Explain how you ensure integrity and consistency between SQL, Python, and business intelligence tools when building data pipelines and reporting for Workday.
Use the supplied SQL, Python, and Workday reporting outputs to identify records that are missing or inconsistent across layers. Return only records requiring investigation.
worker_id, report_month, all source metric values, and consistency_status.report_month, then worker_id.| Column | Type | Description |
|---|---|---|
| worker_idPK | VARCHAR(20) | Workday worker identifier |
| report_monthPK | DATE | First day of the reporting month |
| headcount | INT | Worker headcount metric produced by SQL |
| total_compensation | DECIMAL(12,2) | Compensation metric produced by SQL |
| Column | Type | Description |
|---|---|---|
| worker_idPK | VARCHAR(20) | Workday worker identifier |
| report_monthPK | DATE | First day of the reporting month |
| headcount | INT | Worker headcount metric produced by Python |
| total_compensation | DECIMAL(12,2) | Compensation metric produced by Python |
| Column | Type | Description |
|---|---|---|
| worker_idPK | VARCHAR(20) | Workday worker identifier |
| report_monthPK | DATE | First day of the reporting month |
| headcount | INT | Worker headcount metric published to BI |
| total_compensation | DECIMAL(12,2) | Compensation metric published to BI |