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Experiments

Interactive Data Visualization

This collection of experiments focuses on building performant frontend visualizations for complex datasets, combining mapping, charting, streaming data, and interaction design.

ReactD3.jsCanvasMapbox GL

Overview

Many of the ideas grew from challenges I encountered while building production analytics applications. The project compares different visualization approaches and rendering technologies under realistic frontend constraints.

Goals

  • Large datasets
  • Streaming information
  • Geospatial analytics
  • Interactive dashboards
  • Performance optimization
  • Rendering efficiency

Technical Focus

  • React
  • D3.js
  • Canvas API
  • eCharts
  • Mapbox GL
  • SVG vs Canvas rendering
  • Performance profiling
  • Data visualization architecture

Challenges

Different visualization libraries excel at different workloads. One of the most interesting engineering questions has been understanding when SVG begins to struggle, when Canvas becomes a better fit, and how rendering choices affect the overall user experience.

What I'm Learning

Visualization is about much more than drawing charts. Good interfaces help people discover patterns, ask better questions, and understand information quickly. That requires balancing performance, usability, interaction design, and visual clarity.

Interview Q&A

Why experiment with multiple visualization libraries?

Every library has different strengths. Building the same concepts with multiple technologies has helped me better understand their trade-offs and choose the right tool for different types of applications.

What interests you about visualization?

I enjoy turning large amounts of technical information into interfaces that people can understand almost immediately. It's one of the places where engineering, design, and user experience come together.

Connect

Let's Build Something Great Together

I'm always happy to chat about frontend engineering, accessibility, React, or interesting opportunities.