Check out my most recent work, Proteus (proteus.app), tooling for monitoring and deploying Jobs/CronJobs in Kubernetes clusters. This open source product is focused on helping developers visualize their Job and CronJob metrics, improving management of cluster health.
I gave a talk on Data Modeling Strategies as part of JEENY & Bractlet’s Software Engineering Speaker Series!
– Employed TypeScript to define and implement consistent data models and interfaces, ensuring precise metric data validation, streamlined error-checking, and long-term code maintainability for future iterations
– Used React with React Hooks to generate reusable components and manage state of Job data, allowing for dynamic client-side rendering, optimized page load speeds and elevated user engagement through enhanced interactivity
– Incorporated Node.js Express.js framework with model-view-controller (MVC) design pattern to optimize HTTP requests to diverse cluster and Prometheus endpoints, improving code modularity, readability, and scalability
– Implemented Electron to develop a cross-platform desktop application that renders an interactive dashboard, enabling users to conveniently access and analyze crucial performance data such as Job failure time and type
– Mobilized AWS Elastic Kubernetes Service (EKS) and Elastic Compute Cloud (EC2)’s reliable and scalable container management system to deploy Kubernetes clusters for streamlined application development and testing process
– Utilized Docker to establish a containerized channel between NoSQL database and Prometheus to integrate containerized applications into the AWS ecosystem, optimizing resource management and deployment security
– Integrated Kube State Metrics with Prometheus to efficiently collect cluster metrics and visualize critical performance data, expanding Prometheus’ native functionality for comprehensive and effective monitoring
– Reduced overall data scraping times by optimizing PromQL queries by using more efficient aggregation functions and by reducing the number of metrics being scraped
– Developed relational database to manage clinical data and biospecimens, reducing sample processing and analysis times
– Coordinated cross-functional teams for participant recruitment, raising participation and collection from 10% to 100%