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

Event time is when an event occurs, while processing time is when it is handled.

Late-arriving events can be dropped, updated, or routed based on system needs.

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

Kafka can help ensure messages are processed once, but requires careful design.

Lamport Timestamps help order events in distributed systems while preserving causality.

Physical time tells when events happen, while logical time tells their order.

Apache Kafka is a system for managing large volumes of events efficiently.

In distributed systems, timestamps don't always reflect the true order of events.

Vector clocks help track the order and concurrency of events in distributed systems.

Read-after-write consistency ensures users see their latest updates immediately.

Backpressure helps manage message flow in Kafka during traffic spikes.