How to Build a Composable Product Analytics Stack with Snowplow and Mitzu

In setting up a composable product analytics stack using Snowplow and Mitzu, companies can achieve greater customization and precision compared to traditional analytics platforms like Mixpanel and Amplitude. This approach allows teams to define their own event tracking, data structuring, and product metric calculations, thus avoiding the limitations of predefined schemas and opaque metric logic found in conventional tools. Snowplow handles data collection, processing, and storage, enabling real-time or batch processing of enriched events, while Mitzu facilitates self-service product and marketing analytics directly from the data warehouse. This architecture supports rapid experimentation and AI readiness by ensuring data ownership, adaptability, and efficient SQL-based analytics. Enhanced by advancements in data warehouse technologies and table formats like Iceberg and Delta, this stack allows for seamless integration and insight generation, ultimately empowering teams to better understand and act on user behavior without the constraints of black-box solutions.