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

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

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

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

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

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Choosing the right labels in time-series data is crucial to avoid inefficiencies.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Request-Response asks for a result, while Events notify that something has happened.

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

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

Event Sourcing stores every action as an event to recreate the current state.

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

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

RPO and RTO are key metrics for planning disaster recovery strategies.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.