📌 This document outlines my experience implementing a Kubernetes microservice using KEDA (Kubernetes Event-Driven Autoscaling) to scale based on Pub/Sub messages. The focus is on how KEDA enables efficient scaling of workloads in response to event-driven triggers and the key lessons I learned along the way.
Managing logs across multiple projects was challenging, requiring frequent context switching in GCP Logs Explorer. By implementing log scopes, we centralized logging into a single view, improving troubleshooting and monitoring. Using Terraform, we automated log scope creation, efficiently handling project limits and ensuring scalability. 🚀📊
A high CPU usage issue in a VM caused delays in data transfer from Datastore to BigQuery. Switching to a Pub/Sub-based approach improved scalability, efficiency, and reliability. 🚀🔄
I was tasked with setting up a new development environment for testing and development. It lacked the necessary data, so I had to carefully transfer it from one project to another. This document outlines my experience and the steps I took to transfer data between `Google Cloud Projects`, including `Cloud Storage buckets`, `Firestore (Datastore)`, and `BigQuery datasets`. This guide can be helpful when creating a new environment and seeding it with data from an existing project.
A practical guide on using cf-terraforming to automate Cloudflare resource management with Terraform
A project where I built and deployed a serverless API using Google Cloud Functions and Firestore, integrated with Cloud Build as CI/CD to deploy functions on every push to GitHub automatically. An API that can serve resume data in JSON format. I used Terraform to manage and provision cloud infrastructure.
A step-by-step guide to creating a static website on Google Cloud Platform using Terraform, Cloud Storage, Cloud CDN, and HTTPS load balancer
Lorem Ipsum Dolor Si Amet