Introduction Passing the Kubernetes and Cloud Native Associate (KCNA) exam was a great milestone in my cloud journey. In this post, I’ll walk you through the strategy I used to prepare and pass the exam on the first attempt. I focused on hands-on experience, curated courses, and repeated quiz practice to reinforce core concepts. Why KCNA? KCNA is a foundational certification offered by the Cloud Native Computing Foundation (CNCF). It’s designed to validate your understanding of Kubernetes, cloud-native principles, container orchestration, and open-source tools in the CNCF landscape. ...

KCNA Kubernetes architecture Simple definitions Node: node is a worker machine where container run Cluster: set of node Master node: orchestrate the nodes Components Api server: frontend of the architecture. With api server you can talk with the cluster etcd: Distributed Hash Table **(**DHT) that contains all info of the cluster kubelet: agent that run in all node. Run stop container, interact with the kube api server of the master. container runtime: runtime where containers run controller: control the state of the cluster, restart pods if fails scheduler: distribute works on the nodes Kubectl CLI command for talk with the cluster ...

Introduction In this post, I’ll share how I passed the Terraform Associate certification (HashiCorp Certified: Terraform Associate 003) with a combination of free and paid resources, hands-on labs, and some tips I wish I knew earlier. If you’re preparing for this cert, I hope my experience helps streamline your path. Why Terraform? As someone passionate about DevOps and cloud infrastructure, learning Terraform felt like a natural next step. It’s declarative, cloud-agnostic, and widely adopted in production environments. Earning the certification helped validate my skills and gave me more confidence when building infrastructure as code (IaC) for personal and professional projects. ...

For my university course in Big Data and Cloud Computing, I developed a hands-on project to process and analyze U.S. domestic flight data using a big data pipeline built on Apache Spark, Hadoop and Hive. 📂 Project Repository on GitHub ✈️ Project Overview The dataset comes from the U.S. Department of Transportation (DOT) and includes over 20 years of flight records across domestic U.S. routes. It contains rich details such as: ...

A complete walkthrough of building a production-grade MLOps pipeline using AWS Glue, SageMaker, Terraform, and Kubernetes — with automated CI/CD and GitOps for infrastructure and frontend deployment.