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Kittipong Sorasuchart ✧'s avatar
Kittipong Sorasuchart ✧
Bangkok, Thailand
Senior Full Stack Developer. Founder/CEO of Mirimera.
$1k+
Earned
2x
Hired
5.0
Rating
35
Followers
Senior Full Stack Developer. Founder/CEO of Mirimera.
Cover image for Centralized Server Monitoring System Implementation
Centralized Server Monitoring System Implementation
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Cover image for Professional portfolio website
Professional portfolio website
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10
Cover image for Website Performance and Visibility Auditing Platform
Website Performance and Visibility Auditing Platform
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12
Cover image for Options Trading Platform
Options Trading Platform
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6
386
Grafana(1)
Aduraleke faith Akintade's avatar
Aduraleke faith Akintade
Kigali, Rwanda
Devops Engineer delivering efficient software application.
$1k+
Earned
1x
Hired
5.0
Rating
10
Followers
Devops Engineer delivering efficient software application.
Cover image for Multi-Tenant SaaS Platform on AWS EKS
Multi-Tenant SaaS Platform on AWS EKS
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Cover image for HIPAA-Ready Health Data Platform on AWS
HIPAA-Ready Health Data Platform on AWS
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3
Cover image for which of these A or
which of these A or B would you pick.
2
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131
Cover image for sip, savor and enjoy your
sip, savor and enjoy your coffee.
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110
Grafana(2)
abdullah masood's avatar
abdullah masood
Lahore, Pakistan
AI & Full-Stack Engineer | Building Production AI SaaS, APIs
New to Contra
AI & Full-Stack Engineer | Building Production AI SaaS, APIs
Cover image for 🚀 Introducing GraphBridge – Turn
🚀 Introducing GraphBridge – Turn JSON into Grafana Dashboards One thing I've noticed while working on different projects is that applications already have valuable metrics, but getting those metrics into Grafana often requires exporters, custom integrations, or additional setup. I wanted something much simpler. So I built GraphBridge. The idea is straightforward: Your Application ↓ Send JSON ↓ GraphBridge ↓ Prometheus ↓ Grafana Instead of writing custom exporters, your application simply sends JSON metrics to GraphBridge. For example: { "active_users": 85, "orders_today": 42, "revenue_today": 9840, "failed_jobs": 3, "latency_ms": 210 } GraphBridge automatically converts the data into Prometheus metrics, making it immediately available for Grafana dashboards. Current features ✅ JSON Webhook API ✅ REST API polling ✅ JSON & CSV data sources ✅ Prometheus /metrics endpoint ✅ Docker Compose deployment ✅ Auto-configured Grafana dashboard ✅ Example integrations for Laravel, FastAPI, Node.js, and custom applications Possible use cases • Application monitoring • Queue & background job metrics • Business dashboards • API performance monitoring • FFmpeg streaming statistics • IoT sensor metrics • Internal analytics • Custom operational dashboards The goal is simple: Send JSON. Visualize everything. This is the first public MVP, and I'm planning to continue improving it with additional integrations and monitoring templates. I'd love your feedback and suggestions! ⭐ GitHub: https://lnkd.in/dAYdNJxg
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Cover image for 🚀 Building a Graph-Based Fraud
🚀 Building a Graph-Based Fraud Detection System with Neo4j A single transaction may look normal. Its connections can tell a different story. I’m building a learning project using Neo4j and Cypher to explore relationships between customers, accounts, transactions, and devices—and flag patterns worth investigating. 🔍 Patterns I’m exploring: • Multiple customers sharing the same device • Unusually large transactions • Rapid transactions within a short period • Multiple accounts sending money to one account • Circular transfers between connected accounts The focus is explainability: showing the relationships behind each flag so the suspicious activity is easier to understand. Through this project, I’m strengthening my graph data modelling, Cypher queries, and rule-based detection skills. Next: FastAPI integration, transaction risk scores, fraud alerts, and an interactive monitoring dashboard. 🛠️ Currently a learning prototype, with more updates coming as I build. What would you investigate first: shared devices or circular transfers? #Neo4j #GraphDatabase #FraudDetection #Cypher #BackendDevelopment #LearningInPublic
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Cover image for A Kubernetes deployment should be
A Kubernetes deployment should be easy to trace: what changed, who changed it, and why. That’s what interests me about GitOps. With GitOps, the desired state of your infrastructure and applications lives in Git. Changes go through commits and pull requests, while a controller such as Argo CD or Flux continuously reconciles the running environment with that desired state. A typical workflow looks like this: • Build and test the application through CI. • Push the Docker image to a registry. • Update the image version in the Git configuration. • Review and merge the change. • Let the GitOps controller reconcile the cluster. The benefits go beyond automated deployments: ✅ A versioned history of configuration changes ✅ Reviewable changes before deployment ✅ Visibility into configuration drift ✅ A repeatable way to manage environments One important detail: reverting a Git commit can restore an earlier configuration, but database migrations and changes to application data still need their own recovery plan. For me, GitOps connects development and operations through a workflow both teams already understand: Git. Are you using Argo CD, Flux, or CI-driven deployments for your Kubernetes workloads?
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Cover image for Production Kubernetes & Cloud Deployment
Production Kubernetes & Cloud Deployment
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152
Grafana(1)
Shereen Hasnain's avatar
Shereen Hasnain
Lahore, Pakistan
DevOps Engineer | Docker, Kubernetes & CI/CD Specialist.
New to Contra
DevOps Engineer | Docker, Kubernetes & CI/CD Specialist.
Cover image for 🚀 Completed a Kubernetes Monitoring
🚀 Completed a Kubernetes Monitoring Project with Prometheus & Grafana As part of my DevOps learning journey, I completed a hands-on Kubernetes monitoring project where I deployed and monitored a Kubernetes environment using Helm, Prometheus, and Grafana. 🔧 Technologies Used: • Kubernetes & Minikube • Helm • Prometheus • Grafana • Node Exporter • Kube State Metrics 📌 What I implemented: • Deployed Prometheus and Grafana using Helm • Monitored Kubernetes pods and services • Collected node and cluster metrics • Created a Grafana monitoring dashboard • Monitored CPU, memory, and pod status • Created PromQL queries for monitoring • Configured a High CPU Usage alert (CPU > 80%) • Verified deployments using kubectl get pods, kubectl get svc, and helm list 📸 Screenshots below show the actual implementation, including Kubernetes resources, Helm releases, Grafana dashboard, and alert configuration. This project helped me strengthen my understanding of Kubernetes monitoring, observability, Prometheus, Grafana, Helm, and alerting Currently continuing my journey toward becoming a Junior DevOps Engineer, focusing on Kubernetes, CI/CD, cloud technologies, monitoring, and automation. hashtag#DevOps (https://www.linkedin.com/search/results/all/?keywords=%23devops&origin=HASH_TAG_FROM_FEED) hashtag#Kubernetes (https://www.linkedin.com/search/results/all/?keywords=%23kubernetes&origin=HASH_TAG_FROM_FEED) hashtag#Prometheus (https://www.linkedin.com/search/results/all/?keywords=%23prometheus&origin=HASH_TAG_FROM_FEED) hashtag#Grafana (https://www.linkedin.com/search/results/all/?keywords=%23grafana&origin=HASH_TAG_FROM_FEED) hashtag#Helm (https://www.linkedin.com/search/results/all/?keywords=%23helm&origin=HASH_TAG_FROM_FEED) hashtag#Monitoring (https://www.linkedin.com/search/results/all/?keywords=%23monitoring&origin=HASH_TAG_FROM_FEED) hashtag#Minikube (https://www.linkedin.com/search/results/all/?keywords=%23minikube&origin=HASH_TAG_FROM_FEED) hashtag#CloudComputing (https://www.linkedin.com/search/results/all/?keywords=%23cloudcomputing&origin=HASH_TAG_FROM_FEED) hashtag#DevOpsJourney (https://www.linkedin.com/search/results/all/?keywords=%23devopsjourney&origin=HASH_TAG_FROM_FEED)
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21
Cover image for 🚀 Ansible Nginx Automation Project Built
🚀 Ansible Nginx Automation Project Built a hands-on DevOps project to automate Nginx deployment and configuration using Ansible. 🔧 Key concepts: • Ansible Roles & Playbooks • Jinja2 Templates • Handlers & Tasks • Nginx Configuration • Linux Server Automation • Infrastructure as Code (IaC) This project strengthened my practical understanding of server automation and configuration management.
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Cover image for 🚀 Just completed a Kubernetes
🚀 Just completed a Kubernetes Logging project using the ELK Stack! Built and verified a centralized logging pipeline using: 🔹 Kubernetes & Minikube 🔹 Helm 🔹 Filebeat 🔹 Elasticsearch 🔹 Kibana Kubernetes logs are now collected by Filebeat, stored in Elasticsearch, and visualized through Kibana Discover. This project strengthened my hands-on skills in Kubernetes logging, Helm deployments, troubleshooting, and observability.
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Cover image for CVision AI | AI-Powered Resume
CVision AI | AI-Powered Resume Analyzer Built, containerized, and deployed an end-to-end AI Resume Analyzer. Analyzes resumes for ATS scoring, job matching, and skill gap suggestions. Tech Stack: Docker, Git/CI/CD, Render Cloud, Python/Flask, OpenRouter AI
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Grafana(1)
Mohammed Abul Azad Faisal's avatar
Mohammed Abul Azad Faisal
Dhaka, Bangladesh
DevOps & Cloud Infrastructure Engineer | Proxmox Expert
DevOps & Cloud Infrastructure Engineer | Proxmox Expert
Cover image for Hybrid On-Premises and Cloud Kubernetes Platform
Hybrid On-Premises and Cloud Kubernetes Platform
0
5
Cover image for Automated OpenNebula Monitoring and Alerting
Automated OpenNebula Monitoring and Alerting Architecture Designed an automated monitoring and observability architecture for a multi-region OpenNebula private cloud environment. The solution automatically discovers newly created virtual machines through the OpenNebula API and registers them in Zabbix without requiring manual monitoring configuration. Once a VM is detected, it is assigned to the appropriate host group based on its region, environment, operating system, or customer. Monitoring templates are then applied automatically for Linux, Windows, hypervisor, network, and storage systems. This ensures that every newly provisioned VM receives consistent monitoring, trigger thresholds, availability checks, and alerting policies from the moment it becomes active. The architecture includes regional Zabbix proxies that collect monitoring data from each OpenNebula region and securely forward it to a centralized Zabbix Server. This reduces cross-region monitoring traffic and allows monitoring to continue locally during temporary network interruptions. The platform monitors: CPU and memory utilization Disk capacity and performance Network traffic and packet loss Operating system availability Services and running processes VM uptime and availability Hypervisor health Storage availability and performance Network devices using SNMP Web services and APIs using HTTP checks When a problem is detected, Zabbix evaluates the configured trigger and escalation policy. Notifications are then automatically routed to Telegram, Microsoft Teams, and email based on severity, environment, and operational responsibility. Grafana is integrated with Zabbix as a centralized visualization platform. It provides real-time dashboards for infrastructure health, regional availability, VM performance, uptime, service-level objectives, resource capacity, and active alerts. The monitoring environment is isolated from tenant and production networks using a dedicated monitoring network. Only controlled monitoring, API, and notification traffic is permitted between the cloud regions and the monitoring platform. Key outcomes of the solution include: Automatic VM discovery and monitoring enrollment Consistent monitoring across all OpenNebula regions Automated template and policy assignment Centralized alerting and escalation Telegram, Microsoft Teams, and email notifications Real-time Grafana dashboards Reduced manual administration Faster incident detection and response Scalable monitoring for future cloud expansion This project demonstrates my ability to integrate OpenNebula, Zabbix, Grafana, API-driven automation, multi-region monitoring, centralized alerting, and secure network segmentation into a complete private cloud observability platform.
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Cover image for Designed a complete multi-region private
Designed a complete multi-region private cloud architecture using OpenNebula across three geographically separated regions with a total of 20 physical servers. The solution provides centralized cloud management, regional workload isolation, secure inter-region connectivity, shared cloud services, disaster recovery, backup replication, and dedicated monitoring for the complete infrastructure. The environment was structured as follows: Primary Region: 8 servers Secondary Region: 8 servers Disaster Recovery and Expansion Region: 4 servers Centralized OpenNebula management and scheduling KVM-based virtualization across all compute nodes Regional firewalls, routers, load balancers, and gateway services Separate compute, storage, management, tenant, replication, and monitoring networks Shared storage and Ceph-based storage architecture Cross-region backup replication and workload failover Centralized image, template, user, network, and lifecycle management Secure administrator and tenant access through SSO and role-based access control A dedicated monitoring and observability environment was also designed separately from the production cloud network. The monitoring stack included Prometheus or Zabbix for metrics collection, Grafana for dashboards, Alertmanager for notifications, and Loki or the ELK Stack for centralized log management. Regional monitoring proxies and agents collect host, VM, storage, application, and network metrics from each region while keeping the monitoring infrastructure isolated from tenant and production traffic. The architecture was designed to provide: High availability across multiple regions Centralized private cloud management Secure workload and tenant isolation Disaster recovery and regional failover Scalable compute and storage capacity Centralized monitoring, logging, and alerting Infrastructure automation through APIs, Terraform, Ansible, and CI/CD pipelines This project demonstrates my ability to design secure, scalable, highly available private cloud platforms using OpenNebula, KVM, software-defined networking, distributed storage, infrastructure automation, and enterprise monitoring technologies.
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Cover image for Automated Proxmox Inventory with NetBox
Automated Proxmox Inventory with NetBox
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7
Grafana(3)
Joshua Valle's avatar
Joshua Valle
Palisades Park, USA
Building Intelligent Systems and Data-Driven Solutions
Building Intelligent Systems and Data-Driven Solutions
Cover image for Low-Level Schematic of Developing a Web-Based System for Rem...
Low-Level Schematic of Developing a Web-Based System for Remote Collection and Analysis of Vehicle Electrical Systems Over CANBus Using Carloop (https://scholarworks.arcadia.edu/showcase/2023/comp_sci_math/5/)
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Cover image for Web-Based System for Remote Vehicle Data Collection
Web-Based System for Remote Vehicle Data Collection
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Cover image for I'm offering 3 FREE automation audits to e-commerce business...
I'm offering 3 FREE automation audits to e-commerce businesses, individuals, and more this week (first-come, first-served). I'll review your current processes and show you exactly where you're losing time/money, and how to automate it. No obligation, just want to build my portfolio. Comment or DM if interested. You can also view my work online here: https://automated-business-solutions.netlify.app/
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Cover image for FiveM (GTA) Customer Server Development & Scripting Develope...
FiveM (GTA) Customer Server Development & Scripting Developed and maintained custom FiveM (GTA V) servers, specializing in advanced LUA scripting and automation. Built immersive gameplay systems, custom jobs, and server-side logic using QBCORE and ESX frameworks. Integrated third-party APIs, optimized server performance, and delivered engaging multiplayer experiences. Demonstrated expertise in resource management, anti-cheat scripting, and seamless deployment of new features for a vibrant player community.
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Grafana(2)