In 2017, RIPP presented millions of metadata records, and the application was consuming a lot of data. How did you identify and resolve data quality issues in that scenario?
💡 Model Answer
When dealing with millions of metadata records, data quality issues surface as missing fields, inconsistent formats, or duplicate entries. First, perform data profiling to understand distribution, null rates, and anomalies. Tools like Great Expectations or AWS Glue DataBrew can automatically generate tests. Use schema validation to enforce required fields and data types. For lineage, capture source, transformation, and destination metadata to trace errors. Implement automated alerts when quality thresholds are breached. Finally, create a remediation workflow: flag bad records, either drop, correct, or route them to a quarantine table for manual review. Continuous monitoring and periodic audits ensure that the data remains trustworthy as new records arrive.
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