Intelligent ETL Monitoring and Data Quality Validation for Large-Scale Salesforce Integration Projects
DOI:
https://doi.org/10.71426/jcdt.v1.si1.pp28-35Keywords:
Salesforce integration, Extract, Transform, and Load (ETL) monitoring, Data quality, Anomaly detection, Data integration, Explainable machine learning.Abstract
Large Salesforce integration programs require continuous evidence that incoming records remain complete, structurally valid, semantically consistent, and safe for warehouse consumption. This paper develops an intelligent Extract, Transform, and Load (ETL) monitoring framework that combines deterministic data-quality rules with an explainable anomaly-prioritization model. A public CRM sales-opportunity snapshot containing 201 source records is projected onto a Salesforce-compatible multi-object representation. Fourteen naturally missing account values are preserved, while seven controlled fault classes are injected under a fixed seed to stress underrepresented failure modes without representing the data as proprietary Salesforce records. The resulting 100,000-row workload contains 15,955 review-worthy records. The deterministic rule layer recovered all benchmark-defined defects. Account-grouped evaluation on 15,000 records compared logistic regression, random forest, extremely randomized trees, and XGBoost. XGBoost obtained ROC-AUC 0.9995, PR-AUC 0.9976, F2 0.9953, and recall 1.0000; however, expected calibration error was 0.1968, showing that ranking quality did not imply calibrated probabilities. Rule validation, feature construction, and XGBoost inference processed 100,000 records in 0.483 s, corresponding to 207,092 rows/s in the reported environment. SHAP analysis identified account completeness, deal stage, close value, and sales-cycle duration as dominant signals. The design therefore uses machine learning for explainable triage while retaining deterministic rules as the warehouse acceptance authority.
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DOI prefix: 10.71426
