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Data Processing Workflows

This document describes the end-to-end data processing workflows that keep the SG Cars Trends platform updated with the latest vehicle registration and COE bidding data.

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Workflow Sequence Diagram

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Workflow Components

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1. Cars Data Processing Workflow

Purpose: Process monthly vehicle registration data from LTA DataMall Trigger:
  • Scheduled: Every hour during business hours (9 AM - 6 PM, Mon-Fri)
  • Manual: POST /workflows/trigger endpoint
Process Flow:
  1. Timestamp Check: Query Redis for last_updated:cars to determine if processing is needed
  2. File Download: Fetch “Monthly New Registration of Cars by Make.zip” from LTA DataMall
  3. Checksum Validation: Compare file checksum with previous version to detect changes
  4. Data Extraction: Extract and parse CSV files if changes detected
  5. Data Transformation:
    • Clean special characters from make names
    • Normalize vehicle type classifications
    • Convert string numbers to integers
  6. Database Insert: Batch insert new records with conflict resolution
  7. Cache Update: Update Redis timestamp for successful processing
  8. Blog Generation: Create AI-powered blog post analyzing latest data
  9. Social Media: Announce new blog post across configured platforms
Data Sources: Monthly New Registration of Cars by Make.zip Key Fields Processed:
  • month: YYYY-MM format
  • make: Vehicle manufacturer (normalized)
  • fuel_type: Petrol, Diesel, Electric, Hybrid
  • vehicle_type: Cars, Motorcycles, Buses, etc.
  • number: Registration count

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2. COE Data Processing Workflow

Purpose: Process Certificate of Entitlement bidding results Trigger:
  • Scheduled: Every hour during business hours
  • Manual: POST /workflows/trigger endpoint
Process Flow:
  1. Timestamp Check: Query Redis for last_updated:coe
  2. File Download: Fetch COE bidding results from LTA DataMall
  3. Checksum Validation: Detect file changes
  4. Data Processing: Parse COE bidding data and PQP rates
  5. Database Updates: Insert bidding results and premium rates
  6. Conditional Blog Generation:
    • Generate blog posts only when both bidding exercises are complete (bidding_no = 2)
    • Provides comprehensive monthly COE analysis
  7. Social Media: Announce completed bidding results and blog posts
Data Sources:
  • COE bidding results CSV
  • Prevailing Quota Premium (PQP) rates
Key Fields Processed:
  • month: Bidding month
  • bidding_no: Exercise number (1 or 2)
  • vehicle_class: COE category
  • quota: Available certificates
  • bids_received: Total bids submitted
  • premium: Winning premium (SGD)

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3. Blog Generation Workflow

Purpose: Create AI-powered market insights from processed data AI Integration: Google Gemini API for content generation Content Creation Process:
  1. Data Analysis: Analyze latest month’s data for trends and insights
  2. Content Generation: Use LLM to create comprehensive blog posts
  3. Structured Output: Generate title, content, excerpt, and metadata
  4. SEO Optimization: Create SEO-friendly slugs and descriptions
  5. Storage: Save to database with duplicate prevention
  6. Social Distribution: Announce new content across platforms
Blog Post Features:
  • Executive summaries with key insights
  • Data tables for fuel type and vehicle type breakdowns
  • Market trend analysis and implications
  • Professional market commentary
  • Reading time estimation
  • AI attribution and model versioning

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4. Social Media Integration Workflow

Purpose: Distribute updates across multiple social platforms Supported Platforms:
  • Discord: Webhook-based notifications
  • LinkedIn: Business-focused updates
  • Telegram: Channel messaging
  • Twitter: Social media engagement
Publishing Flow:
  1. Platform Validation: Check configuration for each enabled platform
  2. Parallel Publishing: Simultaneously post to all configured platforms
  3. Error Handling: Graceful degradation with Discord error notifications
  4. Result Aggregation: Collect success/failure statistics

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Error Handling and Reliability

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Redundancy Prevention

Checksum Validation: Files are only processed if checksums differ from previous versions Timestamp Tracking: Redis timestamps prevent duplicate processing Database Constraints: Unique key constraints prevent duplicate records

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Error Recovery

Workflow State: QStash maintains workflow state and enables retries Discord Notifications: Failed workflows trigger Discord alerts with error details Graceful Degradation: Social media failures don’t block data processing

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Monitoring and Logging

CloudWatch Logs: Comprehensive logging for all workflow steps Structured Logging: JSON-formatted logs for easy parsing Error Tracking: Detailed error messages with context

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Configuration

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Environment Variables

Core Workflow:
  • QSTASH_TOKEN: QStash authentication
  • LTA_DATAMALL_API_KEY: LTA DataMall access
  • DATABASE_URL: PostgreSQL connection
  • UPSTASH_REDIS_REST_*: Redis configuration
AI Integration:
  • GEMINI_API_KEY: Google Gemini AI access
Social Media:
  • DISCORD_WEBHOOK_URL: Error notifications
  • LINKEDIN_*: LinkedIn API credentials
  • TELEGRAM_*: Bot configuration
  • TWITTER_*: Twitter API credentials

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Scheduling Configuration

Cron Schedule: */60 0-10 * * 1-5
  • Every hour from 9 AM to 6 PM
  • Monday through Friday (Singapore business hours)
  • Aligned with LTA DataMall update patterns

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Performance Considerations

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Batch Processing

  • Database inserts use batch operations for efficiency
  • Large datasets processed in chunks to avoid timeouts

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Caching Strategy

  • Redis caching for API responses
  • Timestamp-based cache invalidation
  • CDN caching for static content

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Resource Optimization

  • ARM64 Lambda functions for cost efficiency
  • 120-second timeout for long-running processes
  • Memory allocation optimized for CSV processing