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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

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

Distributed backpressure helps manage the flow of work between services to prevent overload.