Flight Data Visualization - DataMinex

  • Home
  • Flight Data Visualization

Real-Time Flight Data Analytics Bot with Dynamic Chart Generation via Telegram

Ā 

šŸš€ Template Overview

This advanced n8n workflow creates an intelligent Telegram bot that transforms raw CSV flight data into stunning, interactive visualizations. Users can generate professional charts on-demand through a conversational interface, making data analytics accessible to anyone via messaging.

Key Innovation: Combines real-time data processing, Chart.js visualization engine, and Telegram’s messaging platform to deliver instant business intelligence insights.

Ā 

šŸŽÆ What This Template Does

Transform your flight booking data into actionable insights with four powerful visualization types:

  • šŸ“ˆ Bar Charts: Top 10 busiest airlines by flight volume
  • šŸ„§ Pie Charts: Flight duration distribution (Short/Medium/Long-haul)
  • šŸ© Doughnut Charts: Price range segmentation with average pricing
  • šŸ“Š Line Charts: Price trend analysis across flight durations

Each chart includes auto-generated insights, percentages, and key business metrics delivered instantly to users’ phones.

Ā 

šŸ—ļø Technical Architecture

Core Components
  1. Telegram Webhook Trigger: Captures user interactions and button clicks
  2. Smart Routing Engine: Conditional logic for command detection and chart selection
  3. CSV Data Pipeline: File reading ā†’ parsing ā†’ JSON transformation
  4. Chart Generation Engine: JavaScript-powered data processing with Chart.js
  5. Image Rendering Service: QuickChart API for high-quality PNG generation
  6. Response Delivery: Binary image transmission back to Telegram
Ā 
Data Flow Architecture
User Input ā†’ Command Detection ā†’ CSV Processing ā†’ Data Aggregation ā†’ 
Chart Configuration ā†’ Image Generation ā†’ Telegram Delivery

Ā 

šŸ› ļø Setup Requirements

Prerequisites
  • n8n instanceĀ (self-hosted or cloud)
  • Telegram Bot TokenĀ from @BotFather
  • CSV datasetĀ with flight information
  • Internet connectivityĀ for QuickChart API

Ā 

Dataset Source

This template uses theĀ Airlines Flights DataĀ dataset from GitHub:
šŸ”—Ā Dataset:Ā Airlines Flights Data by Rohit Grewal

Ā 

Required Data Schema

Your CSV file should contain these columns:

airline,flight,source_city,departure_time,arrival_time,duration,price,class,destination_city,stops

Ā 

File Structure
/data/
  ā””ā”€ā”€ flights.csv (download from GitHub dataset above)

Ā 

āš™ļø Configuration Steps

1. Telegram Bot Setup
  1. Create a new bot via @BotFather on Telegram
  2. Copy your bot token
  3. Configure the Telegram Trigger node with your token
  4. Set webhook URL in your n8n instance
2. Data Preparation
  1. Download the dataset fromĀ Airlines Flights Data
  2. Upload the CSV file toĀ /data/flights.csvĀ in your n8n instance
  3. Ensure UTF-8 encoding
  4. Verify column headers match the dataset schema
  5. Test file accessibility from n8n
3. Workflow Activation
  1. Import the workflow JSON
  2. Configure all Telegram nodes with your bot token
  3. Test theĀ /startĀ command
  4. Activate the workflow

Ā 

šŸ”§ Technical Implementation Details

Chart Generation Process

Bar Chart Logic:

// Aggregate airline counts
const airlineCounts = {};
flights.forEach(flight => {
  const airline = flight.airline || 'Unknown';
  airlineCounts[airline] = (airlineCounts[airline] || 0) + 1;
});

// Generate Chart.js configuration
const chartConfig = {
  type: 'bar',
  data: { labels, datasets },
  options: { responsive: true, plugins: {...} }
};

Dynamic Color Schemes:

  • Bar Charts: Professional blue gradient palette
  • Pie Charts: Duration-based color coding (lightā†’dark blue)
  • Doughnut Charts: Price-tier specific colors (greenā†’purple)
  • Line Charts: Trend-focused red gradient with smooth curves

Ā 

Performance Optimizations
  1. Efficient Data Processing: Single-pass aggregations with O(n) complexity
  2. Smart Caching: QuickChart handles image caching automatically
  3. Minimal Memory Usage: Stream processing for large datasets
  4. Error Handling: Graceful fallbacks for missing data fields

Ā 

Advanced Features

Auto-Generated Insights:

  • Statistical calculations (percentages, averages, totals)
  • Trend analysis and pattern detection
  • Business intelligence summaries
  • Contextual recommendations

User Experience Enhancements:

  • Reply keyboards for easy navigation
  • Visual progress indicators
  • Error recovery mechanisms
  • Mobile-optimized chart dimensions (800x600px)

Ā 

šŸ“ˆ Use Cases & Business Applications

Airlines & Travel Companies
  • Fleet Analysis: Monitor airline performance and market share
  • Pricing Strategy: Analyze competitor pricing across routes
  • Operational Insights: Track duration patterns and efficiency
Data Analytics Teams
  • Self-Service BI: Enable non-technical users to generate reports
  • Mobile Dashboards: Access insights anywhere via Telegram
  • Rapid Prototyping: Quick data exploration without complex tools
Business Intelligence
  • Executive Reporting: Instant charts for presentations
  • Market Research: Compare industry trends and benchmarks
  • Performance Monitoring: Track KPIs in real-time

Ā 

šŸŽØ Customization Options

Adding New Chart Types
  1. Create new Switch condition
  2. Add corresponding data processing node
  3. Configure Chart.js options
  4. Update user interface menu
Data Source Extensions
  • Replace CSV with database connections
  • Add real-time API integrations
  • Implement data refresh mechanisms
  • Support multiple file formats
Ā 
Visual Customizations
// Custom color palette
backgroundColor: ['#your-colors' __="#" __="#" __="#" __="#"],
// Advanced styling
borderRadius: 8,
borderSkipped: false,
// Animation effects
animation: { duration: 2000, easing: 'easeInOutQuart' }

Ā 

šŸ”’ Security & Best Practices

Data Protection
  • Validate CSV input format
  • Sanitize user inputs
  • Implement rate limiting
  • Secure file access permissions
Error Handling
  • Graceful degradation for API failures
  • User-friendly error messages
  • Automatic retry mechanisms
  • Comprehensive logging

Ā 

šŸ“Š Expected Outputs

Sample Generated Insights
  • “āœˆļø Vistara leads with 350+ flights, capturing 23.4% market share"
  • “šŸ“ˆ Long-haul flights dominate at 61.1% of total bookings"
  • “šŸ’° Budget category (ā‚¹0-10K) represents 47.5% of all bookings"
  • “šŸ“Š Average prices peak at ā‚¹14K for 6-8 hour duration flights"
Performance Metrics
  • Response Time: <3 seconds for chart generation
  • Image Quality: 800x600px high-resolution PNG
  • Data Capacity: Handles 10K+ records efficiently
  • Concurrent Users: Scales with n8n instance capacity

Ā 

šŸš€ Getting Started

  1. DownloadĀ the workflow JSON
  2. ImportĀ into your n8n instance
  3. ConfigureĀ Telegram bot credentials
  4. UploadĀ your flight data CSV
  5. TestĀ withĀ /startĀ command
  6. DeployĀ and share with your team

Ā 

šŸ’” Pro Tips

  • Data Quality: Clean data produces better insights
  • Mobile First: Charts are optimized for mobile viewing
  • Batch Processing: Handles large datasets efficiently
  • Extensible Design: Easy to add new visualization types