ViralRanks

Data & Compliance at ViralRanks

Our unified approach to responsible API usage, data security, and YouTube compliance ensures trust, transparency, and actionable insights for creators and partners.

YouTube API Implementation

Advanced strategies for efficient data collection and processing

Efficient Data Collection

  • Batch processing with optimal 50-item chunks
  • Smart pagination for large dataset handling
  • Automated rate limiting and quota management
  • Parallel processing for improved performance

Data Processing Pipeline

  • Real-time data validation and cleaning
  • Intelligent deduplication mechanisms
  • Structured error handling and logging
  • Automated data quality monitoring

BigQuery Integration

  • Optimized table schemas for analytics
  • Efficient append-only data storage
  • Automated table partitioning
  • Regular performance optimization

Performance Metrics

  • Daily channel and video updates
  • Comprehensive metric tracking
  • Historical trend analysis
  • Custom performance indicators

Security & Compliance Measures

Enterprise-grade security and compliance framework

Data Security

  • Secure API key management
  • End-to-end data encryption
  • Regular security audits
  • Access control policies

API Optimization

  • Smart quota allocation
  • Request batching
  • Cache optimization
  • Rate limit monitoring

YouTube API Compliance

  • Full adherence to API policies
  • Regular compliance audits
  • Transparent data practices

Data Collection Details

Comprehensive overview of data points we collect and process

Channel Information

  • Channel ID
  • Channel Title
  • Channel Avatar
  • Channel Description
  • Channel Tags
  • Channel Country

Channel Metrics

  • Total Channel Subscribers
  • Total Channel Video Views
  • Total Channel Videos

Video Information

  • Video ID
  • Video Title
  • Video Description
  • Video Thumbnail
  • Video Tags
  • Publication Date
  • YouTube Category

Video Metrics

  • Total Video Views
  • Total Video Likes
  • Total Video Comments

ViralRanks Derived Metrics

ViralRanks exclusively uses native YouTube Data API metrics and carefully calculated derived metrics that fully comply with' YouTube's Acceptable Metrics Guidelines. We do not create custom YouTube metrics or replace YouTube's native metrics with our own calculations.

Example Hourly Growth Calculation

12024h18023h25022h20021h16020h22019h30018h24017h18016h16015h14014h19013h21012h17011h13010h1009h1808h2407h3006h2205h1604h2003h2502h180Now
(Example hourly views for a single video over the last 24 hours)

YouTube's Acceptable Metrics

We collect the total view count for each video every hour. Hourly growth is calculated by subtracting the previous hour's view count from the current hour's view count. This approach uses only official YouTube API data and simple math, ensuring accuracy and full compliance with YouTube's Acceptable Metrics Guidelines. No external data sources or custom metrics are used—just transparent, native YouTube data and straightforward calculations.

Following 30 Days Data Retention Policy

In compliance with YouTube's Terms of Service, we automatically remove video performance data older than 30 days through scheduled BigQuery SQL jobs. This ensures that our historical data analysis and trend calculations always adhere to YouTube's data retention requirements while maintaining accurate and compliant growth metrics.

Data Pipeline

YouTube Data API

Channel & Video Data

Batch Processing

50 Items per Batch

ETL Pipeline

Transform & Validate

BigQuery Storage

Analytics & ML

Data Collection
  • Daily channel updates
  • Video metrics tracking
  • Smart quota management
Processing
  • Real-time validation
  • Deduplication checks
  • Error handling
Storage
  • Optimized schemas
  • Automated partitioning
  • Performance tuning

Tools & Architecture

Vertex AI Notebooks

Managed Jupyter notebooks for YouTube data extraction and processing

  • Python scripts for YouTube API integration
  • Automated data collection workflows
  • Notebook Executor for scheduled jobs
  • Resource-optimized execution environment

BigQuery ML Integration

Machine learning capabilities directly in BigQuery

  • Anomaly detection in performance metrics
  • Trend analysis and prediction
  • Custom ML models for content analysis
  • Real-time scoring and evaluation

Automated Workflows

Scheduled jobs for consistent data updates

  • Daily metric collection and processing
  • Automated data validation checks
  • Performance monitoring and alerts
  • Resource usage optimization

BigQuery DataWarehouse

Scalable data architecture for multiple content verticals

  • Separate datasets per vertical (Podcasts, Entertainment, Education)
  • Vertical-specific ML model training and feature engineering
  • Optimized table partitioning for performance
  • Cross-vertical analytics and insights