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

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.

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

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

Latency is the time for one request, while throughput is how many requests are processed.

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

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

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

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

OLTP handles transactions while OLAP is for data analysis and reporting.

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

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

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

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

A timeout indicates a deadline was exceeded, not necessarily a failure.