anchor.diff:  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[]
  +[id="confluent-schema-registry"]
   == Confluent Schema Registry
   
   There is an alternative https://github.com/confluentinc/schema-registry[schema registry] implementation provided by Confluent.
  
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: Alternatively, you can serialize the message keys and values by using link:https://avro.apache.org/[Apache Avro].
  The Avro binary format is compact and efficient, and Avro schemas make it possible to ensure that the messages have the correct structure. Avro's schema evolution mechanism makes it possible to evolve the schemas over time, which is essential for {prodname} connectors that dynamically generate the message schemas to match the structure of the database tables. Over time, the change events captured by {prodname} connectors and written by Kafka Connect into a topic may have different versions of the same schema. Avro serialization makes it easier for consumers to adapt to the changing schema.
  
  To use Apache Avro as the message format, you must deploy a schema registry that manages Avro message schemas and their versions. 
  
  ifdef::product[]
  [IMPORTANT]
  ====
  