Marketo Sink Connector for Confluent Cloud

The fully managed Marketo Sink connector for Confluent Cloud streams records from an Apache Kafka® topic into Adobe Marketo Engage. Use it to upsert leads, log custom activities, upsert custom objects, and manage static list membership without building a custom integration.

Note

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 Marketo Sink connector supports the following features:

Multiple Marketo operations: You can configure a single connector to perform one or more of the following operations: upsert a lead, add a custom activity to a lead, upsert a custom object, add a lead to a static list, and remove a lead from a static list. Each record specifies which operation it targets, so one connector instance can route records to more than one operation.
Dynamic resource routing: A single connector instance can route records to different resources for the same operation. For example, one connector can add or remove leads from multiple static lists based on the resource field in each record.
Rate-limit aware batching: The marketo.batch.size and marketo.flush.interval.ms properties control how the connector batches and flushes records, helping it stay within the Marketo daily API quota and rate limits.
At least once delivery: The connector guarantees that records from the Kafka topic are delivered to Marketo at least once.
Automatic retries: The connector retries transient errors, such as rate limiting and server errors, using exponential backoff.
Dead letter queue (DLQ): If you configure a DLQ, the connector routes records that fail Marketo validation, such as an invalid email address or an unknown field, to the DLQ.
Input data formats: The connector supports Avro, JSON_SR (JSON Schema), Protobuf, and schemaless JSON input data formats. To use a Schema Registry-based format, you must enable Schema Registry.

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 Marketo Sink Connector limitations.
If you plan to use one or more Single Message Transformations (SMTs), see SMT Limitations.

Record Schema

Each Marketo operation expects a specific record shape on the source Kafka topic. Every record must include an operation field naming one of the configured Marketo operations, and, for operations that target a specific resource, a resource field. The record payload for the operation goes in the data field. For more information about the fields each Marketo operation supports, see the Marketo developer documentation.

Upsert a lead (upsert_lead)

{
  "operation": "upsert_lead",
  "data": {
    "lookupField": "email",
    "email": "jane.doe@example.com",
    "firstName": "Jane",
    "postalCode": "94105"
  }
}

Add a custom activity to a lead (add_custom_activity_to_lead)

{
  "operation": "add_custom_activity_to_lead",
  "data": {
    "leadId": 1001,
    "activityDate": "2026-09-26T06:56:35+00:00",
    "activityTypeId": 1001,
    "primaryAttributeValue": "Game Giveaway",
    "attributes": [
      {
        "apiName": "URL",
        "value": "https://example.com/game-giveaway"
      }
    ]
  }
}

Upsert a custom object (upsert_custom_object)

The resource field is the API name of the custom object.

{
  "operation": "upsert_custom_object",
  "resource": "car_object_api_name",
  "data": {
    "dedupeBy": "dedupeFields",
    "vin": "19UYA31581L000000",
    "make": "BMW",
    "model": "3-Series 330i",
    "year": 2003
  }
}

Add a lead to a static list (add_to_static_list)

The resource field is the list ID of the static list.

{
  "operation": "add_to_static_list",
  "resource": "1234",
  "data": {
    "id": "54333"
  }
}

Remove a lead from a static list (remove_from_static_list)

The resource field is the list ID of the static list.

{
  "operation": "remove_from_static_list",
  "resource": "1234",
  "data": {
    "id": "54333"
  }
}

Quick Start

Use this quick start to get up and running with the Confluent Cloud Marketo Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream records from an Kafka topic to Marketo.

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.
A Marketo instance with API access enabled, along with your Marketo Munchkin account ID.
A Marketo OAuth 2.0 client ID and client secret from a custom service that you create in Admin > Integration > LaunchPoint in your Marketo instance. Assign the custom service to an API-only user whose role has API permissions for the operations you plan to use.
Awareness of your Marketo subscription’s daily API quota and rate limits, because the connector must operate within them. For more information, see Marketo REST API integration best practices.
To use a Schema Registry-based format, such as Avro, JSON_SR, or Protobuf, you must enable Schema Registry.
At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink connector.

Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing service account resource ID.
- Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

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 Marketo Sink connector card.

Step 4: Enter the connector details

Note

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

At the Add Marketo Sink Connector screen, complete the following:

Topic selection

If you’ve already populated your Kafka topics, select the topics you want to connect from the Topics list.

To create a new topic, click +Add new topic.

Kafka access

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

Configure the authentication properties:
- Marketo Munchkin ID: The Marketo Munchkin ID, used to construct the Marketo REST API base URL (https://<munchkin-id>.mktorest.com), for example, 123-ABC-456.
- Marketo Client ID: The OAuth 2.0 client ID for Marketo API authentication.
- Marketo Client Secret: The OAuth 2.0 client secret for Marketo API authentication.
- Marketo Operation: Comma-separated list of Marketo operations this task supports. Each record must carry an operation field naming one of the configured operations, and the connector routes it accordingly. Valid entries are:
  - upsert_lead: Creates or updates a lead.
  - add_custom_activity_to_lead: Adds a custom activity to a lead.
  - upsert_custom_object: Creates or updates a custom object.
  - add_to_static_list: Adds a lead to a static list.
  - remove_from_static_list: Removes a lead from a static list.
  For a record example for each operation, see Record Schema.
Click Continue.

Configuration

Note

Configuration properties that are not shown in the Cloud Console use the default values. For all property values and definitions, see Configuration Properties.

Input Kafka record value format: Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. BYTES is not supported since this connector cannot extract fields from a raw byte value.

Note

You must have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, or PROTOBUF.

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?.
Max Retry Attempts: Maximum number of retry attempts for transient errors (rate limits, server errors, or token expiry). Default: 5. The minimum value is 1 and the maximum is 20.
Retry Backoff (ms): Initial backoff duration in milliseconds for exponential retry. Subsequent retries double this value with jitter. Default: 30000. The minimum value is 1000.

Additional Configs

Value Converter Replace Null With Default: Specifies whether to replace fields that have a default value and that are null to the default value. When set to true, the connector uses the default value; otherwise, it uses null. Applies to the JSON converter.
Value Converter Schema ID Deserializer: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
Value Converter Reference Subject Name Strategy: Sets the subject reference name strategy for values. Valid entries are DefaultReferenceSubjectNameStrategy or QualifiedReferenceSubjectNameStrategy. You can use this strategy only with PROTOBUF format; the default strategy is DefaultReferenceSubjectNameStrategy.
Schema ID For Value Converter: Sets the schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Value Converter Schemas Enable: Includes schema within each of the serialized values. Input messages must contain schema and payload fields and must not contain additional fields. For plain JSON data, set this to false. Applies to the JSON converter.
Errors Tolerance: Use this property to configure the connector’s error handling behavior.

Warning

Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to all, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to none, the connector task fails on errant records.
Value Converter Ignore Default For Nullables: When set to true, this property ensures that the corresponding record in Kafka is null, instead of showing the default column value. Applies to the AVRO, PROTOBUF, and JSON_SR converters.
Key Converter Schema ID Deserializer: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
Value Converter Decimal Format: Specifies the JSON or 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 DECIMAL logical type values in JSON or JSON_SR as a number representing the decimal value.
Schema GUID For Key Converter: Sets the schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Schema GUID For Value Converter: Sets the schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
Value Converter Connect Meta Data: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
Value Converter Value Subject Name Strategy: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
Key Converter Key Subject Name Strategy: Determines how to construct the subject name for key schema registration.
Schema ID For Key Converter: Sets the schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Auto-restart policy

Enable Connector Auto-restart: Enables 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. If disabled, you must manually restart the connector.

Consumer configuration

Max poll interval(ms): Sets the maximum delay between subsequent consume requests to Kafka. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 300,000 milliseconds (5 minutes).
Max poll records: Sets the maximum number of records to consume from Kafka in a single request. Use this property to improve connector performance in cases when the connector cannot send records to the sink system. The default is 500 records.

Transforms

Single Message Transformations: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.

Processing position

Set offsets: Click Set offsets to define a specific offset for this connector to begin procession data from. For more information on managing offsets, see Manage offsets.

For all property values and definitions, see Configuration Properties

Click Continue.

Sizing

Based on the number of topic partitions you select, Confluent Cloud recommends a number of tasks.

To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
Click Continue.

Review and Launch

Verify the connection details.
Click Launch.

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

Step 5: Check Marketo

Verify that the connector updates leads, custom objects, custom activities, or static list membership in Marketo.

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

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. For more information, see View Connector Dead Letter Queue Errors in Confluent Cloud.

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.

{
  "topics": "marketo-leads",
  "input.data.format": "JSON",
  "connector.class": "MarketoSink",
  "name": "MarketoSink_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "****************",
  "kafka.api.secret": "*************************************************",
  "marketo.munchkin.id": "123-ABC-456",
  "marketo.client.id": "<marketo_client_id>",
  "marketo.client.secret": "<marketo_client_secret>",
  "marketo.operation": "upsert_lead",
  "tasks.max": "1"
}

Note the following property definitions:

"topics": Identifies the topic name or a comma-separated list of topic names.
"input.data.format": Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, or JSON. You must have Confluent Cloud Schema Registry configured if using a schema-based message format such as AVRO, JSON_SR, or PROTOBUF.
"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
```

"marketo.munchkin.id": The Marketo Munchkin ID, used to construct the Marketo REST API base URL. For more information, see the Marketo REST API documentation.
"marketo.client.id" and "marketo.client.secret": The OAuth 2.0 client ID and client secret for Marketo API authentication, created from your Marketo instance’s Admin > Integration > LaunchPoint.
"marketo.operation": A comma-separated list of Marketo operations the connector performs. Valid entries are upsert_lead, add_custom_activity_to_lead, upsert_custom_object, add_to_static_list, and remove_from_static_list. Each record on the source topic must carry an operation field naming one of the configured operations. For more information, see Record Schema.
"tasks.max": Enter the maximum number of tasks for the connector to use. Marketo API rate limits and the daily quota apply across all tasks, so adding tasks doesn’t increase throughput beyond those limits. For more information, see Marketo Sink Connector.

SMTs: For details about adding SMTs using the Confluent CLI, see the Single Message Transformations documentation.

For all property values and definitions, see Configuration Properties.

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 marketo-sink-config.json

Example output:

Created connector MarketoSink_0 lcc-ix4dl

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
+-----------+--------------------+---------+------+
lcc-ix4dl   | MarketoSink_0      | RUNNING | sink

Step 6: Check Marketo

Verify that the connector updates leads, custom objects, custom activities, or static list membership in Marketo.

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

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. For more information, see View Connector Dead Letter Queue Errors in Confluent Cloud.

Configuration Properties

Use the following configuration properties with the fully managed connector.

Which topics do you want to get data from?

topics.regex

A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.

Type: string
Importance: low

topics

Identifies the topic name or a comma-separated list of topic names.

Type: list
Importance: high

errors.deadletterqueue.topic.name

The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: dlq-${connector}
Importance: low

reporter.error.topic.name

The name of the topic to produce records to after each unsuccessful record sink attempt. Disabled by default. Set a topic name to enable error reporting. You can provide ${connector} in the value to use it as a placeholder for the logical cluster ID.

Type: string
Default: “”
Importance: low

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

Input messages

input.data.format

Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF or JSON. BYTES is not supported since this connector cannot extract fields from a raw byte value. 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

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, whenever possible.

Type: string
Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
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 should we connect to Marketo?

marketo.munchkin.id

The Marketo Munchkin ID, used to construct the Marketo REST API base URL (https://<munchkin-id>.mktorest.com), for example, 123-ABC-456.

Type: string
Importance: high

marketo.client.id

The OAuth2 client ID for Marketo API authentication.

Type: string
Importance: high

marketo.client.secret

The OAuth2 client secret for Marketo API authentication.

Type: password
Importance: high

marketo.operation

Comma-separated list of Marketo operations this task supports. Each record must carry an operation field naming one of the configured operations, and the connector routes it accordingly.

Type: list
Importance: high

How should we handle errors?

marketo.retry.max.attempts

Maximum number of retry attempts for transient errors (rate limits, server errors, token expiry). Default: 5. The minimum value is 1 and the maximum is 20.

Type: int
Default: 5
Valid Values: [1,…,20]
Importance: low

marketo.retry.backoff.ms

Initial backoff duration in milliseconds for exponential retry. Subsequent retries double this value with jitter. Default: 30000. The minimum value is 1000.

Type: long
Default: 30000 (30 seconds)
Valid Values: [1000,…]
Importance: low

Consumer configuration

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: low

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

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

Additional Configs

consumer.override.auto.offset.reset

Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset

Type: string
Importance: low

consumer.override.isolation.level

Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level

Type: string
Importance: low

header.converter

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

Type: string
Importance: low

key.converter.use.schema.guid

The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: string
Importance: low

key.converter.use.schema.id

The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: int
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.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and 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.use.schema.guid

The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: string
Importance: low

value.converter.use.schema.id

The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.

Type: int
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

errors.tolerance

Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.

Type: string
Default: all
Importance: low

key.converter.key.schema.id.deserializer

The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.

Type: string
Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
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.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF 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.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

Type: boolean
Default: true
Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

Type: boolean
Default: false
Importance: low

value.converter.value.schema.id.deserializer

The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.

Type: string
Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
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

marketo.flush.interval.ms

Interval in milliseconds at which buffered records are flushed to Marketo. Default: 60000. The minimum value is 1000.

Type: long
Default: 60000 (1 minute)
Valid Values: [1000,…]
Importance: medium

marketo.batch.size

Maximum number of records sent to the Marketo API in a single call. Default: 300. The minimum value is 1 and the maximum is 300.

Type: int
Default: 300
Valid Values: [1,…,300]
Importance: medium

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

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.