Verify Data Tool

The verify data tool validates structured datasets by applying a defined set of rules or a schema to every record. It supports standard formats such as CSV, JSON, and Parquet and outputs a per-record validity flag, a list of errors, and summary metrics. Conceptually, validation is rule-driven: each field is checked for type, range, format, and presence, while cross-field relationships can be enforced through derived rules. The tool does not transform data; it only assesses conformance and reports findings. Beneficiaries include data engineers preparing data for analytics, data scientists validating inputs for models, and QA teams auditing data quality in pipelines. Core differentiators are deterministic rule execution, detailed per-record error reporting, and flexible export options for downstream workflows. Use cases span ingestion validation, pre-model data curation, and ongoing quality monitoring in production data streams. Key features include schema-driven validation, custom regex checks, support for sampling on large datasets, and multi-format export. The tool integrates with ETL platforms and data lakes to provide independent quality assessments and actionable insight for data governance. By offering clear error codes and field-level validity rates, it enables fast remediation planning and continuous improvement of data quality. The approach emphasizes transparency, reproducibility, and scalable validation across diverse data domains and regions.

Typical workflows involve loading a dataset, applying a schema or rule-set, running validation, reviewing per-record results, and exporting a validation report for audits or dashboards.
This tool is valuable for teams seeking objective data quality metrics and documentation of conformity to data contracts.