Topic Replication

The video tutorial explains the concept of distributed systems in the context of big data, focusing on the importance of replication to ensure data availability and reliability. It covers the replication factor, which determines the number of data copies across brokers, and discusses how this helps maintain data access even if some brokers fail. The tutorial also introduces the concept of a leader and in-sync replicas (ISR) in Kafka, explaining how Zookeeper manages leader elections to ensure continuous data service.

What is the primary purpose of replication in a distributed system?

• To reduce the cost of data storage

• To simplify data management

• To ensure data availability in case of machine failure

• To increase the speed of data processing

In the hands-on example, what is the replication factor used for Topic A?

• 1

• 4

• 2

• 3

What happens if a broker goes down in a replicated Kafka setup?

• Another broker can serve the data

• The system stops functioning

• Data processing speed increases

• Data is lost permanently

What is the role of a leader in a Kafka partition?

• To receive and serve data for the partition

• To store backup data

• To reduce data redundancy

• To manage network traffic

Who manages the leader election process in Kafka?

• Kafka itself

• Zookeeper

• The system administrator

• The client application