One thing which is not clear about kafka or kinesis is when you have multiple consumers for the same topic how will they get the data and in what order , and what happens when consumers die down. How do you handle consumers in your data pipeline ?
> By having a notion of parallelism—the partition—within the topics, Kafka is able to provide both ordering guarantees and load balancing over a pool of consumer processes. This is achieved by assigning the partitions in the topic to the consumers in the consumer group so that each partition is consumed by exactly one consumer in the group. By doing this we ensure that the consumer is the only reader of that partition and consumes the data in order. Since there are many partitions this still balances the load over many consumer instances. Note however that there cannot be more consumer instances than partitions.
Kinesis provides the same ordering guarantees. They use different terminology (Kafka topics == Kinesis streams; Kafka partitions == Kinesis shards) but have the same system interface. The details of the APIs used for consumption differ, but they provide the same basic functionality of Kafka's "consumer groups".