As an AI language model, I can suggest the following strategies for an architect to design for resilience in a system with high data velocity requirements:
1. Use distributed systems and architectures: Distributing the load across multiple servers enables better load balancing and reduces the risk of a single point of failure.
2. Implement redundancy: Incorporating backup systems, such as redundant servers or data centers, can mitigate the risk of data loss or system failure.
3. Use caching: Caching technology can help to reduce the amount of data that needs to be retrieved from databases or other sources, reducing the load on the system.
4. Employ monitoring and analytics: Monitoring tools and analytics can provide real-time feedback on system performance, enabling architects to quickly identify problems and take action to address them.
5. Use microservices: Microservices architecture can help to break up the system into manageable components, making it easier to isolate issues and to deploy changes or updates.
6. Employ fault-tolerance practices: Implementing fault-tolerant practices, such as using error-correcting codes, can help to ensure that data is accurately transmitted and received despite network errors or other issues.
By considering these strategies, architects can design systems that are resilient and capable of handling high data velocity requirements.
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