This repository contains the complete implementation design for building an enterprise-grade, open-source Software Engineering Intelligence (SEI) platform. The platform provides data-driven insights into engineering operations, team performance, and development lifecycle optimization.

graph TB
    subgraph "Data Sources Layer"
        A1[GitHub/GitLab APIs]
        A2[Jira/Azure DevOps]
        A3[CI/CD Pipelines]
        A4[Security Scanners]
        A5[Custom APIs]
        A6[Slack/Teams]
    end
    
    subgraph "Data Collection & Processing"
        B1[Apache Airflow<br/>Orchestration]
        B2[Custom Connectors<br/>Python/Go]
        B3[Webhook Handlers<br/>FastAPI]
        B4[Message Queue<br/>Apache Kafka]
    end
    
    subgraph "Data Storage"
        C1[TimescaleDB<br/>Time Series]
        C2[PostgreSQL<br/>Metadata]
        C3[Redis<br/>Cache]
        C4[MinIO<br/>Object Storage]
    end
    
    subgraph "Analytics Engine"
        D1[Apache Spark<br/>Big Data Processing]
        D2[Metabase<br/>BI Platform]
        D3[Custom ML Models<br/>Python/scikit-learn]
        D4[DORA Metrics Engine<br/>Go]
    end
    
    subgraph "API Layer"
        E1[GraphQL API<br/>Hasura]
        E2[REST API<br/>FastAPI]
        E3[WebSocket<br/>Real-time Updates]
    end
    
    subgraph "Frontend Applications"
        F1[Executive Dashboard<br/>React]
        F2[Team Analytics<br/>Vue.js]
        F3[Developer Portal<br/>Next.js]
        F4[Mobile App<br/>React Native]
    end
    
    A1 --> B2
    A2 --> B2
    A3 --> B3
    A4 --> B3
    A5 --> B1
    A6 --> B3
    
    B1 --> B4
    B2 --> B4
    B3 --> B4
    
    B4 --> C1
    B4 --> C2
    C1 --> C3
    
    C1 --> D1
    C2 --> D2
    C1 --> D3
    C2 --> D4
    
    D1 --> E1
    D2 --> E2
    D3 --> E1
    D4 --> E2
    
    E1 --> F1
    E2 --> F2
    E1 --> F3
    E2 --> F4

# Clone the repository
git clone https://github.com/rcdelacruz/open-source-sei-platform.git
cd open-source-sei-platform

# Start with Docker Compose (Development)
docker-compose up -d

# Or deploy to Kubernetes (Production)
kubectl apply -f k8s/

├── docs/                   # Documentation
├── src/
│   ├── collectors/         # Data collection services
│   ├── processors/         # Data processing pipelines
│   ├── apis/              # API services
│   ├── frontend/          # Web applications
│   └── ml/                # Machine learning models
├── infrastructure/
│   ├── docker/            # Docker configurations
│   ├── k8s/               # Kubernetes manifests
│   └── terraform/         # Infrastructure as Code
├── tests/                 # Test suites
└── scripts/               # Automation scripts

• DORA Metrics: Deployment frequency, lead time, change failure rate, recovery time
• Team Performance: Velocity tracking, bottleneck identification, collaboration metrics
• Code Quality: Technical debt analysis, security vulnerability tracking
• Predictive Analytics: Risk prediction, capacity planning, timeline forecasting

• Version Control: GitHub, GitLab, Bitbucket, Azure DevOps
• Project Management: Jira, Azure Boards, Linear, Asana
• CI/CD: Jenkins, GitHub Actions, GitLab CI, CircleCI, Tekton
• Communication: Slack, Microsoft Teams, Discord
• Security: Snyk, SonarQube, Veracode, Checkmarx

• Executive View: High-level KPIs, ROI metrics, strategic insights
• Engineering Manager: Team performance, resource allocation, delivery tracking
• Developer: Personal productivity, code quality, review metrics
• Product Manager: Feature delivery, user impact, technical health

• Infrastructure setup (Kubernetes, databases)
• Core data collectors (Git, Jira)
• Basic analytics pipeline
• Simple dashboards

• DORA metrics implementation
• Team performance analytics
• Advanced data connectors
• Real-time processing

• Machine learning models
• Predictive analytics
• Custom metrics framework
• Mobile applications

• Multi-tenant architecture
• Advanced security features
• Performance optimization
• Documentation & training

1. Prerequisites

   • Docker & Docker Compose
   • Kubernetes cluster (optional)
   • PostgreSQL database
   • Redis instance

2. Development Setup

   # Install dependencies
   make install
   
   # Start development environment
   make dev
   
   # Run tests
   make test

3. Production Deployment

   # Deploy to Kubernetes
   make deploy-prod
   
   # Configure data sources
   make configure-sources

We welcome contributions! Please see our Contributing Guide for details.

This project is licensed under the MIT License - see the LICENSE file for details.

• 📚 Documentation
• 🐛 Issues
• 💬 Discussions

See our Project Roadmap for planned features and milestones.

Built with ❤️ by the Open Source Community