# Google Cloud Functions Sink Connector [End of Life] for Confluent Cloud #### IMPORTANT This connector reached its end of life (EOL) on March 31, 2026. Confluent recommends migrating to [Google Cloud Functions Gen 2 Sink connector](cc-google-cloud-functions-gen2-sink.md#cc-google-cloud-functions-gen2-sink-legacy-v2-migration). For more information, see [Deprecated and end of life connectors](overview.md#deprecated-connectors). The fully managed Google Cloud Functions Sink connector for Confluent Cloud integrates Apache Kafka® with Google Cloud Functions. For basic information about functions, see the [Google Cloud Functions Documentation](https://cloud.google.com/functions/docs/quickstarts). The connector consumes records from Kafka topics and executes a Google Cloud Function. Each request sent to Google Cloud Functions can contain up to the `max.batch.size` number of records. #### NOTE This is a Quick Start for the fully managed cloud connector. If you are installing the connector locally for Confluent Platform, see [Google Functions Sink Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/gcp-functions/current/). ## Features The Google Cloud Functions Sink connector provides the following features: * Results from Google Cloud Functions are stored in the following topics: - `success-` - `error-` * Input data formats supported are Bytes, AVRO, JSON_SR (JSON Schema), JSON (Schemaless) and PROTOBUF. If no schema is defined, values are encoded as plain strings. For example, `"name": "Kimberley Human"` is encoded as `name=Kimberley Human`. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section. ## Limitations Be sure to review the following information. * For connector limitations, see [Google Functions Sink Connector](limits.md#google-functions-sink-limits) limitations. * If you plan to use one or more Single Message Transformations (SMTs), see [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations). ## Quick Start Use this quick start to get up and running with the Confluent Cloud Google Cloud Functions Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events to a target Google Cloud Function. Prerequisites : - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Google Cloud. - Access to a Google Cloud function. For basic information about functions, see the [Google Cloud Functions Documentation](https://cloud.google.com/functions/docs/quickstarts). - A Google Cloud [service account](https://cloud.google.com/iam/docs/creating-managing-service-accounts). You download service account [credentials as a JSON file](https://cloud.google.com/iam/docs/creating-managing-service-account-keys). This credentials file is uploaded when you set up the connector configuration properties - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). - The target Google Cloud function and the Kafka cluster should be in the same region.
- Kafka cluster credentials. The following lists the different ways you can provide credentials. - Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID. - Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). 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](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *or* you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.
- The following `connector-service` account role must be enabled in your project: ![Google Project Connector Role](images/ccloud-google-functions-sink-iam-role.png) - The **Trigger type** must be set to `HTTP`. Select **Require authentication**. ![HTTP Trigger Type](images/ccloud-google-functions-sink-http-trigger.png) ### 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](../get-started/index.md#cloud-create-kafka-cluster). #### 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 **Google Cloud Functions Sink** connector card. ![Google Cloud Functions Sink Connector Card](images/ccloud-google-functions-sink-icon.png) #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-google-functions-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Google Cloud Functions 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 1. 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](service-account.md#s3-cloud-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. 2. Click **Continue**. ### Authentication 1. Configure the authentication properties: - **Cloud Functions project ID**: In the **Cloud Functions project ID** field, enter your Google Cloud project ID where the functions is deployed. - **Google Cloud function name**: Enter the Google Cloud function to invoke in the **Google Cloud function name** field. For basic information about Google Cloud Functions, see [Your First Function](https://cloud.google.com/functions/docs/first-python#creating_a_gcp_project_using_cloud_sdk). - **GCP credentials file**: Upload your Google Cloud credentials JSON file created as part of the [prerequisites](#cc-google-functions-sink-prereqs). 2. Click **Continue**. ### Configuration - **Input Kafka record value format**: Select the input Kafka record value format (data coming from the Kafka topic). Valid entires are AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format. ### **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?](../sr/faqs-cc.md#faq-schema-contexts). - **Batch size**: The maximum number of Kafka records to combine in a single function invocation. To disable batching of records, set this value to 1. - **Max pending requests**: The maximum number of pending requests that can be made to Google Cloud Functions concurrently. - **Request timeout (ms)**: Sets the input Kafka record key format. Valid entries are AVRO, JSON_SR, PROTOBUF, STRING. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format. - **Retry timeout (ms)**: The maximum time, in milliseconds, that the connector attempts to request Google Cloud Functions before timing out (socket timeout). - **Behavior on error**: The connector’s behavior if the called GCP function returns an error. **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. **Additional Configs** - **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`. - **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers. - **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 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 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. - **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers. - **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`. **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](single-message-transforms.md#cc-single-message-transforms-ui). For more information about unsupported SMTs, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). **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](offsets.md#connect-custom-offsets). See [Configuration Properties](#cc-gcs-functions-sink-config-properties) for all property values and definitions. - Click **Continue**. ### Sizing Based on the number of topic partitions you select, you will be provided with a recommended number of tasks. 1. To change the number of recommended tasks, enter the number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in the **Tasks** field. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details. 2. Click **Launch**. The status for the connector should go from **Provisioning** to **Running**. #### Step 5: Check for records Verify that records are being produced. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) 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](#cc-google-functions-sink-prereqs) completed. #### Step 1: List the available connectors Enter the following command to list available connectors: ```none confluent connect plugin list ``` #### Step 2: List the connector configuration properties Enter the following command to show the connector configuration properties: ```none confluent connect plugin describe ``` 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. ```none { "connector.class": "GoogleCloudFunctionsSink", "name": "GoogleCloudFunctionsSinkConnector_0", "topics": "pageviews_proto", "input.data.format": "PROTOBUF", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "****************", "kafka.api.secret": "****************************************************************", "function.name": "dev-test", "project.id": "connect-2021", "gcf.credentials.json": "*", "tasks.max": "1" } ``` Note the following property definitions: * `"connector.class"`: Identifies the connector plugin name. * `"name"`: Sets a name for your new connector. * `"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**, **JSON**, or **BYTES**. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"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](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=`. To list the available service account resource IDs, use the following command: ```bash confluent iam service-account list ``` For example: ```bash 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 ``` * `"function.name"`: The name of your predefined Google Cloud function. * `"project.id"`: Your GCP project ID. * `"gcf.credentials.json"`: This contains the contents of the downloaded JSON file. See [Formatting Google Cloud credentials](#cc-gcf-json-config-format) for details about how to format and use the contents of the downloaded credentials file. *Optional:* * `"max.batch.size"`: The maximum number of records to combine when invoking a single Google Cloud function. Defaults to `1` (batching disabled). Accepts values from `1` to `1000`. * `"max.pending.requests"`: The maximum number of pending requests that can be made to Google Cloud functions concurrently. Defaults to `1`. * `"request.timeout"`: The maximum time in milliseconds that the connector will attempt a request to Google Cloud Functions before timing out (i.e., socket timeout). Defaults to `300000` ms (5 minutes). * `"retry.timeout"`: The total amount of time, in milliseconds (ms), that the connector will exponentially backoff and retry failed requests (i.e., throttling). Response codes that are retried are `HTTP 401 Unauthorized` and `HTTP 500 Internal Server Error`. Defaults to `300000` ms (5 minutes). Enter `-1` to configure this property for indefinite retries. **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. For a list of SMTs that are not supported with this connector, see [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms). See [Configuration Properties](#cc-gcs-functions-sink-config-properties) for all property values and definitions. ##### Formatting Google Cloud credentials The contents of the downloaded credentials file must be converted to string format before it can be used in the connector configuration. 1. Convert the JSON file contents into string format. 2. Add the escape character `\` before all `\n` entries in the Private Key section so that each section begins with `\\n` (see the highlighted lines below). The example below has been formatted so that the `\\n` entries are easier to see. Most of the credentials key has been omitted. ```json { "connector.class": "GoogleCloudFunctionsSink", "name": "GoogleCloudFunctionsSinkConnector_0", "kafka.api.key": "", "kafka.api.secret": "", "topics": "", "data.format": "AVRO", "function.name": "dev-test", "project.id": "connect-2021", "gcf.credentials.json": "{\"type\":\"service_account\",\"project_id\":\"connect- 1234567\",\"private_key_id\":\"omitted\", \"private_key\":\"-----BEGIN PRIVATE KEY----- \\nMIIEvAIBADANBgkqhkiG9w0BA \\n6MhBA9TIXB4dPiYYNOYwbfy0Lki8zGn7T6wovGS5pzsIh \\nOAQ8oRolFp\rdwc2cC5wyZ2+E+bhwn \\nPdCTW+oZoodY\\nOGB18cCKn5mJRzpiYsb5eGv2fN\/J \\n...rest of key omitted... \\n-----END PRIVATE KEY-----\\n\", \"client_email\":\"pub-sub@connect-123456789.iam.gserviceaccount.com\", \"client_id\":\"123456789\",\"auth_uri\":\"https:\/\/accounts.google.com\/o\/oauth2\/ auth\",\"token_uri\":\"https:\/\/oauth2.googleapis.com\/ token\",\"auth_provider_x509_cert_url\":\"https:\/\/ www.googleapis.com\/oauth2\/v1\/ certs\",\"client_x509_cert_url\":\"https:\/\/www.googleapis.com\/ robot\/v1\/metadata\/x509\/pub-sub%40connect- 123456789.iam.gserviceaccount.com\"}", "tasks.max": "1" } ``` 3. Add all the converted string content to the `"gcf.credentials.json"` section of your configuration file as shown in the example above. #### Step 4: Load the properties file and create the connector Enter the following command to load the configuration and start the connector: ```none confluent connect cluster create --config-file .json ``` For example: ```none confluent connect cluster create --config-file google-functions-sink-config.json ``` Example output: ```none Created connector GoogleCloudFunctionsSinkConnector_0 lcc-ix4dl ``` #### Step 5: Check the connector status Enter the following command to check the connector status: ```none confluent connect cluster list ``` Example output: ```none ID | Name | Status | Type +-----------+-------------------------------------+---------+------+ lcc-ix4dl | GoogleCloudFunctionsSinkConnector_0 | RUNNING | sink ``` #### Step 6: Check for records. Verify that records are being produced. For more information and examples to use with the Confluent Cloud API for Connect, see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) 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](/platform/current/connect/kafka_connectors.html). ### Which topics do you want to get data from? `topics` : Identifies the topic name or a comma-separated list of topic names.
* Type: list * 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 ### Input messages `input.data.format` : Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON or BYTES. 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 * 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 your function? `function.name` : The Google Cloud function to invoke.
* Type: string * Importance: high `project.id` : The Google Cloud Project ID where the function is deployed.
* Type: string * Importance: high ### GCP credentials `gcf.credentials.json` : GCP service account JSON file with invoker permission for Functions.
* Type: password * Importance: high ### Cloud Function details `max.batch.size` : The maximum number of Kafka records to combine in a single function invocation. To disable batching of records, set this value to 1.
* Type: int * Default: 1 * Valid Values: [1,…] * Importance: low `max.pending.requests` : The maximum number of pending requests that can be made to Google Cloud Functions concurrently.
* Type: int * Default: 1 * Valid Values: [1,…,128] * Importance: low `request.timeout.ms` : The maximum time, in milliseconds, that the connector attempts to request Google Cloud Functions before timing out (socket timeout).
* Type: int * Default: 300000 (5 minutes) * Valid Values: [0,…] * Importance: low `retry.timeout.ms` : The total amount of time, in milliseconds, that the connector will exponentially backoff and retry failed requests i.e on throttling. Response codes that are retried are HTTP 401 Unauthorized and HTTP 500 Internal Server Error. A value of -1 indicates indefinite retrying.
* Type: int * Default: 300000 (5 minutes) * Valid Values: [-1,…] * Importance: low ### How should we handle errors? `behavior.on.error` : The connector’s behavior if the called GCP function returns an error. Valid options are ‘log’ and ‘fail’. ‘log’ logs the error message and continues processing and ‘fail’ stops the connector in case of an error.
* Type: string * Default: log * 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 ### 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 `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.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.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 `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.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.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 ## 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](/platform/current/tutorials/examples/cloud-etl/docs/index.html). This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud. [![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)