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

CDC with Kafka allows real-time updates from databases to multiple services.

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.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

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

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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

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

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Backpressure helps manage message flow in Kafka during traffic spikes.