Scalable Identity Graph Platform for Privacy-Compliant User Tracking and Targeting

The client faces significant technical challenges in capturing, linking, and querying massive volumes of user and device data in real time to support audience targeting, reporting, and attribution. Traditional graph databases and key-value stores lack the necessary scalability, performance, and flexibility to handle the high throughput and complex multi-hop queries required at the current data volumes, especially as third-party cookies are phased out. This hampers their ability to maintain high-quality user profiles and deliver relevant advertising experiences efficiently at scale.

A large digital advertising technology provider managing vast user data streams across multiple devices and platforms, aiming to enhance their user identity resolution capabilities while ensuring future scalability and compliance.

• Design and implement a scalable data architecture capable of ingesting up to several hundred thousand ID pairs per second, supporting continuous real-time updates.
• Build a flexible identity graph that enables efficient multi-hop user ID relationships, such as device-to-user, user-to-household, and cross-device linkages.
• Ensure the system supports rapid query response times (milliseconds) even during full table scans and complex multi-hop traversals.
• Develop data retention policies to manage different ID lifecycle lengths, from weeks to several months.
• Enable seamless integration with external Ad Tech data sources, ID providers, and downstream targeting/reporting systems.
• Facilitate ongoing system refinement for optimizing query capabilities, scalability, and data management efficiency.

• High-throughput Data Ingestion Module: capable of processing hundreds of thousands of ID pairs per second, including duplicates deduplication.
• Custom Indexing Layer: build specific indexes to store predefined graph paths, enabling efficient relationship traversal and multi-hop lookups.
• Graph Query Engine: support multi-hop queries in arbitrary directions, such as from email hashes to cookies and vice versa.
• Full Table Scan Capabilities: allow wide join operations for user intersection analysis across different data sources.
• Efficient Data Retention Policies: implement flexible storage durations based on ID type (weeks, months).
• Real-time Data Updating and Merging: continuous graph updates as new ID pairs arrive.
• Batch Processing and Full Scan Support: facilitate large-scale data analysis using distributed processing frameworks.

• Data sources from publishers, demand-side platforms (DSPs), supply-side platforms (SSPs), and third-party enrichers for ID pairs.
• Reporting and analytics systems for audience targeting and attribution.
• Identity resolution data providers for cross-platform user linking.
• Customer Data Platforms (CDPs) and user profile management tools.

• Capability to handle at least 500,000 ID pairs per second ingestion rate.
• Response times within milliseconds for complex multi-hop queries, including full table scans.
• Support for large data volumes, scaling into terabytes daily without significant performance degradation.
• Data privacy and security compliance, including anonymization and controlled data access.
• High availability and fault tolerance to ensure uninterrupted real-time operations.

The implementation of a high-performance, scalable identity graph system will enable the client to accurately map users across multiple devices and data sources at unprecedented scale. This will lead to improved ad targeting relevance, enhanced reporting accuracy, and better audience segmentation. The system's agility and capacity to handle increasing data volumes are expected to support ongoing growth, reduce latency in user matching, and provide a resilient infrastructure aligned with upcoming privacy regulations, ultimately driving higher ROI and client satisfaction.