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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.

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

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

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

Availability is about access; reliability is about correct performance.

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

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

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

Batch processing collects data to process later, while stream processing handles data in real-time.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

Tumbling windows are non-overlapping time segments, while hopping windows can overlap.

Watermarks help stream processing systems manage event time and late events.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

CDC with Kafka allows real-time updates from databases to multiple services.