Sanitized reference architecture for a cloud-native Flight Operations Platform built on Azure with microservices, real-time event streaming, and ML-enabled decision intelligence.
Open
flight-ops-architecture.htmlin a browser for the full interactive diagram.
The platform is organized into 7 horizontal layers, each independently deployable and domain-isolated:
| Layer | Purpose | Key Technologies |
|---|---|---|
| Ingestion | External data feeds & third-party systems | ARINC, ACARS, ADS-B, FAA APIs, REST/SOAP |
| Streaming | Event backbone & API integration | Apache Kafka, Azure Event Hubs, Azure API Management |
| Core Services | Domain microservices | Python/FastAPI, Java/Spring Boot, .NET Core on AKS |
| ML / AI | Predictive models & compliance automation | Azure ML, MLflow, XGBoost, LSTM, Azure OpenAI |
| Data Platform | Lake, warehouse & BI | Azure Data Lake Gen2, Snowflake, dbt, Tableau |
| Observability | Metrics, logging & tracing | Prometheus, Grafana, Splunk, OpenTelemetry |
| Security & DevOps | Zero-trust, CI/CD, governance | Azure AD, GitHub Actions, ArgoCD, Azure Policy |
- Route optimization and alternates computation
- Integrates with ML models for fuel-efficient routing
- Exposes both gRPC (internal) and REST (external) interfaces
- Predictive fuel uplift recommendations per flight
- Factors: weather, payload, historical burn rates, route type
- Trained ML model (Azure ML Online Endpoint) called at plan generation time
- Duty time tracking and FAR 117 compliance enforcement
- Pairing optimization and disruption re-crewing workflows
- Integrates with HR/scheduling systems via REST APIs
- Real-time IROPS detection from event streams
- Automated recovery scenario generation and ranking
- Stakeholder alert orchestration (ops center, crew, passengers)
- Flight status push notifications at scale (450M+ annually)
- Delay/cancellation messaging via mobile, email, SMS
- Integrates with loyalty and baggage APIs
Data Sources → Feature Store → Model Training (Azure ML Pipelines)
↓
Model Registry (MLflow)
↓
Online Endpoints → Microservices (real-time inference)
↓
Monitoring (drift detection, retraining triggers)
Models in scope:
- Route & Fuel Optimizer — XGBoost/LSTM trained on historical flights
- Delay Predictor — Classification model (4h pre-departure probability)
- Compliance Checker — Rules engine + NLP for FAR 117 validation
Bronze (Raw) → Silver (Cleansed) → Gold (Business-Ready)
↓ ↓ ↓
All events Validated, typed Dimensional model
as landed deduplicated (Snowflake / Synapse)
↓
Tableau / Power BI
Executive Dashboards
- Zero-trust network model — no implicit internal trust
- RBAC + just-in-time access via Azure AD and PIM
- mTLS between all microservices (cert-manager + Istio sidecar)
- Private endpoints for all data services — no public internet exposure
- GitOps-driven deployments — no manual kubectl, all changes via PR
- Automated compliance gates in CI/CD pipeline (SAST, container scanning, policy checks)
Developer PR
↓
GitHub Actions (lint → unit tests → integration tests → container build)
↓
Security Gate (Trivy scan, SAST, policy check)
↓
ArgoCD sync → AKS Staging (canary deployment)
↓
Synthetic monitoring validation (5 min)
↓
ArgoCD promote → AKS Production (blue-green cutover)
These figures reflect the type of outcomes this architecture pattern is designed to enable:
| Metric | Benchmark |
|---|---|
| Flight planning time reduction | ~14% via ML routing |
| Fuel consumption improvement | 20–30% through predictive uplift |
| Disruption response time | 40–50% faster with automated IROPS |
| Deployment velocity | 60%+ improvement with CI/CD automation |
| SLA breach reduction | 25–30% with proactive alerting |
| On-time delivery | 95%+ with Agile/SAFe governance |
Cloud: Azure (AKS, Event Hubs, Data Factory, API Management, ML, Key Vault, Monitor)
Streaming: Apache Kafka Azure Event Hubs Schema Registry (Avro)
Microservices: Python/FastAPI Java/Spring Boot .NET Core Node.js Docker Kubernetes
ML/AI: Azure ML MLflow XGBoost LSTM scikit-learn Azure OpenAI
Data: Azure Data Lake Gen2 Delta Lake Snowflake dbt Azure Synapse
BI: Tableau Power BI Azure Analysis Services
Observability: Prometheus Grafana Splunk OpenTelemetry Azure App Insights
Security: Azure AD OAuth 2.0 mTLS Azure Key Vault Azure Policy Defender
CI/CD: GitHub Actions ArgoCD Helm Trivy SonarQube
Governance: Collibra Azure Purview Data Catalog
flight-ops-platform-architecture/
├── README.md ← You are here
├── flight-ops-architecture.html ← Interactive architecture diagram
├── diagrams/
│ ├── system-context.md ← C4 Level 1: System Context
│ ├── container-diagram.md ← C4 Level 2: Containers
│ └── data-flow.md ← Data flow narrative
├── adr/
│ ├── 001-event-streaming.md ← ADR: Why Kafka over SQS
│ ├── 002-medallion-data.md ← ADR: Medallion vs. ODS approach
│ └── 003-mlops-platform.md ← ADR: Azure ML vs. custom MLflow
└── runbooks/
├── irops-playbook.md ← IROPS response runbook
└── deployment-guide.md ← Deployment & rollback procedures
Balaji Sivarajan — Senior Technical Program Manager
10+ years delivering cloud, AI/ML, and digital transformation programs across aviation, fintech, and eCommerce.
This architecture is intended for portfolio demonstration and technical discussion. Feedback and questions welcome via LinkedIn.