Streams are a powerful way to collect and analyze data at high speed, enabling developers to uncover meaningful insights and take immediate actions. But working with streams and transforming raw data into actionable information can be challenging, especially when performance and scalability are critical.

In this workshop, we’ll tackle these challenges by building a Bluesky bot that processes data from Bluesky’s stream of posts into actionable insights using Redis, Spring AI, and Redis OM Spring—a powerful combination for modern, data-driven applications.

AGENDA
During this workshop, you will work with practical examples and hands-on exercises to learn how to:
- Collect data into streams using Redis Streams.
- Leverage probabilistic data structures like - Bloom Filters, TopK, and Count-Min Sketch for efficient data summarization.

Use a Vector Database to:
- Implement Retrieval-Augmented Generation (RAG) to enhance your LLM queries with a personalized knowledge base.
- Reduce costs and improve performance using semantic caching for minimizing unnecessary LLM calls.
- Perform vector similarity search with Redis OM Spring to classify and analyze data effectively.

And finally:
- Query your data efficiently for actionable insights.
- Ensure scalability and reliability.

This is an interactive workshop. We will be learning while iterating over real challenges. Don’t forget your laptop!