Zearn Question Pipeline
Python Data Pipeline for Large-Scale Document Processing
Technologies
An automated data pipeline built in Python for extracting, validating, and processing content from various document formats at scale.
Pipeline Architecture
The system processes PDF documents through multiple stages:
1. **Extraction Stage:** - PDF parsing with layout preservation - Image extraction and classification - Text extraction with structure detection
2. **Validation Stage:** - Schema validation for extracted content - Data quality checks and anomaly detection - Error logging and reporting
3. **Processing Stage:** - Content transformation and normalization - AI-assisted classification - Output generation in multiple formats
Technical Details
- Modular pipeline architecture for maintainability - Parallel processing for improved throughput - Comprehensive error handling and recovery - Progress tracking and monitoring - Output validation before final export
Scale
Designed to process 90,000+ items with consistent quality and error handling.
Key Highlights
- Built pipeline processing 90,000+ items
- Implemented parallel processing for throughput
- Designed robust error handling and recovery
- Created comprehensive validation and quality checks