Yeva Byzek walks us through a disaster recovery scenario when running Apache Kafka:
Disaster strikes—catastrophic hardware failure, software failure, power outage, denial of service attack or some other event causes one datacenter with an Apache Kafka® cluster to completely fail. Yet Kafka continues running in another datacenter, and it already has a copy of the data from the original datacenter, replicated to and from the same topic names. Client applications switch from the failed cluster to the running cluster and automatically resume data consumption in the new datacenter based on where it left off in the original datacenter. The business has minimized downtime and data loss resulting from the disaster, and continues to run its mission critical applications.
Ultimately, enabling the business to continue running is what disaster recovery planning is all about, as datacenter downtime and data loss can result in businesses losing revenue or entirely halting operations. To minimize the downtime and data loss resulting from a disaster, enterprises should create business continuity plans and disaster recovery strategies.
Distributed data sources can still succumb to disaster and many of the same policies that people learn when working with relational databases apply to things like Kafka as well.