Live Viewer Profiles

This accelerator demonstrates how to build a real-time use case leveraging Snowplow event data for a video streaming site. Using Java, Apache Kafka, and AWS DynamoDB, this solution accelerator processes live streaming events to build a dynamic, real-time profile for viewers, capturing their interactions with videos and ads. It offers a hands-on guide for developers to understand how to build, deploy, and extend real-time event-driven architectures using Snowplow data and Kafka.

The accelerator is inspired by a typical video streaming scenario where users interact with video content and ads. The goal is to maintain an up-to-date viewer profile in DynamoDB by processing real-time Snowplow events.

Use Cases

• Real-time viewer insights
  Build a live profile for each viewer, capturing their current video status, ad engagement, and overall behavior
• Personalized recommendations
  Use real-time viewer profiles to dynamically recommend content or ads based on engagement patterns
• Ad performance tracking
  Monitor ad clicks and skips in real time for targeted campaign optimization
• Engagement analytics
  Track video completion rates and pausing behavior to analyze content performance
• Alerting and monitoring
  Trigger alerts for unusual patterns, such as excessive ad skipping or session drops

Infrastructure Overview

The infrastructure for this accelerator includes:

1. Snowplow JavaScript Tracker with Media Plugin
   Captures events such as video starts, pauses, ad interactions, and completions from a React-based video streaming site
2. Snowplow & Snowbridge
   The Snowplow pipeline processes and enriches events before routing them to Kafka via Snowbridge. This all runs locally in docker-compose to simulate a real Snowplow environment using Localstack
3. Apache Kafka
   Acts as the messaging layer to stream enriched events in real-time to the Live Viewer Profiles generator
4. Java-based Live Viewer Profiles generator
   Implements the state machine logic for real-time updates to the Live Viewer Profiles
5. AWS DynamoDB
   Serves as the state store, maintaining Live Viewer Profiles with attributes such as video status, ad clicks, and timestamps

Additional Insights

This accelerator showcases how real-time event processing can drive personalized and dynamic user experiences. By maintaining live viewer profiles in DynamoDB, you can analyze engagement, optimize ad placements, and enhance viewer satisfaction. The state machine approach ensures efficient tracking of user status transitions, making the solution both scalable and extensible.

This framework can be adapted to other real-time scenarios, such as live sports streaming, online gaming, or e-commerce interactions. Developers can extend the logic to incorporate additional features like predictive modeling or custom metrics for deeper insights.