Engineering Real-Time Data Synchronization Across Multi-Cloud Systems
summary
Describes a three-layer architecture that separates data acquisition, a synchronization orchestrator, and application access, with event-driven propagation, versioned messages, entity-keyed partitions and atomic conditional updates to enforce ordering and detect conflicts. Offers concrete conflict policies (source-authority, last-writer-wins, field-wise merge), idempotent delta events, hybrid encryption and audit logs. Simulation shows measurable improvements in latency, consistency and security across AWS/Azure/GCP scenarios. Provides a small Kotlin Spring Boot demo (containerized on Kubernetes) as a working prototype, not a full production system.
AI-written summary · about
chapters (9)
- 0:00Multi-cloud sync intro
- 2:03Why multi-cloud adoption
- 4:55Three-layer sync architecture
- 7:26Concurrent updates and versioning
- 10:15Event formats and rules
- 12:23Writes from multiple regions
- 14:30Conflict resolution and staleness
- 16:57Recovering from outages
- 18:35Q&A
related talks
38mMulti-Cloud Replication: From Chaos to Control
26mMulti-Cloud Native Data with Spring and VMware Data Solutions
29mFollowing the Data: A Deeper Look into Distributed Applications
30mData Consistency Patterns in Cloud Native Applications
14mA Cloud-native Architecture for Replicated Data Services
01h 01mCloud Integrations, Data Architecture and MDM solutions for Enterprise Applications
49mApplications architecture spanning multiple clouds
29mEnhancing Flexibility and Productivity with Access Patterns and Storage-Agnostic Abstractions