12 messy employee records, 6 pipeline stages. Follow the data as it transforms from raw CSV, with nulls, duplicates, and outliers, into a clean, aggregated dataset ready for analysis.
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The pipeline ingests 12 employee records from a CSV export. Data is unvalidated: it arrives exactly as sent.
| ID | Name | Age | Salary | Dept | Issues |
|---|---|---|---|---|---|
| 1 | Alice | 32 | $85,000 | Engineering | ✓ |
| 2 | Bob | 28 | $72,000 | Engineering | ✓ |
| 3 | Carol | — | $91,000 | Marketing | Missing |
| 4 | Dave | 45 | — | Sales | Missing |
| 5 | Eve | 31 | $999,999 | Engineering | Outlier |
| 6 | Frank | 38 | $67,000 | Marketting | Inconsistent |
| 7 | Grace | 29 | $78,000 | Sales | ✓ |
| 8 | Bob | 28 | $72,000 | Engineering | Duplicate |
| 9 | Iris | 36 | $88,000 | Marketing | ✓ |
| 10 | Jack | 41 | $95,000 | Sales | ✓ |
| 11 | Karen | 33 | $81,000 | Engineering | ✓ |
| 12 | Leo | "thirty" | $74,000 | Marketing | Type Error |
Raw data arrives from multiple sources: CSV uploads, API responses, database dumps.
Output
12 raw records
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