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PYSPARK • LESSON 218

Splitting Clean and Invalid Datasets

How can we split an ingestion stream into clean valid records for bronze tables and invalid records for triage?

Advanced3 Minutes870 XP
🤔 THE QUESTION

How can we split an ingestion stream into clean valid records for bronze tables and invalid records for triage?

💡 WHAT IS IT?

A master is_valid boolean condition enables filtering the DataFrame into two distinct destination streams.

🎯 WHAT IS IT USED FOR?

Ensuring bronze-to-silver ETL pipelines never halt while capturing flawed records for analysis.

💻 EXAMPLE
is_valid_expr = col("id").isNotNull() & (col("amount") > 0) & col("email").isNotNull()
df_clean = df.filter(is_valid_expr)
df_invalid = df.filter(~is_valid_expr)

🎯 Mission Objectives

Practice typing production-grade PySpark code for Splitting Clean and Invalid Datasets.

  • Dataset splitting
  • Master validity condition
  • Dual-destination ETL branching