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

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Kafka's parallel processing depends on partitions, not just consumers or threads.

Kafka can help ensure messages are processed once, but requires careful design.

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.

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

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

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

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.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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

Stateless servers are easier to scale because they don't store user session data locally.

Tokenization and analyzers help search engines process and match text effectively.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

Kafka's pause temporarily stops reading messages, while seek changes the reading position.

Distributed tracing helps track requests across microservices to find delays.

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