Kafka Linking plays a crucial role in various scenarios including hybrid cloud deployments, disaster recovery, and cluster migration. AutoMQ's Kafka Linking capability can connect AutoMQ with any other streaming system compatible with the Kafka protocol, enabling the replication of data and metadata between the two clusters. This feature supports a variety of Kafka Linking applications and use cases.
AutoMQ, through innovative cluster proxy technology, allows the migration of data from the old cluster to the new cluster without affecting the applications currently running in the production environment, achieving a non-stop migration.
For data infrastructures that rely on Kafka consumption points to work, like Flink jobs, it becomes particularly important to retain these points during cluster migration. This allows users to migrate Kafka to AutoMQ without making any changes to these existing data infrastructures, greatly reducing the difficulty of cluster migration.
Kafka Linking is a fully built-in capability of AutoMQ, with no external dependencies. Users do not need to maintain connectors or linking pipelines themselves.
All data and metadata of the Kafka cluster will be synchronized, including Topics, Consumer Groups, Offsets.
AutoMQ is a new generation of Kafka that is 100% compatible with Apache Kafka. It is compatible with Kafka alternatives on the market, such as Confluent, Aiven, WarpStream, and Redpanda.
AutoMQ provides a very simple and easy-to-use operation UI. Even a Kafka novice can easily complete all the configurations of Kafka Linking.
AutoMQ supports seamless migration from existing Kafka or AutoMQ clusters to new AutoMQ clusters. Whether driven by the need for architectural evolution or the desire to migrate specific topics from old to new clusters, the entire process retains consumer offsets and synchronizes all data and metadata. This feature can be configured through the AutoMQ Enterprise Edition console UI.
Establish a Disaster Recovery (DR) cluster to take over in case of an outage or catastrophe in your primary cluster. The synchronization feature ensures that the DR cluster is always up-to-date with data, metadata, topic structure, configurations, and consumer offsets. This enables low Recovery Point Objectives (RPOs) and Recovery Time Objectives (RTOs). Maintaining a DR cluster is straightforward without relying on external components or intricate management. Thanks to the Kafka Linking feature, which preserves consumer offsets during migration, consumers can failover and recover near the point of interruption, minimizing downtime without additional coding efforts.
By setting up a read-only Kafka cluster, you can offload analytical or batch jobs to isolated hardware, enhancing Kafka's fanout capabilities. This cluster is scalable in seconds, providing virtually infinite read throughput on demand.
Geographically distributing data allows consumers to access data locally, providing lower read latency and improving overall performance.
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