Product Requirements Document (PRD)

# Product Requirements Document (PRD)
## Location Converter: Excel to CSV to PostgreSQL Pipeline

### Project Overview
**Project Name:** location-converter-xlsx-csv-pgtable  
**Version:** 1.0  
**Date:** December 2024  
**Status:** Development Phase

### 1. Executive Summary

This project aims to create a robust data pipeline that processes farmer location data from Excel files, performs data cleaning and analysis, converts the data to CSV format, and finally loads it into a PostgreSQL database table called `bellary_farmers`.

### 2. Project Objectives

#### Primary Goals
- Extract and analyze data from multi-sheet Excel files (Bellary.xlsx)
- Clean and standardize farmer location data
- Convert processed data to CSV format
- Load data into PostgreSQL database for further analysis
- Provide data insights through Jupyter notebook analysis

#### Success Metrics
- Successfully process all 5 sheets from the Excel file
- Achieve 100% data integrity during conversion
- Complete data loading into PostgreSQL without errors
- Generate comprehensive data analysis report

### 3. Data Schema Definition

Based on the provided parameters, the following data structure will be implemented:

#### Source Data Fields
| Field Name | Data Type | Description | Constraints |
|------------|-----------|-------------|-------------|
| S.no | Integer | Serial number/Record ID | Primary identifier |
| Status | String | Current status of farmer record | Enum values |
| Farmer Code | String | Unique farmer identifier | Unique, Not Null |
| Farmer Name | String | Full name of the farmer | Not Null |
| Govt id num | String | Government issued ID number | Unique |
| Primary Contact | String | Phone/contact information | Format validation |
| Farmer Created Date | DateTime | Date when farmer record was created | ISO format |
| Plot Created Date | DateTime | Date when plot was registered | ISO format |
| Sync Date | DateTime | Last synchronization date | ISO format |
| Updation Date | DateTime | Last update timestamp | ISO format |
| Area | Float | Plot area measurement | Positive value |
| GPS Area | Float | GPS-calculated area | Positive value |
| Village | String | Village name | Not Null |

#### Target PostgreSQL Schema
```sql
CREATE TABLE bellary_farmers (
    id SERIAL PRIMARY KEY,
    serial_number INTEGER,
    status VARCHAR(50),
    farmer_code VARCHAR(100) UNIQUE NOT NULL,
    farmer_name VARCHAR(255) NOT NULL,
    govt_id_number VARCHAR(50) UNIQUE,
    primary_contact VARCHAR(20),
    farmer_created_date TIMESTAMP,
    plot_created_date TIMESTAMP,
    sync_date TIMESTAMP,
    updation_date TIMESTAMP,
    area DECIMAL(10,4),
    gps_area DECIMAL(10,4),
    village VARCHAR(100) NOT NULL,
    source_sheet VARCHAR(50),
    imported_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
```

### 4. Technical Requirements

#### 4.1 Environment Setup
- **Python Version:** 3.11+
- **Package Manager:** uv
- **Virtual Environment:** .venv

#### 4.2 Core Dependencies
```
jupyterlab>=4.0.0
pandas>=2.0.0
numpy>=1.24.0
openpyxl>=3.1.0
psycopg2-binary>=2.9.0
sqlalchemy>=2.0.0
python-dotenv>=1.0.0
```

#### 4.3 Development Dependencies
```
pytest>=7.0.0
black>=23.0.0
flake8>=6.0.0
pre-commit>=3.0.0
```

### 5. Functional Requirements

#### 5.1 Data Extraction
- **FR-001:** System shall read Excel files with multiple sheets
- **FR-002:** System shall identify and process exactly 5 sheets from Bellary.xlsx
- **FR-003:** System shall handle different column naming conventions across sheets
- **FR-004:** System shall preserve data integrity during extraction

#### 5.2 Data Analysis & Cleaning
- **FR-005:** System shall provide comprehensive data profiling in Jupyter notebook
- **FR-006:** System shall identify and report data quality issues
- **FR-007:** System shall standardize column names across all sheets
- **FR-008:** System shall validate data types and formats
- **FR-009:** System shall handle missing values appropriately
- **FR-010:** System shall detect and report duplicate records

#### 5.3 Data Transformation
- **FR-011:** System shall merge data from all 5 sheets into a single dataset
- **FR-012:** System shall apply consistent data formatting rules
- **FR-013:** System shall add metadata columns (source_sheet, imported_at)
- **FR-014:** System shall export cleaned data to CSV format

#### 5.4 Database Integration
- **FR-015:** System shall connect to PostgreSQL database
- **FR-016:** System shall create bellary_farmers table if not exists
- **FR-017:** System shall load CSV data into PostgreSQL table
- **FR-018:** System shall handle database connection errors gracefully
- **FR-019:** System shall provide data loading progress feedback

### 6. Non-Functional Requirements

#### 6.1 Performance
- **NFR-001:** Data processing shall complete within 5 minutes for files up to 10MB
- **NFR-002:** Database loading shall handle up to 100,000 records efficiently
- **NFR-003:** Memory usage shall not exceed 2GB during processing

#### 6.2 Reliability
- **NFR-004:** System shall have 99% success rate for data conversion
- **NFR-005:** System shall provide detailed error logging
- **NFR-006:** System shall support transaction rollback on failures

#### 6.3 Usability
- **NFR-007:** System shall provide clear progress indicators
- **NFR-008:** System shall generate comprehensive data quality reports
- **NFR-009:** System shall include detailed documentation and examples

### 7. Project Structure

```
location-converter-xlsx-csv-pgtable/
├── .venv/                          # Virtual environment
├── .cursor/                        # Cursor IDE configuration
├── data/
│   ├── raw/                        # Original Excel files
│   ├── processed/                  # Cleaned CSV files
│   └── reports/                    # Data quality reports
├── notebooks/
│   ├── 01_data_exploration.ipynb   # Initial data analysis
│   ├── 02_data_cleaning.ipynb      # Data cleaning process
│   └── 03_data_validation.ipynb    # Final validation
├── src/
│   ├── __init__.py
│   ├── data_extractor.py           # Excel reading functionality
│   ├── data_cleaner.py             # Data cleaning utilities
│   ├── data_validator.py           # Data validation rules
│   └── db_loader.py                # Database operations
├── scripts/
│   ├── setup_environment.py        # Environment setup
│   ├── run_pipeline.py             # Main pipeline execution
│   └── create_database.py          # Database schema creation
├── tests/
│   ├── test_data_extractor.py
│   ├── test_data_cleaner.py
│   └── test_db_loader.py
├── config/
│   ├── database.yaml               # Database configuration
│   └── data_mapping.yaml           # Column mapping rules
├── .env.example                    # Environment variables template
├── .gitignore
├── requirements.txt
├── README.md
├── PRD.md                          # This document
└── Makefile                        # Build automation
```

### 8. Implementation Phases

#### Phase 1: Project Setup (Week 1)
- Initialize uv environment
- Install required dependencies
- Set up project structure
- Create basic documentation

#### Phase 2: Data Exploration (Week 1-2)
- Analyze Excel file structure
- Identify data quality issues
- Create data profiling notebook
- Define cleaning strategies

#### Phase 3: Data Processing Pipeline (Week 2-3)
- Implement data extraction module
- Develop data cleaning utilities
- Create validation framework
- Build CSV export functionality

#### Phase 4: Database Integration (Week 3-4)
- Set up PostgreSQL connection
- Create database schema
- Implement data loading pipeline
- Add error handling and logging

#### Phase 5: Testing & Documentation (Week 4)
- Write comprehensive tests
- Create user documentation
- Perform end-to-end testing
- Optimize performance

### 9. Risk Assessment

#### High Risk
- **Data Quality Issues:** Inconsistent data formats across sheets
- **Database Connectivity:** Network or authentication failures
- **Memory Constraints:** Large file processing limitations

#### Medium Risk
- **Column Mapping:** Variations in column names across sheets
- **Data Type Conversion:** Invalid data format handling
- **Performance:** Slow processing for large datasets

#### Low Risk
- **Environment Setup:** Dependency installation issues
- **File Access:** Permission or path-related problems

### 10. Success Criteria

#### Technical Success
- [ ] All 5 Excel sheets successfully processed
- [ ] Data quality report generated with <5% error rate
- [ ] CSV file created with all required columns
- [ ] PostgreSQL table populated with complete data
- [ ] Pipeline execution time under 5 minutes

#### Business Success
- [ ] Farmer data accessible for analysis
- [ ] Data integrity maintained throughout pipeline
- [ ] Reproducible process for future data updates
- [ ] Comprehensive documentation for maintenance

### 11. Deliverables

1. **Working Data Pipeline**
   - Complete Python application
   - Jupyter notebooks for analysis
   - Database schema and loading scripts

2. **Documentation**
   - Technical documentation
   - User guide
   - API documentation
   - Data dictionary

3. **Testing Suite**
   - Unit tests
   - Integration tests
   - Data validation tests

4. **Configuration Files**
   - Environment setup
   - Database configuration
   - Data mapping rules

### 12. Maintenance & Support

#### Ongoing Requirements
- Regular data quality monitoring
- Database performance optimization
- Documentation updates
- Bug fixes and enhancements

#### Support Model
- Self-service documentation
- Issue tracking system
- Regular maintenance schedule

---

**Document Version:** 1.0  
**Last Updated:** December 2024  
**Next Review:** January 2025