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Kafka uses state stores to remember previous events in stateful stream processing.

Kafka Streams uses local state for quick data access and changelogs for recovery.

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.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

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.

Kafka commit strategies determine how offsets are managed during message processing.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

Event Sourcing stores every action as an event to recreate the current state.

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

Apache Kafka is a system for managing large volumes of events efficiently.

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

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

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.

You can replay Kafka events safely by separating live and replay processes.

Event time is when an event occurs, while processing time is when it is handled.

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

Kafka's `seek()` lets you replay only specific messages, saving time.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Kafka Rebalance Listeners help manage state during partition changes in consumer groups.

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

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

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

Multi-region data consistency ensures that users in different locations see the same data.

Kafka uses heartbeats and timeouts to check if consumers are alive.