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Alerting helps notify the right people about production issues quickly.

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

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

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

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Availability is about access; reliability is about correct performance.

Monolithic and microservices architectures each have unique advantages and challenges for software development.

Consistency models determine what data reads in distributed systems can see.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

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

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Backpressure helps manage message flow in Kafka during traffic spikes.

Distributed tracing helps track requests across microservices to find delays.

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

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

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.