Real-Time Analytics Dashboard

Problem

Network and system monitoring teams at a high-scale SaaS enterprise were working off batch reports generated every five minutes. Because of this lag, critical live stream socket anomalies and micro-outages went undetected until clients filed high-priority support tickets, cascading into significant Service Level Agreement (SLA) breaches.

Approach

I architected a WebSockets pipeline to ingest live event data, using React along with D3.js to render canvas-based real-time line charts, scatterplots, and system heatmaps without triggering expensive React re-renders.

// Using canvas rendering for maximum frame-rate stability
useEffect(() => {
  const canvas = canvasRef.current;
  if (!canvas) return;
 
  const ctx = canvas.getContext("2d");
  if (!ctx) return;
 
  let animationFrameId: number;
 
  const render = () => {
    ctx.clearRect(0, 0, width, height);
 
    // Draw real-time stream data points
    ctx.fillStyle = "var(--accent)";
    data.forEach((point) => {
      ctx.beginPath();
      ctx.arc(xScale(point.time), yScale(point.value), 3, 0, 2 * Math.PI);
      ctx.fill();
    });
 
    animationFrameId = requestAnimationFrame(render);
  };
 
  render();
  return () => cancelAnimationFrame(animationFrameId);
}, [data, width, height]);

Result

• Successfully decreased incident detection times (Mean Time to Detect) by 70%.
• Achieved constant 60 FPS performance during real-time visualization of ingestion streams peaking at 10k messages per second.
• Adopted by three critical internal operational teams with high satisfaction scores.