anchor.diff:  ====
   
   ifdef::community[]
  -Another option is using the Confluent schema registry, which is described later.
  +Another option is using the <<confluent-schema-registry,Confluent schema registry>>, which is described later.
   endif::community[]
   
   // Type: concept
   // Title: Overview of deploying a {prodname} connector that uses Avro serialization
  -[id="overview-of-deploying-a-debezium-connector-that-uses-avro-serializatioin"]
  +[id="overview-of-deploying-a-debezium-connector-that-uses-avro-serialization"]
   == Overview
   
   To deploy a {prodname} connector that uses Avro serialization, there are three main tasks: 
  
positive.code: .. Create a custom image for Kafka Connect. See link:{LinkCDCInstallOpenShift}[NameCDCInstallOpenShift] for an example of how to do this. Start with the `Dockerfile` in that example. Then add the {registry} converters to the connector directories. 
  endif::product[]
  
  . Run a Kafka Connect image that is configured to use Avro:
  +
  ifdef::community[]
  [source,subs="attributes"]
  
negative.code: +
  `oc create -f https://raw.githubusercontent.com/apicurio/apicurio-registry-operator/master/docs/resources/example-cr/in-memory.yaml`
  +
  The in-memory deployment is not suitable for production. Use the Apache Kafka persistence option for production. For more information, see {LinkServiceRegistryGetStart}[NameServiceRegistryGetStart].
  endif::product[]
  
  . Build a {prodname} container image that implements the Avro converter:
  