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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.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Stateless servers are easier to scale because they don't store user session data locally.

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

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