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Production Cloud Platform (platform engineering lifecycle)

Production-style infrastructure monitoring and telemetry processing platform demonstrating Kubernetes orchestration, CI/CD automation, observability and operational reliability practices.


Current Development Status

This repository is actively maintained and continuously expanded as part of a production-style engineering portfolio.


Current implementation:

  • Sensor Simulator
  • FastAPI Ingestion API
  • PostgreSQL Persistence
  • Background Processing Service
  • Alert Generation Workflow

Recent focus areas include:

  • cloud-native infrastructure and platform engineering;
  • Kubernetes and container orchestration;
  • CI/CD automation and GitOps workflows;
  • observability, monitoring and reliability engineering;
  • secure networking and infrastructure security.

Some components and documentation are currently being updated and refactored to reflect ongoing improvements and new functionality.

Planned enhancements include:

  • end-to-end CI/CD pipelines;
  • Helm-based deployments;
  • GitOps workflows with ArgoCD;
  • infrastructure observability and alerting;
  • production-style service deployment lifecycle.

The repository remains functional and serves as a continuously evolving engineering project.


Overview

Production-style cloud platform demonstrating the complete service delivery lifecycle, including infrastructure provisioning, containerization, Kubernetes orchestration, CI/CD automation, GitOps deployment workflows, observability and operational reliability practices.

The project simulates telemetry ingestion and processing for infrastructure monitoring systems and is being developed incrementally using production engineering approaches.

The system models a simplified monitoring workflow for industrial and technical infrastructure environments.


Security Notice

This repository uses demonstration credentials. Kubernetes Secrets included in the repository are intended only for testing environments and must be replaced with properly managed secrets in production deployments.

Exanmples include:

  • K8s Secrets
  • GitHub Secrets
  • External Secret Managers
  • Vault-based secret storage

Target Production Architecture

The following diagram represents the target production architecture that is being implemented incrementally. Current development focuses on telemetry ingestion, data storage and service deployment lifecycle components. Additional platform capabilities such as GitOps workflows, observability, service orchestration and infrastructure automation are introduced in subsequent development stages.

                    ┌─────────────────────┐
                    │ Sensor Simulators   │
                    │ Python generators   │
                    └─────────┬───────────┘
                              │ HTTP JSON
                              ▼
                    ┌─────────────────────┐
                    │ NGINX Ingress       │
                    └─────────┬───────────┘
                              ▼
                    ┌─────────────────────┐
                    │ FastAPI API         │
                    │ validation/auth     │
                    └─────────┬───────────┘
                              ▼
                    ┌─────────────────────┐
                    │ Redis Queue         │
                    │ async buffering     │
                    └─────────┬───────────┘
                              ▼
                    ┌─────────────────────┐
                    │ Telemetry Worker    │
                    │ processing          │
                    │ anomaly detection   │
                    └─────────┬───────────┘
                              ▼
                    ┌─────────────────────┐
                    │ PostgreSQL          │
                    │ telemetry/events    │
                    └─────────┬───────────┘
                              │
          ┌───────────────────┼───────────────────┐
          ▼                   ▼                   ▼
 ┌────────────────┐ ┌────────────────┐ ┌────────────────┐
 │ Prometheus     │ │ Loki           │ │ OpenTelemetry  │
 │ metrics        │ │ logs           │ │ traces         │
 └────────┬───────┘ └────────┬───────┘ └────────┬───────┘
          └──────────────────┼──────────────────┘
                             ▼
                    ┌─────────────────────┐
                    │ Grafana             │
                    │ dashboards/alerts   │
                    └─────────────────────┘


        ┌──────────────────────────────────────┐
        │ Kubernetes (k3s/kind/EKS/GKE)        │
        │ deployments/services/HPA/secrets     │
        └──────────────────────────────────────┘


        ┌──────────────────────────────────────┐
        │ GitHub Actions CI/CD                 │
        │ build/test/deploy                    │
        └──────────────────────────────────────┘


        ┌──────────────────────────────────────┐
        │ Terraform IaC                        │
        │ cluster/network/storage              │
        └──────────────────────────────────────┘

Prometheus, Grafana and Loki provide centralized monitoring, metrics collection and log aggregation.

Current Implementation Status

Implemented:

  • Sensor Simulator service
  • FastAPI Ingestion API
  • PostgreSQL Persistence
  • Telemetry Processing Worker
  • Docker Containerization
  • Docker Conpose Deployment
  • Kubernetes Deployment (Kind)
  • ConfigMap and Secrets
  • Stateful PostgreSQL Storage

Planned:

  • GitHub Container Registry
  • Helm packaging
  • GitHub Actions CI
  • ArgoCD GitOps deployment
  • Prometheus and Grafana observability stack
  • Redis

Platform workflow

  1. Monitoring devices or external systems submit telemetry events through the ingestion API
  2. The API validates and queues telemetry data in Redis
  3. Worker services asynchronously process telemetry events
  4. Processing results and alert metadata are stored in PostgreSQL
  5. Prometheus and Loki collect operational metrics and logs
  6. Grafana dashboards provide observability into platform health and workload processing

Operational Goals

  • rolling deployments
  • self-healing workloads
  • horizontal scaling
  • centralized logging
  • infrastructure reproducibility
  • deployment automation

Core Components

Infrastructure

Terraform VPC networking IAM configuration Security groups Environment isolation

Kubernetes Platform

Deployments Services Ingress Autoscaling Rolling updates Health checks

CI/CD

GitHub Actions Automated build pipelines Container image deployment Rollback workflows

Observability

Prometheus Grafana Loki Centralized logging Alerting

Security

TLS Kubernetes secrets Network policies Secure service communication

Repository Structure

production-cloud-platform/
│
├── infrastructure/
│   ├── terraform/
│   ├── helm/
│   └── scripts/
│
├── kubernetes/
│   ├── base/
│   ├── monitoring/
│   ├── ingress/
│   └── applications/
│
├── services/
│   ├── api/
│   ├── worker/
│   ├── simulator/
│   └── shared/
│
├── observability/
│   ├── grafana/
│   ├── prometheus/
│   ├── loki/
│   └── otel/
│
├── docs/
│   ├── architecture/
│   ├── operations/
│   ├── runbooks/
│   └── threat-model/
│
├── .github/workflows/
│
├── screenshots/
│
├── Makefile
├── docker-compose.yaml
└── README.md

Failure Recovery Scenarios

The platform includes operational scenarios for: Pod failure recovery Rolling deployments Failed deployment rollback Node failure handling Autoscaling under load

Screenshots

Architecture diagrams, dashboards and deployment screenshots are located in:

screenshots/

Documentation

Detailed documentation is available in:

docs/

Including:

architecture deployment workflow operations troubleshooting threat model

Technology Stack

Kubernetes Terraform Docker GitHub Actions Prometheus Grafana PostgreSQL Redis

License

MIT License

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Production-style cloud-native platform with Kubernetes, Terraform, CI/CD, observability and secure infrastructure automation.

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