Datadog faced significant engineering challenges in efficiently managing and propagating user-specific configuration data across thousands of workload containers handling millions of logs per second. The core issue was ensuring low-latency and reliable configuration updates in a complex, multi-tenant distributed environment. Initial solutions relied on direct database connections and Kafka for cache invalidation, but these proved inadequate as Datadog scaled, leading to database overloads and unreliable notifications. A new architecture was developed using a gRPC context service and a context-publisher system, which allowed each workload container to maintain a local, frequently updated replica of the necessary configuration data, drastically reducing database load and improving reliability. This approach, which leverages the small size and slow update rate of context data, has been successfully generalized to handle various use cases, enhancing Datadog's resilience to database outages while maintaining swift configuration updates. The project underscores the importance of understanding data properties to devise scalable solutions, and Datadog continues to expand this system to support more types of context data, inviting engineers to join their team in tackling such challenges.