White paper
A blueprint for real-time recommendation systems
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
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