Analyzing the Dub.co analytics playbook

Dub serves as a model for modern small SaaS companies, particularly in how it facilitates real-time, user-facing analytics for marketing teams through its open-source platform. The company manages and shortens links to track performance and conversions, processing over 150 million events annually. Dub's technical architecture includes storing link metadata and click events using NextJS, PlanetScale, Prisma, and Tinybird, employing an event-driven approach to efficiently handle CRUD operations. Dub utilizes materialized views and columnar databases to optimize low-latency queries, choosing simplicity over complexity by using managed services like Tinybird's Events API for high-throughput ingestion. The company employs best practices such as filtering first, deferring joins, and selectively pre-aggregating data to maintain performance while offering dynamic filtering in its analytics APIs. This allows Dub to serve thousands of users in real-time, providing a flexible API that supports dynamic query patterns without affecting the frontend experience. Dub's approach emphasizes the importance of aligning data preparation with querying needs to ensure scalability and efficiency, making it a valuable case study in SaaS analytics.