Databricks Delta Lake Sink Connector for Confluent Cloud¶

The fully-managed Databricks Delta Lake Sink connector for Confluent Cloud periodically polls data from Apache Kafka® and copies the data into an Amazon S3 staging bucket, and then commits these records to a Databricks Delta Lake instance.

Note the following considerations:

The connector is available only on Amazon Web Services (AWS).
The connector appends data only.
The Amazon S3 bucket, the Delta Lake instance, and the Kafka cluster must be in the same region.
The connector adds a field named partition. Your Delta Lake table must include a field named partition using type INT (partition INT).

Refer to the Cloud connector limitations for additional information.

Note

This Quick Start is for the fully-managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see Databricks Delta Lake Sink 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 Databricks Delta Lake Sink connector provides the following features:

Supports multiple tasks: The connector supports running one or more tasks. Refer to the limitations for additional information.
Supported data formats: The connector supports input data from Kafka topics in Avro, JSON Schema, and Protobuf formats. You must enable Schema Registry to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
Automatically creates tables: If you do not provide a table name, the connector can create a table using the originating Kafka topic name (that is–the configuration property defaults to ${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.

Refer to the Cloud connector limitations for additional information.

See Configuration Properties for configuration property values and descriptions.

Quick Start¶

Important

Be sure to review and complete the tasks in Set up Databricks Delta Lake (AWS) Sink Connector for Confluent Cloud before configuring the connector.

Use this quick start to get up and running with the Confluent Cloud Databricks Delta Lake Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream data.

Prerequisites

Authorized access to a Confluent Cloud cluster on AWS.
All Databricks Delta Lake and AWS CloudFormation procedures completed. See Set up Databricks Delta Lake (AWS) Sink Connector for Confluent Cloud.
The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
You must enable Schema Registry to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
An AWS account configured with Access Keys. You use these access keys when setting up the connector.
An AWS User Account IAM Policy configured for bucket access. Note that if you have an access policy (or policies) for Amazon S3 storage that includes a condition for NAT IPs, you must update your policy to also include Databricks VPC IDs for these S3 gateway endpoints. Resources to help you make this change and an example of the S3 policy can be found in the Databricks Community page.

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 Databricks Delta Lake 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 Databricks Delta Lake Sink Connector screen, complete the following:

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.

Enter the following Databricks Delta Lake connection details. These fields use information you get from Databricks and AWS. See the Databricks Delta Lake setup procedure.
- Delta Lake Host Name: The host name used to connect to Delta Lake. For example: dbc-acdefg123456-hi78.cloud.databricks.com.
- Delta Lake HTTP Path: The HTTP path used to connect to Delta Lake. For example: sql/protocolv1/a/123456789/1234-5678-abcd9efg.
- Delta Lake Token: The personal access token used to authenticate the user when connecting to Delta Lake using JDBC.
- Delta Lake Catalog: The destination catalog under which the destination database and tables are located.
- Delta Lake Database: The destination database under which the destination tables are located.
In the S3 Staging Bucket Name field, enter the S3 staging bucket where files get written to from Kafka and subsequently copied into the Databricks Delta Lake table.
Visit your https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html#access-keys-and-secret-access-keys and select a Key ID to provide S3 access to this Confluent connector. Enter the Access Key ID in the In the Staging S3 Access Key ID field.
In the Staging S3 Secret Access Key field, enter your S3 secret access key.
Click Continue.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
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?.
- Delta Lake Table Format: A format string for the destination table name, which may contain ${topic} as a placeholder for the originating topic name. For example, to create a table named kafka-orders based on a Kafka topic named orders, you would enter kafka-${topic} in this field. Note that you must use the ${topic} placeholder if you have multiple originating topics.
- Input Kafka record key format Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING. A valid schema must be available in Schema Registry to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
- Delta Lake Topic2Table Map: Map of topics to tables (optional). Create mapping as comma-separated tuples. For example: <topic-1>:<table-1>,<topic-2>:<table-2>,.... If you use this property, the connector ignores any string entered for Delta Lake Table Format.
- Delta Lake Table Auto Create: Specifies whether to create the destination table based on record schema if it does not exist.
- Delta Lake Tables Location: The underlying location where the data in the Delta Lake tables is stored. If you set s3://<your-s3-bucket>/tmp/, data is stored in the tmp directory. Be sure the AWS IAM role used for the Databricks Delta Lake instance is permitted to write records to the bucket directory and that the directory exists (in this case, tmp).
- Delta Lake Table2Partition Map: Map of tables to partition fields (optional). Create mapping as comma-separated tuples. For example: <table-1>:<partition-1>,<table-2>:<partition-2>,... Note that you can specify multiple partitions per table. Be sure to add a separate tuple for each partition. For example: <table-1>:<partition-1>, <table-1>:<partition-2>), <table-2>:<partition-3>".
- Flush Interval (ms): The time interval in milliseconds to periodically invoke file commits. This configuration ensures that file commits are invoked at every configured interval. Defaults to 300,000 milliseconds (5 minutes).
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.
Consumer configuration
- Max poll interval(ms): Set 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: Set 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 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.

Step 5: Check the S3 bucket¶

Check that records are populating the staging Amazon S3 bucket and then populating the Databricks Delta Lake table.

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. See View Connector Dead Letter Queue Errors in Confluent Cloud for details.

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 required and optional connector properties.

{
  "name": "DatabricksDeltaLakeSinkConnector_0",
  "config": {
    "topics": "clickstreams, pageviews",
    "input.data.format": "AVRO",
    "connector.class": "DatabricksDeltaLakeSink",
    "name": "DatabricksDeltaLakeSinkConnector_0",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "****************",
    "kafka.api.secret": "**************************************************",
    "delta.lake.host.name": "dbc-e12345cd-e12345ed.cloud.databricks.com",
    "delta.lake.http.path": "sql/protocolv1/o/1234567891811460/0000-01234-str6jlpz",
    "delta.lake.token": "************************************",
    "delta.lake.topic2table.map": "pageviews:pageviews,clickstreams:clickstreams-test",
    "delta.lake.table.auto.create": "false",
    "staging.s3.access.key.id": "********************",
    "staging.s3.secret.access.key": "****************************************",
    "staging.bucket.name": "databricks0",
    "flush.interval.ms": "300000",
    "tasks.max": "1"
  }
}

Note the following required property definitions:

"name": Sets a name for your new connector.
"connector.class": Identifies the connector plugin name.
"topics": Enter the topic name or a comma-separated list of topic names.

"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
```
"input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR, and PROTOBUF. You must have Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
"delta.lake...."”: See the Databricks Delta Lake setup procedure for where you can get this information. See Configuration Properties for additional property values and descriptions.
"staging....": These properties use information you get from Databricks and AWS. See the Databricks Delta Lake setup procedure.
"flush.interval.ms": The time interval in milliseconds (ms) to periodically invoke file commits. This property ensures the connector invokes file commits at every configured interval. The commit time is adjusted to 00:00 UTC. The commit is performed at the scheduled time, regardless of the last commit time or number of messages. This configuration is useful when you have to commit your data based on current server time, like at the beginning of each hour. The default value used is 300000 ms (5 minutes).
"tasks.max": Enter the maximum number of tasks for the connector to use. Refer to the limitations for additional information.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.

See Configuration Properties for configuration 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 databricks-delta-lake-sink-config.json

Example output:

Created connector DatabricksDeltaLakeSinkConnector_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   | DatabricksDeltaLakeSinkConnector_0 | RUNNING | sink

Step 6: Check the S3 bucket.¶

Check that records are populating the staging Amazon S3 bucket and then populating the Databricks Delta Lake table.

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. See View Connector Dead Letter Queue Errors in Confluent Cloud for details.

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.

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

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

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, or PROTOBUF. 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

input.key.format

Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING. 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
Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, 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.

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 should we connect to your Databricks Delta Lake?¶

delta.lake.host.name

The host name used to connect to Delta Lake.

Type: string
Importance: high

delta.lake.http.path

The HTTP path used to connect to Delta Lake.

Type: string
Importance: high

delta.lake.token

The personal access token used to authenticate the user when connecting to Delta Lake via JDBC.

Type: password
Importance: high

delta.lake.catalog

The destination catalog under which the destination database and tables are located.

Type: string
Default: “”
Importance: low

delta.lake.database

The destination database under which the destination tables are located.

Type: string
Default: default
Importance: low

delta.lake.table.format

A format string for the destination table name, which may contain ‘${topic}’ as a placeholder for the originating topic name. For example, kafka_${topic} for the topic ‘orders’ will map to the table name ‘kafka_orders’.

Type: string
Default: ${topic}
Importance: medium

delta.lake.topic2table.map

Map of topics to tables (optional). Format: comma-seperated tuples, e.g. <topic-1>:<table-1>,<topic-2>:<table-2>,…

Type: string
Default: “”
Importance: low

delta.lake.table.auto.create

Whether to automatically create the destination table based on record schema if it does not exist.

Type: boolean
Default: false
Importance: medium

delta.lake.tables.location

The underlying location where the data in the Delta Lake table(s) is stored. If you set s3://<your-s3-bucket>/tmp/, Delta Lake data will be stored under s3://<your-s3-bucket>/tmp/. Make sure the AWS IAM for the Databricks Delta Lake instance has the permision to write records to the specified directory, and the specified directory exists (e.g. tmp)

Type: string
Default: “”
Importance: medium

delta.lake.table2partition.map

Map of tables to partition fields (optional). Format: comma-separated tuples. For example: <table-1>:<partition-1>,<table-2>:<partition-2>,… Note that you can specify multiple partitions per table. Be sure to add a separate tuple for each partition. For example: <table-1>:<partition-1>, <table-1>:<partition-2>), <table-2>:<partition-3>

Type: string
Default: “”
Importance: low

Amazon S3 details¶

staging.s3.access.key.id

Type: password
Importance: high

staging.s3.secret.access.key

Type: password
Importance: high

flush.interval.ms

The time interval in milliseconds to periodically invoke file commits. This configuration ensures that file commits are invoked at every configured interval. Time of commit will be adjusted to 00:00 of selected timezone. The commit will be performed at the scheduled time, regardless of the previous commit time or number of messages. This configuration is useful when you have to commit your data based on current server time, for example at the beginning of every hour.

Type: long
Default: 300000 (5 minutes)
Importance: medium

staging.bucket.name

The S3 staging bucket where files get written to from Kafka and subsequently copied into the Databricks Delta Lake table. Must be in the same region as your Confluent Cloud cluster.

Type: string
Importance: high

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 or the “latest” offset (the default). 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

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.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.subject.name.strategy

How to construct the subject name for key schema registration.

Type: string
Default: TopicNameStrategy
Importance: low

key.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 Key Converter.

Type: boolean
Default: true
Importance: low

key.converter.schemas.enable

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

Type: boolean
Default: false
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.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

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 ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.