White paper
A blueprint for real-time recommendation systems
What you'll learn
Deliver personalized experiences in the moment
Today’s recommendation engines must operate at scale under increasing complexity: more data, noisier signals, and customer expectations for instant, relevant engagement. Whether it’s personalizing a homepage, finding the right product at checkout, or tailoring a content feed, businesses must act on limited signals in milliseconds, or risk losing the moment.
This white paper outlines a practical blueprint for building and updating real-time recommendation systems that scale with today’s demands.
Key highlights
How today's recommendation systems combine offline and real-time components to support fast, relevant decisions at scale
Why low-latency feature access is essential for scalable inference pipelines and the critical role of the feature store
How modern systems handle multi-stage retrieval and re-ranking, edge and core deployment, and incomplete or conflicting data
How Aerospike's patented Hybrid Memory Architecture delivers sub-millisecond reads, high throughput, and global availability at a fraction of the cost of memory-only systems
Real-world examples from Sony Interactive Entertainment, Myntra, Quantcast, Wayfair, Rakuten, and Flipkart