Stream: Vale
Time: 12:00 - 12:45
Everyone wants real-time data. Few organizations define what real-time actually means. This session explores how enterprise teams design low-latency data architectures while balancing: • Consistency • Scalability • Resilience • Operational risk • Cost Attendees will learn practical design considerations for moving from batch integration to event-driven architectures while maintaining enterprise-grade reliability and governance. This session explores why integration complexity continues to grow even as platforms become more sophisticated, and how organizations are reducing repetitive work through reusable data delivery patterns and shared operational data architectures. Attendees will learn practical approaches for: • Reusing existing data pipelines instead of rebuilding them • Reducing duplicate extracts and data movement • Simplifying access to operational and mainframe data • Improving consistency across downstream data consumers • Supporting new use cases without creating new integration debt
There is currently no attachment for Designing Real-Time Data Architectures Without Breaking Your Mainframe
Hands-on fractional CTO innovating with real-time data and AI. Achievements: Driving innovation at Hazelcast, a leader in real-time data and AI, capable of processing billions of events per second with zero downtime. Innovating in real-time use-cases e.g. fraud detection, transaction monitoring, churn detection, real-time risk, personalization, clickstream analytics, connected vehicles, and predictive maintenance. Past firms include Akka, Hazelcast, Cloudera, and webMethods