Data Management

OnlyAutomator’s data management system provides robust storage, processing, and analysis capabilities for content creator data. Data Architecture Overview

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Data Categories

OnlyAutomator manages several categories of data:

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1. User Data

Information about system users (content creators):

• Account Information: Email, name, profile details
• Authentication Details: Hashed passwords, session tokens
• Preferences: System settings and preferences
• Subscription Status: Current plan, billing information

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2. Creator Analytics

Performance metrics for content creators:

• Subscriber Metrics: Growth, churn, and engagement statistics
• Revenue Data: Income streams, transaction history
• Content Performance: Engagement with various content types
• Message Analytics: Response times, engagement rates

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3. Operational Data

System operational information:

• System Logs: Application events and errors
• Audit Trails: Record of significant user actions
• Performance Metrics: System response times and health
• Task Queue Status: Status of background processing jobs

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Data Storage Solutions

The system uses multiple storage solutions optimized for different data types:

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PostgreSQL (Supabase)

Primary relational database for structured data:

• User accounts and profiles
• Subscription and billing records
• Creator analytics metrics
• System configuration

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Object Storage

Binary and large object storage:

• User avatars and images
• Exported reports and data files
• System backups
• Temporary processing files

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Redis Cache

In-memory cache for high-speed data access:

• Session data
• Frequently accessed metrics
• API response caching
• Rate limiting information

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Data Processing

OnlyAutomator implements several data processing mechanisms:

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Real-time Processing

Immediate data handling for user-facing features:

• Analytics dashboard updates
• Notification generation
• User action responses

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Batch Processing

Scheduled tasks for intensive operations:

• Daily analytics aggregation
• Report generation
• System maintenance
• Data archiving

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Event-driven Processing

Processing triggered by specific events:

• Webhook responses
• Subscription changes
• External API updates
• System alerts

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Data Security

Comprehensive security measures protect all data:

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Encryption

Multiple encryption layers:

• Data at rest: All stored data encrypted
• Data in transit: TLS/SSL for all communications
• Field-level encryption: Extra protection for sensitive data
• Encryption key management: Secure key rotation and storage

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Access Control

Robust permissions system:

• Role-based access: Appropriate permissions per user type
• Row-level security: Data access limited to owners and authorized users
• API authentication: Secure token-based API access
• Admin audit logs: Tracking of all administrative actions

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Compliance Features

Tools to meet regulatory requirements:

• Data export: Complete data export for user requests
• Data deletion: Comprehensive removal capabilities
• Privacy controls: Granular user consent management
• Retention policies: Configurable data retention periods

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Data Integration

The system connects with multiple data sources:

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OnlyFans Integration

Primary data source integration:

• Credential management: Secure storage of access tokens
• Data synchronization: Regular updates from OnlyFans API/scraping
• Change detection: Identification of significant changes
• Error handling: Robust handling of API changes and limitations

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Third-party Sources

Additional integrations for enhanced functionality:

• Payment processors: Transaction data from Stripe, PayPal
• Marketing platforms: Campaign data from email and ad services
• Social media: Basic metrics from connected social accounts
• Analytics services: Enhanced metrics from Google Analytics, etc.

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Data Governance

Policies ensuring proper data management:

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Data Quality

Ensuring accurate and reliable data:

• Validation rules: Data format and content verification
• Consistency checks: Cross-reference verification
• Error correction: Automated and manual correction workflows
• Data completeness: Identification of missing information

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Lifecycle Management

Managing data throughout its lifetime:

• Creation policies: Standards for data entry and generation
• Retention rules: Time-based data preservation policies
• Archiving procedures: Moving older data to long-term storage
• Deletion workflows: Secure data removal when appropriate

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Disaster Recovery

Ensuring data survivability:

• Regular backups: Automated daily and weekly backups
• Point-in-time recovery: Ability to restore to specific moments
• Geographic redundancy: Data stored in multiple physical locations
• Recovery testing: Regular validation of restoration procedures