Darshan Vandra is a Senior Software Engineer with 7 years of experience building cloud-native, data-driven systems using Go, Python, Kubernetes and JavaScript, currently at Red Hat in Ahmedabad. He blends applied AI and machine learning know-how with hands-on expertise in network and cyber security, Unix/Linux administration, and hardware/network certification. Darshan is an active open-source contributor—having improved Red Hat’s Dependency Analytics VS Code extension and enhanced real-time network analysis in the Skydive project—demonstrating a knack for both UI/UX polish and low-level telemetry integration. Comfortable across the full stack, he focuses on turning complex distributed and network data into actionable insights and reliable tooling.
8 years of coding experience
8 years of employment as a software developer
Bachelor of Engineering (B.E.), Computer Engineering, Bachelor of Engineering (B.E.), Computer Engineering at L.D College of Engineering - Ahmedabad
Contributions:3 releases, 28 reviews, 40 commits in 6 months
Contributions summary:Darshan primarily contributed to the development and maintenance of the Red Hat Dependency Analytics VS Code extension. Their work involved updating API endpoints, configuring staging environments, and managing anonymous user tracking features. They also worked on integrating Golang support for stack analyses. Additionally, the user made significant changes to the UI and notification features, enhancing the user experience and integrating with external dependency reports.
An open source real-time network topology and protocols analyzer
Role in this project:
Full-stack Developer
Contributions:19 commits, 19 PRs, 119 comments in 9 months
Contributions summary:Darshan contributed to the user interface (UI) of the Skydive project by implementing an OpenFlow rule filter within the UI. They modified JavaScript files related to rule details and table displays, indicating a focus on enhancing the visual representation and interaction of network rules. Furthermore, the user added and integrated SFlow decoding and metrics, including OVSDP, OVS App Resources, and VLAN counters into the project, which demonstrates their contributions towards analyzing and displaying network performance data. The user also added advanced options for the OVS SFlow capture type with sampling rate and polling interval.
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