Cloud-Native Best Practices | Anti-Patterns & Lessons Learned

The Core Idea

Deep learning relies on representing data across layered feature spaces.

This approach allows the system to learn complex patterns and relationships within the data.

Twelve-Factor App: Follow twelve-factor methodology

Microservices: Design for service independence, allowing for modularity and scalability.

Observability: Implement comprehensive monitoring – metrics, logs, and traces – to gain insights into system behavior.

Frequently Asked Questions

Follow the twelve-factor app methodology, design for failure, implement health checks, use external configuration, store state externally, implement graceful shutdown, write logs to stdout, use environment parity, implement retries and circuit breakers, design for horizontal scaling, and use containers and orchestration. These practices ensure applications are cloud-native ready.

Common anti-patterns include monolithic containers, shared databases, hardcoded configuration, missing health checks, ignoring observability, no autoscaling, tight coupling, ignoring security, no disaster recovery, and manual deployments. Avoid these to build successful cloud-native applications.

Frequently asked questions