How can we construct a production validation pipeline with schema enforcement, quality flags, and quarantine persistence?
Combining null checks, boundary filters, regex validation, and timestamp auditing in a complete Data Quality stage.
Enterprise ingestion frameworks serving mission-critical analytics with automated quality SLA checks.
df_dq = df.withColumn("dq_timestamp", current_timestamp()).withColumn("is_valid", col("user_id").isNotNull() & col("email").rlike("@") & (col("age") >= 18))
df_passed = df_dq.filter(col("is_valid") == True)Practice typing production-grade PySpark code for Production Data Quality Pipeline.