Python Automation: CMDB/IPAM Reconciliation

The Problem

Managing IP address allocation across enterprise IT requires keeping two critical systems in sync:

• ServiceNow CMDB: Asset and configuration management
• Infoblox IPAM: IP address management

Manual reconciliation was time-consuming, error-prone, and lacked audit trails.

The Solution

I developed a Python automation tool that:

Features

• One-Click Operation: Single command execution
• Automated Reconciliation: Compares subnets between systems
• Change Tracking: Identifies additions, deletions, and modifications
• Auditable Evidence: Generates detailed reports with timestamps
• API Integration: Leverages both ServiceNow and Infoblox REST APIs

Technical Implementation

# Simplified example structure
import requests
from datetime import datetime

class CMDBIPAMReconciliation:
    def __init__(self, snow_instance, infoblox_url):
        self.snow = ServiceNowAPI(snow_instance)
        self.infoblox = InfobloxAPI(infoblox_url)

    def reconcile_subnets(self):
        # Fetch data from both systems
        snow_subnets = self.snow.get_subnets()
        infoblox_subnets = self.infoblox.get_networks()

        # Compare and identify discrepancies
        discrepancies = self.compare_subnets(
            snow_subnets,
            infoblox_subnets
        )

        # Generate audit report
        self.generate_report(discrepancies)

        return discrepancies

Key Technologies

• Python 3.x: Core application logic
• Requests: HTTP API interactions
• Pandas: Data manipulation and analysis
• ServiceNow Table API: CMDB data access
• Infoblox WAPI: Network object management

Results

Before Automation

• ⏱️ Time: 4-6 hours per reconciliation
• ❌ Errors: Manual data entry mistakes
• 📝 Documentation: Inconsistent
• 🔍 Audit Trail: Limited

After Automation

• ⚡ Time: 5-10 minutes per reconciliation
• ✅ Errors: Eliminated data entry mistakes
• 📊 Documentation: Comprehensive reports
• 🔐 Audit Trail: Complete with timestamps and evidence

Business Impact

• Time Savings: 95% reduction in reconciliation time
• Accuracy: 100% data accuracy vs. ~85% manual accuracy
• Compliance: Full audit trail for regulatory requirements
• Scalability: Can handle enterprise-scale IP ranges

Lessons Learned

1. API Documentation: Thorough understanding of both APIs was crucial
2. Error Handling: Robust exception handling prevents production failures
3. Logging: Detailed logs enable troubleshooting
4. Testing: Extensive testing in dev environment before production

This automation demonstrates the power of Python for IT operations, transforming manual processes into reliable, auditable, automated workflows.