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

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

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

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

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

Batch processing collects data to process later, while stream processing handles data in real-time.

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.

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

Backpressure prevents system overload by managing message processing rates in Kafka.

Backpressure helps manage message flow in Kafka during traffic spikes.

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Kafka's assign() method allows manual control of partition assignments for consumers.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Graceful shutdown in Kafka ensures no data loss during consumer termination.