The main challenge is ensuring the framework can adapt to diverse file formats and evolving business rules. Another common issue is managing performance when handling large datasets. In my experience, scalable infrastructure like AWS services helps handle these challenges smoothly.
To create a solid framework, make it modular. This way, you can easily adjust for new data sources. Regularly update your validation rules and integrate real-time feedback loops to catch anomalies. Scalability is key, especially with AI systems.
- Validation Logic: The core rules that check for nulls, duplicates, and file structure.
- Automation Engine: Services like AWS Lambda or Step Functions to handle execution.
- Reporting: A system (like CloudWatch) that logs and alerts when validations fail. These components work in sync to ensure data is consistently validated.
The framework automates checks through predefined rules that are applied across all datasets. Null checks, structure validations, and duplicate detection are done using AWS Lambda functions or similar automation tools that run these checks and alert for any failures.