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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.

Graceful degradation allows apps to function partially during service failures.

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

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

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

Failover ensures systems remain operational by managing leader changes during failures.

Dynamo-style databases are designed for high availability and fault tolerance in distributed systems.

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

Raft and Paxos are protocols that help computers agree on values in distributed systems.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Paxos is a protocol that helps distributed systems agree on one value despite failures.

gRPC allows efficient communication between services using a structured request-response model.

Availability Zones help prevent downtime by isolating failures across multiple locations.