiple withColumn transformations together in a fluent, readable ETL pipeline?

Intermediate⏱ 3 Minutes⭐ 350 XP

🤔 THE QUESTION

How do we chain multiple withColumn transformations together in a fluent, readable ETL pipeline?

💡 WHAT IS IT?

Chaining sequential withColumn operations to apply boolean conversions and metadata injection.

🎯 WHAT IS IT USED FOR?

Silver-layer lakehouse cleansing where records require type conversions and audit provenance tags.

💻 EXAMPLE

from pyspark.sql.functions import col, lit, when

cleaned_df = df \
    .withColumn("is_active", when(col("status") == "A", True).otherwise(False)) \
    .withColumn("source_system", lit("SAP_ERP"))

🎯 Mission Objectives

Practice typing production-grade PySpark code for Multi-Column Pipeline Transformations.

✓ Chain multiple withColumn operations
✓ Convert legacy status codes to booleans
✓ Inject source system lineage tag

🚀 Start Mission

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