refactor: modernize codebase with modular architecture and improved maintainability

[f0rk3ed]

Summary

Comprehensive refactor of the csv2table codebase to improve maintainability, readability, and follow modern Python best practices while preserving 100% backward compatibility.

Changes Made

Code Structure Improvements

• Modular Architecture: Split monolithic script into focused classes (TypeDetector, RedshiftManager, CSVAnalyzer, SQLGenerator)
• Configuration Management: Introduced Config and RedshiftConfig dataclasses for clean parameter handling
• Type Safety: Added comprehensive type hints throughout the codebase
• Modern Python: Leveraged dataclasses, enums, pathlib, and context managers

Code Quality Enhancements

• Reduced LOC: Decreased from ~650 to ~450 lines while maintaining all functionality
• Eliminated Globals: Removed global state and variables for better testability
• Error Handling: Improved validation and error messages
• Documentation: Enhanced inline documentation and code organization

Preserved Functionality

• ✅ All command-line arguments and behavior unchanged
• ✅ Complete Redshift/S3 integration maintained
• ✅ Type detection algorithms preserved
• ✅ PostgreSQL and Redshift compatibility intact
• ✅ All CSV parsing capabilities retained

Technical Improvements

• Better Abstractions: Used pathlib.Path for file operations, defaultdict for counters
• Cleaner Logic: Consolidated duplicate patterns, simplified control flow
• Memory Efficiency: Maintained streaming CSV processing
• Separation of Concerns: Clear boundaries between analysis, generation, and AWS operations

Testing

• [x] Verified all examples from README work identically
• [x] Tested PostgreSQL DDL generation
• [x] Tested Redshift COPY commands with S3
• [x] Validated type detection accuracy
• [x] Confirmed backward compatibility

Breaking Changes

None - this is a pure refactor maintaining identical CLI behavior and output.

Benefits

• Maintainability: Easier to understand, modify, and extend
• Testability: Modular design enables better unit testing
• Readability: Clear class responsibilities and modern Python patterns
• Performance: Reduced complexity without sacrificing functionality
• Future-Proof: Better foundation for new features

The refactored code follows expert Python practices while preserving the tool's reliability and feature completeness that users depend on.