Build a CSV Data Quality API with FastAPI, Pandas, Pytest, and Docker
📰 Dev.to · Bob Oner
Learn to build a CSV data quality API using FastAPI, Pandas, Pytest, and Docker for efficient data validation and processing
Action Steps
- Build a FastAPI application to handle CSV file uploads
- Use Pandas to read and validate CSV files
- Configure Pytest for unit testing and integration testing
- Containerize the API using Docker for easy deployment
- Test the API with sample CSV files to ensure data quality checks are working correctly
Who Needs to Know This
Data engineers, data scientists, and backend developers can benefit from this API to ensure high-quality data for analytics and operations
Key Insight
💡 Using FastAPI, Pandas, Pytest, and Docker together enables efficient and reliable CSV data quality checks
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🚀 Build a CSV data quality API with FastAPI, Pandas, Pytest, and Docker to ensure high-quality data for analytics and operations
Key Takeaways
Learn to build a CSV data quality API using FastAPI, Pandas, Pytest, and Docker for efficient data validation and processing
Full Article
CSV files are still everywhere. They appear in internal operations, analytics workflows, data...
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