BSc IT Project Guide: Data Validation to Ensure Data Follows Required Formats
1. Project Title
Data Validation to Ensure Data Follows Required Formats
2. Objective
To develop a software tool that ensures data consistency and accuracy by validating inputs against predefined formats, constraints, and business rules.
3. Project Scope
- Validate numerical, textual, and date fields.
- Ensure required fields are not empty.
- Match input formats using regular expressions.
- Provide real-time feedback for incorrect entries.
- Generate validation reports for batch data.
4. Tools and Technologies
- Programming Language: Python/JavaScript
- Frameworks: Django/Flask or Node.js
- Libraries: Pandas, Cerberus, Regex, JSONSchema
- Database: MySQL, MongoDB
- Frontend: HTML, CSS, JavaScript (React optional)
5. System Design
- Input Layer: Accept user or file-based data inputs.
- Validation Engine: Apply rules and return results.
- Error Handler: Logs and communicates format violations.
- Output: User interface or reports indicating data validity.
6. Methodology
1. Requirement Analysis
2. System Design (UML, Flowcharts)
3. Implementation of validation rules (Regex, Schema checks)
4. Integration with frontend or file import module
5. Testing with various datasets
6. Documentation and Final Presentation
7. Expected Outcome
- A functional data validation system capable of real-time
and batch data verification.
- Enhanced data quality and minimized format-related errors.
8. Future Scope
- Integration with large-scale ETL pipelines.
- Add machine learning for anomaly and pattern detection.
- Support for multilingual data and international formats.
9. References
- Documentation of Cerberus, JSON Schema, and Regex
libraries.
- Online resources and research papers on data validation techniques.