Building a pub/sub architecture with Kafka for product analytics enables scalable, real-time insights into user behavior and product performance.

Topic design and organization:

• Create dedicated Kafka topics for different event types such as page views, clicks, purchases, and feature usage
• Organize topics by product area, user journey stage, or analytical use case
• Implement proper partitioning strategies to enable parallel processing

Producer setup:

• Set up event producers using Snowplow trackers and application servers to send data to appropriate Kafka topics
• Publish event data in real-time as user interactions occur
• Implement proper serialization and schema validation for consistent data quality

Consumer and processing:

• Create specialized consumers for different analytics use cases including cohort analysis, conversion tracking, and behavioral segmentation
• Use Kafka Streams or Apache Flink to process data in real-time for immediate insights
• Implement stream processing for aggregating metrics, computing event counts, and performing complex analytics

Visualization and activation:

• Integrate with tools like Power BI, Tableau, or custom dashboards to visualize product analytics metrics
• Display key metrics including active users, product views, conversions, and engagement patterns
• Enable real-time alerts and automated actions based on product analytics insights