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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.

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

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

Redis is a fast in-memory data store used for caching and low-latency applications.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Data residency is crucial for compliance with laws about where data can be stored.

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

Backpressure helps manage message flow in Kafka during traffic spikes.