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

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.

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

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

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

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

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Database per Service ensures each microservice owns its data, reducing dependencies.

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Message delivery semantics define how messages are sent in distributed systems and handle failures.