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Observability helps engineers understand system behavior to debug production issues effectively.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

A single component's failure can take down your entire application, even with redundancy.

FAANG engineers use high-level design principles to create scalable systems.

Disaster Recovery Testing verifies that your recovery plan works effectively in real situations.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Disaster recovery ensures systems can restore services and data after major disruptions.

High Availability ensures your application remains operational even during server failures.

Availability is about access; reliability is about correct performance.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

Fault domains help prevent multiple servers from failing together in an architecture.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Microservices can communicate using five key patterns based on their needs.

Distributed backpressure helps manage the flow of work between services to prevent overload.

Active-passive architecture ensures a standby system takes over if the main system fails.

Alerting helps notify the right people about production issues quickly.

REST, RPC, and gRPC are different methods for communication in software systems.

Choosing the right boundaries for microservices is crucial for effective architecture.

Fallbacks allow systems to handle failures safely and maintain user experience.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Database per Service ensures each microservice owns its data, reducing dependencies.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

CQRS separates how we change data from how we read data, improving scalability.

Microservices and modular monoliths differ mainly in deployment and communication methods.

Container data can be lost unless stored in persistent volumes.

Stateless servers are easier to scale because they don't store user session data locally.