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

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

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

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

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

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

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.

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

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

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

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

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

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

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

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

Request-Response asks for a result, while Events notify that something has happened.

Microservices can communicate using five key patterns based on their needs.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Structured logging helps in quickly identifying issues by organizing log data consistently.