anchor.diff:  .. 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:
  +. Run the newly built Kafka Connect image, configuring it so it uses the Avro converter (or configure specific converters to do so):
   +
   ifdef::community[]
   [source,subs="attributes"]
  
positive.code: Each {prodname} connector provides a configuration property, `sanitize.field.names` that you can set to `true` if you have columns that do not adhere to Avro rules for names. Setting `sanitize.field.names` to `true` allows serialization of non-conformant fields without having to actually modify your schema.
  
  ifdef::community[]
  == Confluent Schema Registry
  
  There is an alternative https://github.com/confluentinc/schema-registry[schema registry] implementation provided by Confluent.
  
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:
  