Get Started with the ClickHouse Sink Connector for Confluent Cloud¶

The fully-managed ClickHouse Sink connector for Confluent Cloud moves data from an Apache Kafka® topic to the ClickHouse database. It writes data from a topic in Kafka to a table in the specified ClickHouse database.

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 connector offers the following features:

Client-side field level encryption (CSFLE) support: The connector supports CSFLE for sensitive data. For more information about CSFLE setup, see the connector configuration.
Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance.
Database authentication: Uses password authentication.
Input Data Format with or without a Schema: The connector supports input data from Kafka topics in Avro, JSON Schema (JSON_SR), Protobuf, JSON (schemaless), or Bytes format. Schema Registry must be enabled to use a Schema Registry-based format.
Offset management capabilities: The connector supports offset management. For more information, see Manage offsets for Sink Connectors.

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

Quick Start¶

Use this quick start to get up and running with the Confluent Cloud ClickHouse Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream Kafka events to an ClickHouse DB container.

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. For more information, see Install the Confluent CLI.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). For more information, see Schema Registry Enabled Environments.
Access to a ClickHouse database.
If you have a VPC-peered cluster in Confluent Cloud, consider configuring a PrivateLink Connection. between ClickHouse and the VPC. For additional networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

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 ClickHouse Sink connector card.

Step 4: Enter the connector details¶

At the ClickHouse Sink Connector screen, complete the following:

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.

Configure the following:
- Select an Input Kafka record value format: (data coming from the Kafka topic) AVRO, BYTES, JSON, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry to use a schema-based message format.
- Topic to Table Mapping: Enter a comma-separated list that maps Kafka topic names to ClickHouse DB table names (e.g. “topic1=table1, topic2=table2, etc.).
- (Optional) Enable Client-Side Field Level Encryption for data decryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. Select the connector behavior (ERROR or NONE) on data decryption failure. If set to ERROR, the connector fails and writes the encrypted data in the DLQ. If set to NONE, the connector writes the encrypted data in the target system without decryption. For more information on CSFLE setup, see Manage CSFLE for connectors.
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?.
- Select the Input Kafka record key format: Sets the data format for incoming record keys. Valid entries are: Avro, Bytes, JSON, JSON Schema, Protobuf, or String. A valid schema must be available in Schema Registry to use a schema-based message format.
Addtional Configuration

To add an additional configuration, see, Additional Connector Configuration Reference for Confluent Cloud.

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.
Processing position
- Set offsets: To define a specific offset, see Manage offsets.
For all property values and definitions, see Configuration Properties.
Click Continue.

Step 5: Check ClickHouse¶

After the connector is running, verify that new records are populating the ClickHouse database.

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

Using the Confluent CLI¶

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

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors¶

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties¶

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file¶

Create a JSON file that contains the connector configuration properties. The following example shows the required connector properties.

 {
     "connector.class": "ClickHouseSink",
     "name": "<my-connector-name>",
     "schema.context.name": "default",
     "input.data.format": "JSON",
     "input.key.format": "JSON",
     "kafka.auth.mode": "KAFKA_API_KEY",
     "kafka.api.key": "<my-kafka-api-key>",
     "kafka.api.secret": "<my-kafka-api-secret>",
     "topics": "<topic-name>",
     "hostname": "<hostname>",
     "port": "8443",
     "username": "<my-username>",
     "password": "<password>",
     "database": "<database-name>",
     "topic2TableMap": "topic1=table1",
     "tableRefreshInterval": "0",
     "bypassRowBinary": "false",
     "exactlyOnce": "false",
     "errors.tolerance": "none",
     "errors.deadletterqueue.context.headers.enable": "false",
     "tolerateStateMismatch": "false",
     "max.poll.interval.ms": "300000",
     "max.poll.records": "500",
     "tasks.max": "1",
     "value.converter.decimal.format": "BASE64",
     "value.converter.replace.null.with.default": "true",
     "value.converter.reference.subject.name.strategy": "DefaultReferenceSubjectNameStrategy",
     "value.converter.schemas.enable": "false",
     "value.converter.value.subject.name.strategy": "TopicNameStrategy",
     "key.converter.key.subject.name.strategy": "TopicNameStrategy",
     "value.converter.ignore.default.for.nullables": "false",
     "auto.restart.on.user.error": "true"
}

Note the following property definitions:

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

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

   Id     | Resource ID |       Name        |    Description
+---------+-------------+-------------------+-------------------
   123456 | sa-l1r23m   | sa-1              | Service account 1
   789101 | sa-l4d56p   | sa-2              | Service account 2
```
"input.data.format": Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, BYTES, JSON, JSON_SR (JSON Schema), or 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).
"topics": Identifies the topic name or a comma-separated list of topic names.
"hostname": The hostname of the ClickHouse server. Use a hostname address and not a full URL. For example: btnmsdpy5r.us-east-2.aws.clickhouse.cloud. The ClickHouse hostname address must provide a service record (SRV). A standard connection string does not work.
"password": ClickHouse database password.
"database": ClickHouse database name.
"topic2TableMap": Comma-separated list that maps topic names to table names (e.g. "topic1=table1, topic2=table2, etc…").
"tasks.max": The connector supports running multiple tasks.
"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).
"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.

Note

(Optional) To enable CSFLE for data encryption, specify the following properties:

csfle.enabled: Flag to indicate whether the connector honors CSFLE rules.
sr.service.account.id: A Service Account to access the Schema Registry and associated encryption rules or keys with that schema.
csfle.onFailure: Configures the connector behavior (ERROR or NONE) on data decryption failure. If set to ERROR, the connector fails and writes the encrypted data in the DLQ. If set to NONE, the connector writes the encrypted data in the target system without decryption.

Warning

Security Risk: Dead Letter Queue (DLQ) with CSFLE

When using CSFLE with connectors that route failed messages to a Dead Letter Queue (DLQ), be aware that data sent to the DLQ is written in plaintext (unencrypted). This poses a significant security risk as sensitive data that should be encrypted may be exposed in the DLQ.

Do not use DLQ with CSFLE in the current version. If you need error handling for CSFLE-enabled data, use alternative approaches such as:

Setting the connector behavior to ERROR to throw exceptions instead of routing to DLQ
Implementing custom error handling in your applications
Using NONE to pass encrypted data through without decryption

For more information on CSFLE setup, see Manage CSFLE for connectors.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and definitions.

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 clickhouse-sink.json

Example output:

Created connector confluent-clickhouse-sink 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   | confluent-clickhouse-sink  | RUNNING | sink

Step 6: Check ClickHouse¶

After the connector is running, verify that records are populating your ClickHouse database.

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.

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

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
Default: JSON
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

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

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

How should we connect to your ClickHouse?¶

hostname

The hostname or IP address of the ClickHouse server

Type: string
Importance: high

port

The ClickHouse port - default is 8443 (for HTTPS in the cloud), but for HTTP (the default for self-hosted) it should be 8123

Type: int
Default: 8443
Importance: high

ssl

Enable SSL connection to ClickHouse

Type: boolean
Default: true
Importance: high

username

ClickHouse database username

Type: string
Default: default
Importance: high

password

ClickHouse database password

Type: password
Importance: high

database

ClickHouse database name

Type: string
Default: default
Importance: high

Database details¶

topic2TableMap

Comma-separated list that maps topic names to table names (e.g. “topic1=table1, topic2=table2, etc…”)

Type: string
Default: “”
Importance: medium

tableRefreshInterval

Time (in seconds) to refresh the table definition cache

Type: int
Default: 0
Importance: medium

keeperOnCluster

Allows configuration of ON CLUSTER parameter for self-hosted instances (e.g. ON CLUSTER clusterNameInConfigFileDefinition) for exactly-once connect_state table

Type: string
Default: “”
Importance: medium

bypassRowBinary

Allows disabling use of RowBinary and RowBinaryWithDefaults for Schema-based data (Avro, Protobuf, etc.) - should only be used when data will have missing columns, and Nullable/Default are unacceptable

Type: boolean
Default: false
Importance: medium

dateTimeFormats

Date time formats for parsing DateTime64 schema fields, separated by ; (e.g. someDateField=yyyy-MM-dd HH:mm:ss.SSSSSSSSS;someOtherDateField=yyyy-MM-dd HH:mm:ss)

Type: string
Default: “”
Importance: medium

Connection details¶

jdbcConnectionProperties

Connection properties when connecting to ClickHouse. Must start with ? and joined by & between param=value

Type: string
Default: “”
Importance: medium

exactlyOnce

Exactly Once Enabled

Type: boolean
Default: false
Importance: medium

errors.tolerance

Connector Error Tolerance. Supported: none, all

Type: string
Default: none
Importance: high

errors.deadletterqueue.topic.name

If set (with errors.tolerance=all), a DLQ will be used for failed batches (see Troubleshooting)

Type: string
Default: “”
Importance: medium

errors.deadletterqueue.context.headers.enable

Adds additional headers for the DLQ

Type: boolean
Default: false
Importance: medium

clickhouseSettings

Comma-separated list of ClickHouse settings (e.g. “insert_quorum=2, etc…”)

Type: string
Default: “”
Importance: medium

tolerateStateMismatch

Allows the connector to drop records “earlier” than the current offset stored AFTER_PROCESSING (e.g. if offset 5 is sent, and offset 250 was the last recorded offset)

Type: boolean
Default: false
Importance: medium

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

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

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