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// overview/assembly-kafka-components.adoc

[id="kafka-concepts-components_{context}"]
= Kafka component architecture

[role="_abstract"]
A Kafka cluster comprises the brokers responsible for message delivery. 

ZooKeeper is used for cluster management.
When deploying Kafka in KRaft (Kafka Raft metadata) mode, cluster management is simplified by integrating broker and controller roles within Kafka nodes, eliminating the need for ZooKeeper.
Kafka nodes take on the roles of brokers, controllers, or both.
Roles are configured in Strimzi using node pools. 

Each of the other Kafka components interact with the Kafka cluster to perform specific roles.

.Kafka component interaction

image:overview/kafka-concepts-supporting-components.png[Data flows between several Kafka components and the Kafka cluster. See the component descriptions after this image.]

Apache ZooKeeper:: Apache ZooKeeper provides a cluster coordination service, storing and tracking the status of brokers and consumers. ZooKeeper is also used for controller election. If ZooKeeper is used, the ZooKeeper cluster must be ready before running Kafka. In KRaft mode, ZooKeeper is not required because the coordination is managed in the Kafka cluster by Kafka nodes operating as controllers.
Kafka Connect:: Kafka Connect is an integration toolkit for streaming data between Kafka brokers and other systems using _Connector_ plugins.
Kafka Connect provides a framework for integrating Kafka with an external data source or target, such as a database, for import or export of data using connectors.
Connectors are plugins that provide the connection configuration needed.
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* A _source_ connector pushes external data into Kafka.
* A _sink_ connector extracts data  out of Kafka
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External data is translated and transformed into the appropriate format.
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You can deploy Kafka Connect with `build` configuration that automatically builds a container image with the connector plugins you require for your data connections.
Kafka MirrorMaker:: Kafka MirrorMaker replicates data between two Kafka clusters, within or across data centers.
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MirrorMaker takes messages from a source Kafka cluster and writes them to a target Kafka cluster.
Kafka Bridge:: Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster.
Kafka Exporter:: Kafka Exporter extracts data for analysis as Prometheus metrics, primarily data relating to offsets, consumer groups, consumer lag and topics. Consumer lag is the delay between the last message written to a partition and the message currently being picked up from that partition by a consumer
