// Category: debezium-using
// Type: concept
// ModuleID: description-of-debezium-architecture
// Title: Description of Debezium architecture
[id="debezium-architecture"]
= {prodname} Architecture

ifdef::community[]
Most commonly, you deploy {prodname} by means of Apache {link-kafka-docs}/#connect[Kafka Connect].
Kafka Connect is a framework and runtime for implementing and operating:
endif::community[]

ifdef::product[]
You deploy {prodname} by means of Apache {link-kafka-docs}/#connect[Kafka Connect].
Kafka Connect is a framework and runtime for implementing and operating:
endif::product[]

* Source connectors such as {prodname} that send records into Kafka
* Sink connectors that propagate records from Kafka topics to other systems

The following image shows the architecture of a change data capture pipeline based on {prodname}:

image::debezium-architecture.png[{prodname} Architecture]

As shown in the image, the {prodname} connectors for MySQL and PostgresSQL are deployed to capture changes to these two types of databases. Each {prodname} connector establishes a connection to its source database:

* The MySQL connector uses a client library for accessing the `binlog`.
* The PostgreSQL connector reads from a logical replication stream.

Kafka Connect operates as a separate service besides the Kafka broker.

By default, changes from one database table are written to a Kafka topic whose name corresponds to the table name.
If needed, you can adjust the destination topic name by configuring {prodname}'s {link-prefix}:{link-topic-routing}#topic-routing[topic routing transformation]. For example, you can:

* Route records to a topic whose name is different from the table's name
* Stream change event records for multiple tables into a single topic

After change event records are in Apache Kafka, different connectors in the Kafka Connect eco-system can stream the records to other systems and databases such as Elasticsearch, data warehouses and analytics systems, or caches such as Infinispan.
Depending on the chosen sink connector, you might need to configure {prodname}'s {link-prefix}:{link-event-flattening}#new-record-state-extraction[new record state extraction] transformation. This Kafka Connect SMT propagates the `after` structure from {prodname}'s change event to the sink connector. This is in place of the verbose change event record that is propagated by default.

ifdef::community[]
== {prodname} Server

Another way to deploy {prodname} is using the xref:operations/debezium-server.adoc[{prodname} server].
The {prodname} server is a configurable, ready-to-use application that streams change events from a source database to a variety of messaging infrastructures.

The following image shows the architecture of a change data capture pipeline that uses the {prodname} server:

image::debezium-server-architecture.png[{prodname} Architecture]

The {prodname} server is configured to use one of the {prodname} source connectors to capture changes from the source database.
Change events can be serialized to different formats like JSON or Apache Avro and then will be sent to one of a variety of messaging infrastructures such as Amazon Kinesis, Google Cloud Pub/Sub, or Apache Pulsar.

== Embedded Engine

Yet an alternative way for using the {prodname} connectors is the xref:operations/embedded.adoc[embedded engine].
In this case, {prodname} will not be run via Kafka Connect, but as a library embedded into your custom Java applications.
This can be useful for either consuming change events within your application itself,
without the needed for deploying complete Kafka and Kafka Connect clusters,
or for streaming changes to alternative messaging brokers such as Amazon Kinesis.
You can find https://github.com/debezium/debezium-examples/tree/main/kinesis[an example] for the latter in the examples repository.
endif::community[]
