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Database replication helps keep data available and allows systems to handle more read requests.

Database replication enhances data availability and read performance but has challenges like replication lag.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Kafka uses replication and in-sync replicas to ensure data is always available.

Primary shards partition data, while replica shards duplicate it for redundancy.

Replicas keep your system available, while snapshots allow for data recovery.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Distributed locks ensure only one server performs a task, avoiding duplication.

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

Consistency models determine what data reads in distributed systems can see.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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