Spreadsheet-as-Database Anti-pattern

Immediate Spreadsheet Detection:

# Python script to detect spreadsheet usage
import os
import pandas as pd

def audit_spreadsheet_usage(directory):
    spreadsheet_files = []
    size_risks = []
    
    for root, dirs, files in os.walk(directory):
        for file in files:
            if file.endswith(('.xlsx', '.xls', '.csv')):
                filepath = os.path.join(root, file)
                spreadsheet_files.append(filepath)
                
                # Check size risks
                try:
                    if file.endswith('.csv'):
                        row_count = sum(1 for line in open(filepath))
                    else:
                        df = pd.read_excel(filepath, nrows=0)
                        row_count = len(pd.read_excel(filepath))
                    
                    if row_count > 50000:  # Near Excel limits
                        size_risks.append({
                            'file': filepath,
                            'rows': row_count,
                            'risk': 'Approaching Excel limits'
                        })
                except:
                    pass
    
    return spreadsheet_files, size_risks

Database Migration Path:

-- Create proper database schema for spreadsheet data
CREATE TABLE IF NOT EXISTS migrated_spreadsheet_data (
    id SERIAL PRIMARY KEY,
    import_timestamp TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    source_file VARCHAR(255),
    source_row_number INTEGER,
    data_payload JSONB,
    validation_status VARCHAR(50),
    validation_errors TEXT[]
);

-- Add validation rules
CREATE OR REPLACE FUNCTION validate_imported_data()
RETURNS TRIGGER AS $
BEGIN
    -- Check for required fields
    IF NEW.data_payload->>'critical_field' IS NULL THEN
        NEW.validation_status = 'FAILED';
        NEW.validation_errors = array_append(
            NEW.validation_errors, 
            'Missing critical field'
        );
    END IF;
    
    -- Check data types
    IF NOT (NEW.data_payload->>'numeric_field' ~ '^\d+{{{remediation}}}#39;) THEN
        NEW.validation_status = 'FAILED';
        NEW.validation_errors = array_append(
            NEW.validation_errors,
            'Invalid numeric format'
        );
    END IF;
    
    RETURN NEW;
END;
$ LANGUAGE plpgsql;

Spreadsheet Replacement Strategy:

CREATE TABLE spreadsheet_inventory (
    id SERIAL PRIMARY KEY,
    filename VARCHAR(255),
    file_path TEXT,
    purpose TEXT,
    owner VARCHAR(255),
    row_count INTEGER,
    column_count INTEGER,
    critical_data BOOLEAN,
    migration_priority INTEGER,
    target_database VARCHAR(255),
    migration_status VARCHAR(50)
);

-- Prioritization query
SELECT 
    filename,
    row_count,
    CASE 
        WHEN row_count > 60000 THEN 'IMMEDIATE - Near Excel limit'
        WHEN critical_data AND row_count > 30000 THEN 'HIGH - Critical data at risk'
        WHEN critical_data THEN 'MEDIUM - Critical but manageable'
        ELSE 'LOW - Non-critical'
    END as migration_urgency
FROM spreadsheet_inventory
WHERE migration_status != 'COMPLETED'
ORDER BY 
    CASE 
        WHEN row_count > 60000 THEN 1
        WHEN critical_data AND row_count > 30000 THEN 2
        WHEN critical_data THEN 3
        ELSE 4
    END;

Monitoring for Spreadsheet Creep:

-- Alert on new spreadsheet dependencies
CREATE TABLE spreadsheet_usage_alerts (
    id SERIAL PRIMARY KEY,
    detected_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    detection_type VARCHAR(50),
    file_path TEXT,
    accessed_by VARCHAR(255),
    risk_level VARCHAR(20)
);

CREATE OR REPLACE FUNCTION detect_spreadsheet_access()
RETURNS void AS $
DECLARE
    v_alert RECORD;
BEGIN
    -- Check system logs for spreadsheet access
    FOR v_alert IN 
        SELECT DISTINCT 
            username,
            object_name as file_path
        FROM system_access_log
        WHERE log_time > NOW() - INTERVAL '1 hour'
        AND object_name ~ '\.(xlsx?|csv){{{remediation}}}#39;
        AND action = 'data_import'
    LOOP
        INSERT INTO spreadsheet_usage_alerts 
            (detection_type, file_path, accessed_by, risk_level)
        VALUES 
            ('DATA_IMPORT', v_alert.file_path, v_alert.username, 'HIGH');
    END LOOP;
END;
$ LANGUAGE plpgsql;

Additional Best Practices:

• Implement hard blocks on production spreadsheet imports
• Create web-based data entry forms instead of spreadsheet collection
• Use proper ETL tools for data transformation
• Regular training on database tools for non-technical staff
• Automated alerts when spreadsheets approach size limits