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For example, if your JSON payload looks something on the order of:

{
 "c1": 10000,
 "c2": "bar",
 "create_ts": 1501834166000,
 "update_ts": 1501834166000
}

This will not be compatible for Sink Connectors that require the schema for data ingest when mapping from Kafka Connect datatypes to, for example, JDBC datatypes. Rather, that data is expected to be structured like so:

{
 "schema": {
 "type": "struct",
 "fields": [{
 "type": "int32",
 "optional": true,
 "field": "c1"
 }, {
 "type": "string",
 "optional": true,
 "field": "c2"
 }, {
 "type": "int64",
 "optional": false,
 "name": "org.apache.kafka.connect.data.Timestamp",
 "version": 1,
 "field": "create_ts"
 }, {
 "type": "int64",
 "optional": false,
 "name": "org.apache.kafka.connect.data.Timestamp",
 "version": 1,
 "field": "update_ts"
 }],
 "optional": false,
 "name": "foobar"
 },
 "payload": {
 "c1": 10000,
 "c2": "bar",
 "create_ts": 1501834166000,
 "update_ts": 1501834166000
 }
}

The "schema" is a necessary component in order to dictate to the JsonConverter how to map the payload's JSON datatypes to Kafka Connect datatypes on the consumer side, and certain Sink Connectors absolutely require a schema in order for them to function (eg: JdbcSinkConnector). Instead of requiring explicitly defined schemas within all json records from every producer that is part of a data pipeline where a Sink Connector will consume its records, the JsonConverter should have the ability to make inferences based on the contents of the JSON data.

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