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PYSPARK • LESSON 216

Data Quality Flag Matrix

How can we compute multiple granular data quality rule flags for every incoming record?

Advanced3 Minutes850 XP
🤔 THE QUESTION

How can we compute multiple granular data quality rule flags for every incoming record?

💡 WHAT IS IT?

Assigning individual boolean columns for each validation rule creates a transparent audit trail.

🎯 WHAT IS IT USED FOR?

Enterprise data quality scoring and tracking compliance metrics in monitoring dashboards.

💻 EXAMPLE
df_audit = df.withColumn("null_key_flag", col("id").isNull()).withColumn("invalid_email_flag", ~col("email").rlike("@")).withColumn("negative_price_flag", col("price") < 0)

🎯 Mission Objectives

Practice typing production-grade PySpark code for Data Quality Flag Matrix.

  • Multi-rule quality flags
  • Audit trail generation
  • Data governance scoring