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Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Consistent hashing helps distribute data across servers with minimal movement.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

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