Why is data quality testing essential in a big data pipeline?
- A Silent quality failures propagate wrong numbers into decisions without any job failing
- B It is required by regulation
- C It speeds up processing
- D It reduces storage costs
Answer
Silent quality failures propagate wrong numbers into decisions without any job failing
Row count checks, null rate thresholds and referential integrity tests catch what a successful job status does not reveal.





