KRS Data & Financial Statement Analysis System | Python + Django

System Overview

Advanced platform for automatic analysis of National Court Register data and financial statements. The system processes over 1 million records monthly, providing 95% reduction in company data acquisition time.

System Architecture

1. Data Processing Engine

• KRS Data Pipeline

  • Distributed scraping
  • Incremental updates
  • Change detection
  • Data validation

• Performance Optimization

  • Parallel processing
  • Caching strategy
  • Load balancing
  • Resource management

2. Financial Analysis

• XML Processing Engine

  • Custom document parsers
  • Structure validation
  • Data versioning
  • Archiving

• Financial Analytics

  • Financial indicators
  • Trend analysis
  • Anomaly detection
  • Predictive metrics

3. Integration and Synchronization

• SharpSpring Connect

  • Real-time sync
  • Bi-directional flow
  • Error handling
  • Data mapping

• API Layer

  • RESTful endpoints
  • Batch processing
  • Rate limiting
  • Authentication

4. Admin Panel

• Monitoring Dashboard

  • Real-time stats
  • System health
  • Process tracking
  • Alert management

• Data Management

  • CRUD operations
  • Bulk actions
  • Audit logging
  • Custom filters

Performance Metrics

• 95% data acquisition time reduction
• 1M+ processed records monthly
• 99.9% data accuracy
• 100% update automation

Technology Stack

Backend Infrastructure

• Django framework
• Celery workers
• Redis cache
• Docker containers

Data Processing

• Custom XML parsers
• Financial algorithms
• ML models
• ETL pipelines

Conclusions and Results

The system demonstrates the effectiveness of automation in corporate data processing, providing significant acceleration of analytical processes while maintaining high accuracy.