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

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

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

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

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.

Backpressure helps manage message flow in Kafka during traffic spikes.

Fetch limits control data size from Kafka, while poll records limit processed records.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka consumer offsets help track message processing to avoid loss and duplication.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Kafka consumer lag shows how far behind consumers are from new messages.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

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

Classifying failures and using controlled retries in Kafka prevents outages.