Renu Tewari describes what MirrorMaker does for Kafka today and what is coming with version 2:
Apache Kafka has become an essential component of enterprise data pipelines and is used for tracking clickstream event data, collecting logs, gathering metrics, and being the enterprise data bus in a microservices based architectures. Kafka is essentially a highly available and highly scalable distributed log of all the messages flowing in an enterprise data pipeline. Kafka supports internal replication to support data availability within a cluster. However, enterprises require that the data availability and durability guarantees span entire cluster and site failures.
The solution, thus far, in the Apache Kafka community was to use MirrorMaker, an external utility, that helped replicate the data between two Kafka clusters within or across data centers. MirrorMaker is essentially a Kafka high-level consumer and producer pair, efficiently moving data from the source cluster to the destination cluster and not offering much else. The initial use case that MirrorMaker was designed for was to move data from clusters to an aggregate cluster within a data center or to another data center to feed batch or streaming analytics pipelines. Enterprises have a much broader set of use cases and requirements on replication guarantees.
Read on for the list of benefits and upcoming features.