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Alerting helps notify the right people about production issues quickly.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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.

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

Time-series databases are designed to store and query time-stamped metrics efficiently.

Latency, throughput, and response time are key metrics in system performance.

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.

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

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.