GLPI ↔ ServiceNow Integration Platform

Overview

Developed a complete integration platform for synchronizing tickets between GLPI and ServiceNow. The project demonstrates the evolution of software architecture through three progressive implementations — from a simple proof of concept to a production-ready clean architecture system.

The platform enables automated ticket synchronization, field mapping, value transformations, and enterprise-grade integration workflows between ITSM systems.

Objectives

• Synchronize tickets between GLPI and ServiceNow
• Automate field mapping and value transformations
• Build a scalable and maintainable enterprise integration system
• Demonstrate architectural evolution from monolithic to clean architecture

Tech Stack

• Language: Python
• APIs: GLPI REST API, ServiceNow REST API
• Configuration: YAML
• Data Export: OpenPyXL (Excel)
• CLI Framework: Click
• Testing: Pytest
• Architecture: Clean Architecture + Dependency Injection

Project Structure

The project is divided into three phases:

Phase 1 — Basic Integration (POC)

A monolithic implementation focused on validating API connectivity and basic ticket synchronization.

Phase 2 — Field Mapping & Discovery

Introduced configuration-driven field mapping, automatic field discovery, value transformation, and modular design.

Phase 3 — Clean Architecture (Production-Ready)

Implemented a layered clean architecture with:

• Domain layer
• Application layer
• Infrastructure layer
• Interface layer

This version includes dependency injection, retry logic, connection pooling, session management, CLI support, and production-grade error handling.

Key Features

• Bi-directional ticket synchronization
• Configuration-driven field mapping using YAML
• Automatic field discovery across GLPI and ServiceNow
• Value transformation for statuses, priorities, urgency, and impact
• Retry logic with exponential backoff
• Session management and connection pooling
• CLI-based ticket synchronization workflows
• Extensible clean architecture design
• Comprehensive error handling and validation
• Production-ready scalable implementation

Architecture Evolution

P1 — Monolithic Architecture

Single-script implementation for rapid API integration testing.

P2 — Modular Architecture

Introduced reusable modules and configuration-based workflows.

P3 — Clean Architecture

Adopted enterprise architecture principles:

• Separation of concerns
• Dependency inversion
• Testability
• Scalability
• Maintainability

The final implementation follows a highly modular and extensible design suitable for enterprise deployments.

Implementation

• Built API integration workflows for GLPI and ServiceNow
• Implemented YAML-based dynamic field mapping system
• Developed field discovery tools for identifying available API fields
• Added transformation layers for status and priority normalization
• Designed CLI commands using Click framework
• Implemented retry handling and connection management
• Structured codebase using clean architecture principles

Outcomes

• Successfully synchronized tickets between two enterprise ITSM platforms
• Reduced manual ticket duplication and synchronization overhead
• Improved maintainability through modular and layered architecture
• Demonstrated scalable integration design patterns for enterprise systems

Impact

This project highlights enterprise integration engineering, API orchestration, and software architecture evolution. It demonstrates how systems can evolve from rapid prototypes into scalable, production-ready platforms using clean architecture principles.

The project showcases:

• Enterprise Integration
• API Engineering
• Clean Architecture
• Scalable Python Development
• ITSM Automation

Scalability & Reliability

• Retry logic with exponential backoff
• Connection pooling and session reuse
• Dependency injection for extensibility
• Layered architecture for long-term maintainability
• Designed for enterprise-scale deployments

Key Learnings

• Enterprise API integration patterns
• Designing maintainable clean architectures
• Configuration-driven software design
• Building scalable and testable Python applications
• ServiceNow and GLPI workflow automation

Role

• Backend / Integration Engineer