Overview
In today’s data-driven landscape, ensuring data accuracy during migrations, integrations, and transformations is crucial. Our platform automates file-to-file comparison to validate that your source and target datasets match perfectly — across metrics, schema, and values.
Key Data Comparison Validations
1. Aggregation Testing
Validate that aggregated or summarized data matches the detailed records between source and target files. Our engine compares sum, count, average, min, max, and other aggregate metrics.
2. Data Mismatch Detection
Identify missing, extra, or altered data across files — even when order or formats differ.
- Detects missing or duplicate rows
- Column-by-column value comparison
- Tolerance support for numeric/time differences
3. Schema Validation
Validate structure consistency including data types, column counts, headers, and delimiters.
4. Random Chunk Testing
Validate representative sampled subsets of very large datasets for faster runtime.
How It Works
- Hash-based record comparison
- Metadata-driven schema validation
- Rule-based aggregate validation
- Sampling algorithms for large datasets
Benefits
- Early detection of data integrity issues
- Automated & repeatable validation workflows
- Supports CSV, Excel, JSON, Parquet
- Integrates with ETL, Data Warehouse & CI/CD
Use Cases
- Data migration validation
- ETL output verification
- Report reconciliation
- Vendor data feed comparison