A data validation pipeline automates checks that compare incoming data with quality constraints, then reports status so teams can respond before reliability problems spread. section 14.3.6
Build the pipeline around a practical monitoring loop:
- Identify critical quality dimensions, such as completeness, accuracy, timeliness, and consistency. section 14.3.6
- Define acceptable ranges, run checks regularly, and produce a status report for each dimension. section 14.3.6
- Alert responsible teams when results fall outside range, balancing false positives against false negatives. section 14.3.6
- Review results over time and update constraints as understanding of the data and use case improves. section 14.3.6
Missing-value constraint
A pipeline can require complete data while allowing no more than 5% missing values, then report and alert when the threshold is exceeded. section 14.3.6
