Wahaj Aayani

A Vue.js-based data-agnostic tool for designing custom charts in Adobe XD.

I was the full-stack developer for the project. A user can choose from various graphs like pie, donut, line, and area to visualize the data they uploaded in CSV format or copy-pasted.

The app had a Parse Server on the back end. It was responsible for writing and updating data to the database.

A vehicle tracker application provides real-time locations of vehicles with a central dashboard to plot them on a map.

I was engaged majorly to help design the back end's architecture and the web's dashboard.

Each vehicle would emit its stats like location, state, speed, mileage, and fuel status every 15 seconds, which would end up in a stream (Kafka). Each stream had two consumers—the app ingestor and a unified stream processor (Flink).

After a few transformations, the ingestor would provide this data to the app's back end. At the same time, Flink would perform an analytical operation in real-time to extract meaningful data for various purposes, such as average mileage, occasional proximity, potential theft, over-speeding, among others.

The Vue.js-based dashboard would then show this information to the user.

MyMonorepo is my very own self-investment in pursuit of having a single space for all good coding practices that I have acquired throughout my professional exploits.

It has a variety of project types and boilerplates that can fire up and deploy a fully working project in a matter of minutes.

Currently, MyMonorepo supports three project types that it can deploy with a few simple commands documented in the README Lambda template (with or without cron), services like Node.js, Express, and NestJS, as well as websites, such as CRA and Next.js.

The CI/CD pipeline is set up to dynamically generate a Terraform file that deploys the project on one push. The deployment states are maintained in an AWS S3 bucket, so it is possible to decommission a running project instantly.

Right now, it is capable of deploying to AWS EC2, ECS, and Lambda, but it will always be a work in progress to accommodate different deployment types.

A mobile application with real-time match commentary for several sporting events like NBA, NCAA, and UEFA.

I was the chief architect, project manager, and developer of the overall project, but I was primarily engaged in back-end work and developing a few admin dashboards.

The mobile application is made in React Native, while the back end is in the NestJS framework of Node.js.

SportsRadar API (later Sports Data) is used for fetching live sporting events that end up in a queue (RabbitMQ). These events are added to a PostgreSQL database running on an AWS RDS server and broadcast to a user's mobile phone in real-time using WebSockets.

The user can then comment on each unique event which is instantly visible to other users of the app.

Extensive use of Node.js and React is made across all twelve application services. It has different dashboards built for other purposes. A React-based admin dashboard, an analytical dashboard with a Cube.js back end, and a video manager generate match highlights for each game that communicates with a Python back end.

Additionally, I set up CI/CD pipelines using GitLab that makes the new changes live as soon as the code is merged. And the system metrics and logs can be viewed in the ELK.