I have written cloud formation templates for deploying ECS fargate in custom VPC on a public subnet. I have divided templates into Network and Fargate Service Stacks. Network parent stack for creates a custom VPC, ECS cluster, Roles, ALB and to export these as output for others stacks. Fargate Service Stack creates Input parameters, Task definitions, load balancer target group, load balancer rule, and ECS service.
I used DevOps with Kubernetes using Terraform (Infrastructure as Code) and Azure DevOps (Continuous Delivery) on multiple cloud platforms (AWS and Azure). I learned the basics of Continuous Integration and Continuous Delivery and implemented them using Azure DevOps. I learned to create Kubernetes Clusters and deployed Docker Containers of Microservices to Kubernetes using Azure DevOps Pipelines on the Cloud with AWS EKS and Azure AKS.
I learned how to use the capabilities of Keptn to provide self-healing for an application without modifying code. This implementation will scale up the pods of an application using remediation actions detected by Dynatrace if the application undergoes heavy CPU saturation.
To understand keptn, I installed it on a Kubernetes cluster, along with Istio for traffic routing and ingress control. I used keptn as the control plane for continuous delivery and automated operations. Deployed my microservice with blue/green deployments and set up quality gates to prevent bad builds to reach production. Istio as the ingress and service mesh within the cluster for traffic routing between blue/green versions of our services. Unleash as a feature toggle framework that is connected to keptn to toggle feature based on monitoring data.
I used Dynatrace to understand the Distributed tracing for the site reliability. I used EasyTravel demo .Net and Java application to understand Dynatrace Environment by deploying the OneAgent on different Hosts, and setting up process groups, services, tagging, and Alerting. I Explored the Dashboards, Smartscpae, problems, user sessions, logs, service flow, purepaths. I monitored Transactions and services, hosts, databases. I also checked out host & process availability, and problem notification.
Developed a simple Currency Conversion system to understand RESTful web services and Microservices concepts. I used Java and Spring Boot, in combination with Spring Web MVC makes it easy to develop RESTful web services. By using Spring (Dependency Management), Spring MVC, Spring Boot, Spring Security, Spring Boot Actuator, Swagger, Maven, Eclipse (IDE), Postman, and Tomcat Embedded Web Server to understand the RESTful web services. I used the microservice basics to establish communication, enable load balancing, scaling up and down of microservices. I learned to centralize the configuration of microservices with Spring Cloud Config Server. I implemented Eureka Naming Server, Distributed tracing with Spring Cloud Sleuth and Zipkin, and created fault-tolerant microservices with Zipkin.
CI/CD pipeline using various DevOps Tools to deploy a simple Tomcat project on AWS. Installed Jenkins, Ansible, Docker, Git, Maven, and Tomcat on various AWS EC2 servers. Created various Jenkin jobs along with Git version controlling for Continuous Integrations. Automated Docker containers and SSH to other servers using Ansible playbooks for Continuous Delivery.
“InaNutshell” is an easy-to-use web service and one-stop access to the store, listen and learn the information from personal documents. Different types of documents (such as pdf, txt) can be stored and users can learn the information through concise summaries provided for each document. The summaries are presented as audio clips so that users can consume information on the go. Additionally, Amazon Alexa skill has been implemented so that users can listen to the audio clips through voice commands.
Implemented a given randomly generated cellular automata rule table that is used to change pixel states according to their neighboring pixels’ state. In the case of a binary image, each pixel’s next state is determined by looking at its neighbors in a 3 ×3 window. And also, we have a start image and a goal image. With that information, we going to write a GA that produces an image showing edges of the given image after some number of passes.
