A Python tool that receives a dataset and a set of validation rules, checks the data's integrity, and generates a comprehensive report on any issues found.
In real-world data pipelines, you cannot safely assume that incoming files are flawless. A required column might disappear, dates can arrive in an invalid format, identifiers might be duplicated, and missing values can easily break downstream processing or models.
This tool acts as a layer of defense before loading or processing data, ensuring that anomalies are caught early.
As an example, if you provide a traffic dataset (e.g., trips.csv), the tool will verify:
- Schema completeness: Do all required columns exist?
- Missing values: Are there any null or empty cells?
- Uniqueness: Are there duplicate rows or IDs?
- Data types: Are the values of the correct type (e.g., integer, string, boolean)?
- Range limits: Are numerical values within logical, expected boundaries?
- Custom logic: Do specific values comply with your predefined business rules?
The output is a clear JSON or HTML report detailing which checks passed, which failed, and exactly where the problems are located.