Data Architecture for CTV/OTT Ad Delivery (with Privacy and Scalability)

Introduction:

This post explores a data architecture suitable for a CTV/OTT ad delivery system, emphasizing scalability, revenue tracking, and user privacy. It adopts an events-based approach for data flow, ensuring accurate ad billing and preventing double billing. We'll draw inspiration from structures used by leading on-demand streaming platforms, while adhering to data privacy best practices.

Data Flow and Components:

The system utilizes a two-way data flow:

1. Ad Decision and Delivery: This flow determines which ads are shown to specific users and delivers them.

2. Ad Event Feedback and Analytics: This flow captures user interaction with ads and provides insights for advertisers and the streaming platform.

Key Components:

• Machine Learning Model (External): This pre-existing model predicts ad click-through rates and audience segments for targeted advertising. (This functionality is assumed to be outside the scope of this interview.)

• Key-Value Store (Redis): Stores a list of available ads for a particular event, ensuring fast retrieval for ad selection.

• Content Delivery Network (CDN): Manages ad delivery across different time zones and handles ad appropriateness checks.

• API Gateway: Provides a secure and privacy-conscious interface for ad selection and interaction logging.

• Document Database: Stores detailed information about ad impressions, including user interaction (play, pause, skip), timestamp, replay status, errors, and latency.

• Apache Kafka: A real-time streaming platform that ingests ad event data from the API Gateway.

• Apache Spark: Performs real-time analytics on ad event data using Kafka for insights dashboards.

• Data Warehouse: Stores aggregated ad event data for historical analysis and reporting.

Addressing Challenges:

• Double Billing Prevention: The document database ensures an ad is only recorded as "shown" once, even if replayed. We also maintain double the anticipated ad inventory to minimize the chance of no ad being available.

• Privacy-Conscious Design: The API Gateway enforces data access controls and anonymizes user data before exposing it to analytics.

• Reduced User Churn Focus: While the system models subscription changes, its primary focus is on delivering relevant ads, not influencing user decisions.

Data Exposure and Analytics:

• Real-time Analytics: Advertisers and streaming platforms receive real-time estimates of ad impressions and potential revenue through Apache Spark dashboards.

• Aggregated Logs: Platform developers can access aggregated data from the data warehouse to monitor user viewership trends and adjust server capacity using micro-services deployed through load balancers.

Security and Privacy Considerations:

• Leverage industry-standard encryption protocols for data transmission and storage.

• Implement user consent mechanisms for data collection and adhere to relevant privacy regulations.

Benefits:

• Scalable architecture to handle peak viewership events.

• Accurate ad billing with double-billing prevention.

• Real-time insights for advertisers and the streaming platform.

• User privacy focus with anonymization and data access controls.

Conclusion:

This data architecture provides a robust foundation for CTV/OTT ad delivery, balancing scalability, revenue tracking, and user privacy. It leverages familiar components like Redis and Kafka, making it adaptable for system design interviews. Remember, this is a conceptual framework, and specific implementations may vary based on the chosen technologies and company requirements.