Zendesk Source Connector for Confluent Cloud

Zendesk is a customer service system for tracking, prioritizing, and solving customer support tickets. The fully-managed Zendesk Source connector for Confluent Cloud copies data into Apache Kafka® from various Zendesk support tables such as tickets, ticket_audits, ticket_fields, groups, organizations, satisfaction_ratings, among others. The connector streams data to Zendesk using the Zendesk Support API. See Supported tables for more information.

Note

This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Zendesk Source Connector for Confluent Platform.
If you require private networking for fully-managed connectors, make sure to set up the proper networking beforehand. For more information, see Manage Networking for Confluent Cloud Connectors.

Features

The Zendesk Source connector provides the following features:

Topics created automatically: The connector can automatically create Kafka topics.
At least once delivery: The connector guarantees that records are delivered at least once to the Kafka topic.
Supported data formats: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, and JSON (schemaless) output formats. You must enable Schema Registry to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).
Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Limitations

Be sure to review the following information.

For connector limitations, see Zendesk Source Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

Supported tables

See the following dropdown list for supported Zendesk tables.

Manage custom offsets

You can manage the offsets for this connector. Offsets provide information on the point in the system from which the connector is accessing data. For more information, see Manage Offsets for Fully-Managed Connectors in Confluent Cloud.

To manage offsets:

Manage offsets using Confluent Cloud APIs. For more information, see Cluster API reference.

Get the current offset

To get the current offset, make a GET request that specifies the environment, Kafka cluster, and connector name.

GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets
Host: https://api.confluent.cloud

Response:

Successful calls return HTTP 200 with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
     "offsets": [

        {
           "partition": {
           "name": "tickets"
           },
           "offset": {
           "updated_at": 1712559408
           }
        },
        {
           "partition": {
           "name": "targets"
           },
           "offset": {
           "created_at": 1607376776000
           }
        },
        {
           "partition": {
           "name": "users"
           },
           "offset": {
           "updated_at": 1712639446
           }
        },
        {
           "partition": {
           "name": "ticket_audits"
           },
           "offset": {
           "created_at": 1607359500000
           }
        }
     ],
    "metadata": {
        "observed_at": "2024-03-28T17:57:48.139635200Z"
    }
}

Responses include the following information:

The position of latest offset.
The observed time of the offset in the metadata portion of the payload. The observed_at time indicates a snapshot in time for when the API retrieved the offset. A running connector is always updating its offsets. Use observed_at to get a sense for the gap between real time and the time at which the request was made. By default, offsets are observed every minute. Calling GET repeatedly will fetch more recently observed offsets.
Information about the connector.

Update the offset

To update the offset, make a POST request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies new offset and a patch type.

POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
Host: https://api.confluent.cloud

 {
     "type": "PATCH",
      "offsets": [
         {
            "partition": {
            "name": "tickets"
            },
            "offset": {
            "updated_at": 1554687029
            }
         },
         {
            "partition": {
            "name": "targets"
            },
            "offset": {
            "created_at": 1554687029
            }
         },
         {
            "partition": {
            "name": "users"
            },
            "offset": {
            "updated_at": 1554687029
            }
         },
         {
            "partition": {
            "name": "ticket_audits"
            },
            "offset": {
            "created_at": 1554687029
            }
         }
      ]
 }

Considerations:

You can only make one offset change at a time for a given connector.
This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.
For source connectors, the connector attempts to read from the position defined by the requested offsets.

Response:

Successful calls return HTTP 202 Accepted with a JSON payload that describes the offset.

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
       {
          "partition": {
          "name": "tickets"
          },
          "offset": {
          "date_updated": 1618184736
          }
       }
    ],
    "requested_at": "2024-03-28T17:58:45.606796307Z",
    "type": "PATCH"
}

Responses include the following information:

The requested position of the offsets in the source.
The time of the request to update the offset.
Information about the connector.

Delete the offset

To delete the offset, make a POST request that specifies the environment, Kafka cluster, and connector name. Include a JSON payload that specifies the delete type.

 POST /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request
 Host: https://api.confluent.cloud

{
  "type": "DELETE"
}

Considerations:

Delete requests delete the offset for the provided partition and reset to the base state. A delete request is as if you created a fresh new connector.
This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.
Do not issue delete and patch requests at the same time.
For source connectors, the connector attempts to read from the position defined in the base state.

Response:

Successful calls return HTTP 202 Accepted with a JSON payload that describes the result.

{
  "id": "lcc-example123",
  "name": "{connector_name}",
  "offsets": [],
  "requested_at": "2024-03-28T17:59:45.606796307Z",
  "type": "DELETE"
}

Responses include the following information:

Empty offsets.
The time of the request to delete the offset.
Information about Kafka cluster and connector.
The type of request.

Get the status of an offset request

To get the status of a previous offset request, make a GET request that specifies the environment, Kafka cluster, and connector name.

GET /connect/v1/environments/{environment_id}/clusters/{kafka_cluster_id}/connectors/{connector_name}/offsets/request/status
Host: https://api.confluent.cloud

Considerations:

The status endpoint always shows the status of the most recent PATCH/DELETE operation.

Response:

Successful calls return HTTP 200 with a JSON payload that describes the result. The following is an example of an applied patch.

{
   "request": {
      "id": "lcc-example123",
      "name": "{connector_name}",
      "offsets": [
         {
         "partition": {
            "name": "tickets"
         },
         "offset": {
            "date_updated": 1618184736
         }
         }
      ],
      "requested_at": "2024-03-28T17:58:45.606796307Z",
      "type": "PATCH"
   },
   "status": {
      "phase": "APPLIED",
      "message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses."
   },
   "previous_offsets": [
      {
         "partition": {
         "name": "brands"
         },
         "offset": {
         "updated_at": 1666023665000
         }
      },
      {
         "partition": {
         "name": "apps"
         },
         "offset": {
         "updated_at": 1713982063000
         }
      }
   ],
   "applied_at": "2024-03-28T17:58:48.079141883Z"
}

Responses include the following information:

The original request, including the time it was made.
The status of the request: applied, pending, or failed.
The time you issued the status request.
The previous offsets. These are the offsets that the connector last updated prior to updating the offsets. Use these to try to restore the state of your connector if a patch update causes your connector to fail or to return a connector to its previous state after rolling back.

JSON payload

The table below offers a description of the unique fields in the JSON payload for managing offsets of the Zendesk Source connector.

Field	Definition	Required/Optional
`created_at`	The UNIX timestamp when the table row was created.	Required
`updated_at`	The UNIX timestamp when the row was last updated.	Required

Quick Start

Use this quick start to get up and running with the Confluent Cloud Zendesk Source connector. The quick start provides the basics of selecting the connector and configuring it to stream events.

Prerequisites

Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
Authorization and credentials to access the Zendesk service URL.
Zendesk API: Support APIs must be enabled for the Zendesk account.
Either the oauth2 or password mechanisms should be enabled for the Zendesk account. For additional information, see Using the API dashboard: Enabling password or token access.
Certain tables, such as custom_roles, can only be accessed if the Zendesk Account is an Enterprise account. For more information, see Custom Agent Roles.
A few Zendesk configuration settings may need to be enabled to ensure export is possible. For example, satisfaction_ratings can only be exported if this option is enabled. For more information, see Support API: Satisfaction Ratings.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

Step 2: Add a connector

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the Zendesk Source connector card.

Step 4: Enter the connector details

Note

Make sure you have all your prerequisites completed.
An asterisk ( * ) designates a required entry.

At the Add Zendesk Source Connector screen, complete the following:

Kafka credentials

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
Note

Freight clusters support only service accounts for Kafka authentication.

Click Continue.

Authentication

Add the Zendesk authentication details:
- Zendesk Service URL: The URL where the connector gets Zendesk source data. For example, https://<sub-domain>.zendesk.com
- Endpoint Authentication type: Choose either basis or bearer for the authentication type. For more information, see OAuth tokens in the Zendesk docs.
Click Continue.

Configuration

Add the following details:
- Select the output record value format (data going to the Kafka topic): AVRO, JSON, JSON_SR (JSON Schema), or PROTOBUF. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). For additional information, see Schema Registry Enabled Environments.
  
  Note
  
  For Schema Registry-based output formats, the connector attempts to deduce the schema based on the source API response returned. The connector registers a new schema for every NULL and NOT NULL value of an optional field in the API response. For this reason, the connector may register schema versions at a much higher rate than expected.
- Zendesk tables: The Zendesk tables the connector exports and writes to Kafka. To balance the load between workers, order the tables by their expected size or throughput requirement. For the list of supported tables, see Supported tables.
- Topic Name Pattern: The pattern to use for the topic name, where the ${entityName} literal is replaced with each entity name. If ${entityName} is not specified, the connector writes all records to a single topic named ZD_${entityName}. A valid topic pattern should follow the regex [a-zA-Z0-9\\.\\-\\_]*(\\$\\{entityName\\})?[a-zA-Z0-9\\.\\-\\_]*.
- Zendesk start time (ISO 8601): Rows updated after the time entered are processed by the connector. The value should be formatted using the ISO 8601 format yyyy-MM-dd'T'HH:mm:SS. If left blank, the default time is set to the time the connector is launched minus one minute.
Show advanced configurations
- Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
Auto-restart policy
- Enable Connector Auto-restart: Control the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. In such cases, you would need to manually restart the connector.
Transforms
- Single Message Transforms: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.
For all property values and definitions, see Configuration Properties.
Click Continue.

Sizing

Based on the number of topic partitions you select, you will be provided with a recommended number of tasks.

To change the number of tasks, use the Range Slider to select the desired number of tasks.
Click Continue.

Review and Launch

Verify the connection details by previewing the running configuration.
After you’ve validated that the properties are configured to your satisfaction, click Launch.

The status for the connector should go from Provisioning to Running.

Step 5: Check for records

Verify that records are being produced at the Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Using the Confluent CLI

Complete the following steps to set up and run the connector using the Confluent CLI.

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties. See Configuration Properties for additional configuration property values and descriptions.

{
  "connector.class": "ZendeskSource",
  "name": "ZendeskSource_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "zendesk.url": "https://<sub-domain>.zendesk.com",
  "zendesk.tables": "tickets, groups, users",
  "zendesk.user": "<username>",
  "zendesk.password": "*********************************",
  "output.data.format": "AVRO",
  "tasks.max": "1",
}

Note the following property definitions:

"connector.class": Identifies the connector plugin name.
"name": Sets a name for your new connector.

"kafka.auth.mode": Identifies the connector authentication mode you want to use. There are two options: SERVICE_ACCOUNT or KAFKA_API_KEY (the default). To use an API key and secret, specify the configuration properties kafka.api.key and kafka.api.secret, as shown in the example configuration (above). To use a service account, specify the Resource ID in the property kafka.service.account.id=<service-account-resource-ID>. To list the available service account resource IDs, use the following command:
```
confluent iam service-account list
```
For example:
```
confluent iam service-account list

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2
```

Enter the Zendesk connection details.
- "zendesk.url": The URL where the connector gets Zendesk source data. For example, https://<sub-domain>.zendesk.com``.
- "zendesk.tables": A comma-separated list of Zendesk tables the connector exports and writes to Kafka. To balance the load between workers, order the tables by their expected size or throughput requirement. For the list of supported tables, see Supported tables.
Enter the authentication details. The example shows the default basic authentication properties "zendesk.user" and "zendesk.password". You can use the properties "zendesk.auth.type": "bearer" and "bearer.token": "<token-string>" to authenticate. This is a single string that is sent in the HTTP Authorization header.
"output.data.format": Enter an output data format (data going to the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON (schemaless). Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.

Note

For Schema Registry-based output formats, the connector attempts to deduce the schema based on the source API response returned. The connector registers a new schema for every NULL and NOT NULL value of an optional field in the API response. For this reason, the connector may register schema versions at a much higher rate than expected.
"tasks.max": Enter the number of tasks to use with the connector. Only one task per connector is supported.
Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.

See Configuration Properties for all property values and descriptions.

Step 4: Load the properties file and create the connector

Enter the following command to load the configuration and start the connector:

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file zendesk-source-config.json

Example output:

Created connector ZendeskSource_0 lcc-do6vzd

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name         | Status  | Type  | Trace
+------------+--------------------------+---------+--------+-------+
lcc-do6vzd   | ZendeskSource_0          | RUNNING | source |       |

Step 6: Check for records.

Verify that records are being produced at the Kafka topic.

For more information and examples to use with the Confluent Cloud API for Connect, see the Confluent Cloud API for Connect Usage Examples section.

Configuration Properties

Use the following configuration properties with the fully-managed connector. For self-managed connector property definitions and other details, see the connector docs in Self-managed connectors for Confluent Platform.

Note

These are properties for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Zendesk Source Connector for Confluent Platform.

How should we connect to your data?

name

Sets a name for your connector.

Type: string
Valid Values: A string at most 64 characters long
Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode.

Type: string
Default: KAFKA_API_KEY
Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

Type: string
Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

How do you want to name your topic(s)?

topic.name.pattern

The pattern to use for the topic name, where the ${entityName} literal will be replaced with each entity name. If ${entityName} is not specified all the records will be written to a single topic. A valid topic pattern should follow the regex [a-zA-Z0-9.-_]*(${entityName})?[a-zA-Z0-9.-_]*

Type: string
Default: ZD_${entityName}
Valid Values: Must match the regex [a-zA-Z0-9\.\-\_]*(\$\{entityName\})?[a-zA-Z0-9\.\-\_]*
Importance: high

Schema Config

schema.context.name

Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.

Type: string
Default: default
Importance: medium

How should we connect to Zendesk?

zendesk.url

The zendesk service url that connector will connect to.

Type: string
Importance: high

zendesk.auth.type

Authentication type of the endpoint. Valid values are basic and bearer

Type: string
Default: basic
Valid Values: basic, bearer
Importance: high

zendesk.tables

The Zendesk tables that are to be exported and written to Kafka. To avail a reasonable load balance between workers, the tables could be ordered by their expected size or throughput.

Type: list
Importance: high

zendesk.since

Rows updated after this time will be processed by the connector. If left blank, the default time will be set to the time this connector is launched minus 1 minute. The value should be formatted as ISO 8601. Example format yyyy-MM-dd’T’HH:mm:SS.

Type: string
Importance: medium

Authorization: Basic

zendesk.user

The username to be used with an endpoint requiring authentication.

Type: string
Importance: high

zendesk.password

The password to be used with an endpoint requiring authentication.

Type: password
Importance: high

Authorization: Bearer

bearer.token

The bearer authentication token to be used when auth.type=bearer. The supplied token will be used as the value of Authorization header in HTTP requests.

Type: password
Importance: high

Connection details

max.batch.size

The maximum number of records that should be returned and written to Kafka at one time.

Type: int
Default: 100
Importance: low

max.in.flight.requests

The maximum number of requests that may be in-flight at once.

Type: int
Default: 10
Importance: low

max.poll.interval.ms

The time in milliseconds between requests to fetch changed or updated entities.

Type: long
Default: 3000 (3 seconds)
Importance: low

request.interval.ms

The time in milliseconds to wait before checking for updated records.

Type: long
Default: 15000 (15 seconds)
Importance: low

max.retries

The maximum number of times to retry on errors before failing the task.

Type: int
Default: 10
Importance: low

retry.backoff.ms

The time in milliseconds to wait following an error before a retry attempt is made.

Type: long
Default: 3000 (3 seconds)
Importance: low

Output messages

output.data.format

Sets the output Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF

Type: string
Default: JSON
Importance: high

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

Additional Configs

header.converter

The converter class for the headers. This is used to serialize and deserialize the headers of the messages.

Type: string
Importance: low

producer.override.compression.type

The compression type for all data generated by the producer. Valid values are none, gzip, snappy, lz4, and zstd.

Type: string
Importance: low

value.converter.allow.optional.map.keys

Allow optional string map key when converting from Connect Schema to Avro Schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

Type: boolean
Importance: low

value.converter.connect.meta.data

Allow the Connect converter to add its metadata to the output schema. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.avro.schema.support

Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.

Type: boolean
Importance: low

value.converter.enhanced.protobuf.schema.support

Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.flatten.unions

Whether to flatten unions (oneofs). Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.index.for.unions

Whether to generate an index suffix for unions. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.generate.struct.for.nulls

Whether to generate a struct variable for null values. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.int.for.enums

Whether to represent enums as integers. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.latest.compatibility.strict

Verify latest subject version is backward compatible when use.latest.version is true.

Type: boolean
Importance: low

value.converter.object.additional.properties

Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.optional.for.nullables

Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.use.latest.version

Use latest version of schema in subject for serialization when auto.register.schemas is false.

Type: boolean
Importance: low

value.converter.use.optional.for.nonrequired

Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.

Type: boolean
Importance: low

value.converter.wrapper.for.nullables

Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.wrapper.for.raw.primitives

Whether a wrapper message should be interpreted as a raw primitive at root level. Applicable for Protobuf Converters.

Type: boolean
Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

Type: string
Default: TopicNameStrategy
Importance: low

value.converter.decimal.format

Specify the JSON/JSON_SR serialization format for Connect DECIMAL logical type values with two allowed literals:

BASE64 to serialize DECIMAL logical types as base64 encoded binary data and

NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.

Type: string
Default: BASE64
Importance: low

value.converter.flatten.singleton.unions

Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.reference.subject.name.strategy

Set the subject reference name strategy for value. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. Note that the subject reference name strategy can be selected only for PROTOBUF format with the default strategy being DefaultReferenceSubjectNameStrategy.

Type: string
Default: DefaultReferenceSubjectNameStrategy
Importance: low

value.converter.value.subject.name.strategy

Determines how to construct the subject name under which the value schema is registered with Schema Registry.

Type: string
Default: TopicNameStrategy
Importance: low

Egress allowlist

connector.egress.whitelist

Type: string
Default: “”
Importance: high

Next Steps

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud for Apache Flink, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.