In a scenario where data inconsistency is a major issue, how can AI/ML assist in enhancing the ETL testing process?
- Code Reviews, Source System Redesign, Data Partitioning, Incremental Loading
- Pattern Recognition, Anomaly Detection, Data Cleansing, Manual Validation
- Predictive Analytics, Automated Data Profiling, Enhanced Data Governance, Manual Data Comparison
- Traditional Testing Methods, Increased Documentation, Delayed Feedback, Minimal Automation
AI/ML can assist in handling data inconsistency through pattern recognition, anomaly detection, and data cleansing. This enhances ETL testing by automating processes that traditionally required manual effort, leading to more accurate and efficient results.
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