How do distributed clustering systems work in production?

Distributed clustering splits computation across multiple nodes for scalability.

Large datasets are partitioned across workers. Local clustering is performed, then results are merged or refined using hierarchical merging or parameter sharing. Systems often use Spark, Ray, or parameter servers. Synchronization and consistency are major challenges.