Delivering Real-Time Personalization with Databricks and Redis (Blog Post)
Overview
I co-authored this technical article with Anant Pingle at Databricks to show how streaming data becomes personalized application experiences. Databricks Real-Time Mode processes customer activity as it arrives, while Redis serves the latest recommendations to the application.
The article walks through an e-commerce example, from clickstream ingestion and session scoring to Redis data modeling and application reads, with a runnable demo and performance results.
Challenges & Solutions
Keeping recommendations current within a browsing session through continuous event processing.
Connecting Databricks Real-Time Mode to Redis through Spark’s ForeachWriter.
Serving ranked products efficiently with Redis Sorted Sets, Hashes, pipelined operations, and expiration.
Outcomes
Published a joint Redis–Databricks walkthrough with implementation code and architecture guidance.
Documented a test sustaining 100,000 events per second with 157 ms p99 end-to-end latency using Azure Managed Redis.
Explained how the pattern extends to fraud scoring, online ML features, and inventory updates.