Data Tech 2026

Real-time personalization requires more than fast model inference. It depends on data systems that can combine batch computation, streaming signals, feature management, and ranking logic while operating under strict latency and reliability constraints.

This talk presents a production-inspired architecture for context-aware personalization, showing how batch pipelines, streaming data flows, feature stores, and downstream scoring or ranking layers work together to support dynamic user experiences at scale. Rather than focusing only on model development, the session highlights the data architecture needed to make personalization practical, resilient, and responsive in real-world systems.

The talk also explores key engineering trade-offs, including data freshness versus latency, feature completeness versus response time, and resilience strategies when parts of the pipeline are delayed or degraded. Attendees will gain a practical view of how modern data platforms can support intelligent decisioning and real-time personalization in production environments.