Production Robust NULL Sanitation Pipeline

How do we construct an end-to-end data cleansing pipeline combining filtering, imputation, and coalesce?

Intermediate⏱ 3 Minutes⭐ 430 XP

🤔 THE QUESTION

How do we construct an end-to-end data cleansing pipeline combining filtering, imputation, and coalesce?

💡 WHAT IS IT?

A composite cleansing pipeline dropping corrupt IDs, imputing numeric defaults, and coalescing trimmed text.

🎯 WHAT IS IT USED FOR?

Silver-layer transaction processing pipelines in enterprise data warehouses.

💻 EXAMPLE

from pyspark.sql.functions import col, coalesce, lit, trim

clean_df = df \
    .filter(col("transaction_id").isNotNull()) \
    .fillna({"quantity": 1, "currency": "USD"}) \
    .withColumn("vendor", coalesce(trim(col("vendor_name")), lit("DIRECT")))

🎯 Mission Objectives

Practice typing production-grade PySpark code for Production Robust NULL Sanitation Pipeline.

✓ Filter valid transaction IDs
✓ Impute default quantity and currency
✓ Trim and coalesce vendor names

🚀 Start Mission

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