Our organization is currently transitioning to a multi-cloud strategy using both AWS and Azure. We are seeing significant latency spikes when synchronizing our RDS instances with Azure-based analytics tools. What are the best architectural patterns to minimize inter-cloud latency and ensure data consistency?
3 answers
Dealing with cross-provider latency requires moving away from the public internet. You should look into dedicated private connectivity options like AWS Direct Connect and Azure ExpressRoute, linked via a cloud exchange provider like Megaport or Equinix. This creates a high-speed, low-latency "bridge" that bypasses public congestion. Additionally, consider implementing a data mesh architecture where processing happens closer to the source to reduce the volume of data traveling across the pipe. Synchronous replication is rarely feasible over long distances, so switching to an asynchronous event-driven model using Kafka can help maintain system performance.
Are you currently using a third-party transit gateway to manage the traffic, or are you relying on standard VPN tunnels between the two virtual private clouds?
You might want to explore Edge Computing. By caching and processing data at the edge before sending it to the secondary cloud, you can significantly reduce the "chatty" nature of your app.
I agree with Brian. Reducing the payload size at the edge is a cost-effective way to handle the physics of latency without over-investing in expensive private lines.
We are using standard VPN tunnels right now, which I suspect is the bottleneck. The overhead of encryption over the public web seems to be adding at least 50ms to every round trip, which is unacceptable for our real-time dashboard requirements.