Real-time Analytics Pipeline Throughput

import matplotlib.pyplot as plt
import squarify
import numpy as np

# Events per second (thousands)
labels = ['Click Events', 'Page Views', 'API Calls', 'Transactions',
          'User Sessions', 'Error Logs', 'System Metrics', 'IoT Sensors']
sizes = [450, 320, 580, 95, 145, 185, 280, 420]
total = sum(sizes)

# CLAUDE.md colors
colors = ['#F5276C', '#27D3F5', '#5314E6', '#6CF527', '#F5B027',
          '#C82909', '#276CF5', '#27F5B0']

fig, ax = plt.subplots(figsize=(12, 8), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

pct = [s/total*100 for s in sizes]
squarify.plot(sizes=sizes, 
              label=[f'{l}\n{s}K/s ({p:.1f}%)' for l, s, p in zip(labels, sizes, pct)],
              color=colors, alpha=0.85, ax=ax,
              text_kwargs={'fontsize': 9, 'color': 'white', 'fontweight': 'bold'})

ax.axis('off')
ax.set_title(f'Event Throughput - {total/1000:.1f}M events/sec', 
             fontsize=18, color='#f8fafc', fontweight='bold', pad=20)

plt.tight_layout()
plt.show()