Jira Source Connector for Confluent Cloud

The fully-managed Jira Source connector for Confluent Cloud is used to move data from Jira to an Apache Kafka® topic. This connector polls data from Jira through Jira v2 APIs, converts data into Kafka records, and moves the records into the Kafka topic. Each row from a Jira table is converted into exactly one Kafka record.

Note

Features

The Jira Source connector supports the following features:

  • At least once delivery: The connector guarantees that records are delivered at least once to the Kafka topic (if the file row parsed is valid).

  • Supports multiple tasks: The connector supports running one or more tasks. More tasks may improve performance. One Jira resource (table) is covered by one task only.

  • Offset management capabilities: Supports offset management. For more information, see Manage custom offsets.

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

Limitations

Be sure to review the following information.

Jira Resources

The connector can fetch from the following resources:

Task Distribution

The Jira Source connector groups resources to process related data within a single task. The tasks.max configuration defines the maximum number of tasks, but the connector might use fewer, depending on the resources configured in jira.resources.

The connector uses the following grouping logic:

  • Issue Grouping: When you include issues in your configuration, it forms the basis of a task. Any of its dependent resources that are also specified in jira.resources will be processed in the same task.

    • The dependent resources for issues are: changelogs, issue_comments, issue_transitions, resolutions, and worklogs.

  • Project Grouping: When you include projects, it forms a separate task group.

    • The dependent resource for projects is versions.

  • Other Resources: Any other resources not part of these groups (for example, users or project_categories) are distributed among the remaining available tasks.

Example 1: Grouping with Dependents

Given the following configuration, where dependents are included:

"tasks.max": "4"
"jira.resources": "issues, resolutions, versions, worklogs, projects, project_categories"

Even though tasks.max is set to 4, the connector only creates three tasks because there are only three resource groups to process. The distribution is as follows:

  • Task 1: Processes projects and its requested dependent, versions.

  • Task 2: Processes issues and its requested dependents, resolutions and worklogs.

  • Task 3: Processes the remaining resource, project_categories.

Example 2: Grouping without Dependents

Given a configuration where only primary resources are requested:

"tasks.max": "2"
"jira.resources": "issues, projects"

The dependents are not included because they were not specified in jira.resources. The distribution is:

  • Task 1: Processes issues.

  • Task 2: Processes projects.

Manage custom offsets

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

To manage offsets:

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

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

Response:

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

{
    "id": "lcc-example123",
    "name": "{connector_name}",
    "offsets": [
        {
           "partition": {
           "name": "users"
           },
           "offset": {
           "date_updated": null
           }
        },
        {
           "partition": {
           "name": "projects"
           },
           "offset": {
           "date_updated": null
           }
        }
     ],
    "metadata": {
        "observed_at": "2024-03-28T17:57:48.139635200Z"
    }
}

Responses include the following information:

  • The position of latest offset.

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

  • Information about the connector.

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

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

{
   "type": "PATCH",
   "offsets": [
      {
         "partition": {
         "name": "users"
         },
         "offset": {
         "date_updated": 1618184736
         }
      },
      {
         "partition": {
         "name": "projects"
         },
         "offset": {
         "date_updated": 1618184736
         }
      }
   ]
}

Considerations:

  • You can only make one offset change at a time for a given connector.

  • This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.

  • For source connectors, the connector attempts to read from the position defined by the requested offsets.

Response:

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

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

Responses include the following information:

  • The requested position of the offsets in the source.

  • The time of the request to update the offset.

  • Information about the connector.

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

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

{
  "type": "DELETE"
}

Considerations:

  • Delete requests delete the offset for the provided partition and reset to the base state. A delete request is as if you created a fresh new connector.

  • This is an asynchronous request. To check the status of this request, you must use the check offset status API. For more information, see Get the status of an offset request.

  • Do not issue delete and patch requests at the same time.

  • For source connectors, the connector attempts to read from the position defined in the base state.

Response:

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

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

Responses include the following information:

  • Empty offsets.

  • The time of the request to delete the offset.

  • Information about Kafka cluster and connector.

  • The type of request.

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

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

Considerations:

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

Response:

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

{
   "request": {
      "id": "lcc-example123",
      "name": "{connector_name}",
      "offsets": [
         {
         "partition": {
            "name": "issues"
         },
         "offset": {
            "date_updated": 1618184736
         }
         }
      ],
      "requested_at": "2024-03-28T17:58:45.606796307Z",
      "type": "PATCH"
   },
   "status": {
      "phase": "APPLIED",
      "message": "The Connect framework-managed offsets for this connector have been altered successfully. However, if this connector manages offsets externally, they will need to be manually altered in the system that the connector uses."
   },
   "previous_offsets": [
      {
         "partition": {
         "name": "issues"
         },
         "offset": {
         "date_updated": "2023-11-07 18:44"
         }
      }
   ],
   "applied_at": "2024-03-28T17:58:48.079141883Z"
}

Responses include the following information:

  • The original request, including the time it was made.

  • The status of the request: applied, pending, or failed.

  • The time you issued the status request.

  • The previous offsets. These are the offsets that the connector last updated prior to updating the offsets. Use these to try to restore the state of your connector if a patch update causes your connector to fail or to return a connector to its previous state after rolling back.

JSON payload

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

Field

Definition

Required/Optional

name

The name of the table.

Required

date_updated

The Unix timestamp of the last update in the row of the target table.

Required

Quick Start

Use this quick start to get up and running with the Confluent Cloud Jira Source connector. The quick start provides the basics of selecting the connector and configuring it to get data from one or more Jira resources.

Prerequisites

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 Jira Source connector card.

Jira Source Connector Card

Step 4: Enter the connector details

Note

  • Make sure you have all your prerequisites completed.

  • An asterisk ( * ) designates a required entry.

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

  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. 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.

  1. Click Continue.

  1. Enter the following Jira server connection details:

    • Jira URL: The Jira server URL. For example: https://<server-name>.atlassian.net.

    • Jira Username: Username or email associated with the Jira account.

    • Jira API key: API key for the Jira account.

  2. Click Continue.

  1. Enter the following configuration details:

    • Topic Name Pattern: The pattern to use for the topic name where the ${resourceName} literal will be replaced with each resource name.

    • Since: Records created or updated after this time will be processed by the connector. The expected format for jira.since is yyyy-MM-dd HH:mm.

    • Jira resources: The resources that are to be extracted and written to Kafka.

  2. Select the output record value format (data going to the Kafka topic): AVRO, JSON, JSON_SR (JSON Schema), or PROTOBUF. Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf). For additional information, see Schema Registry Enabled Environments .

    Note

    For Schema Registry-based output formats, the connector attempts to deduce the schema based on the source API response returned. The connector registers a new schema for every NULL and NOT NULL value of an optional field in the API response. For this reason, the connector may register schema versions at a much higher rate than expected.

    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?.

    • Maximum Batch Size: Defaults to 100 records.

    • Maximum In Flight requests: The maximum number of requests that may be in flight at one time. Defaults to 10 requests.

    • Maximum Poll interval (ms): The time in milliseconds (ms) between requests to fetch changed or updated resources. Defaults to 3000 ms (3 seconds).

    • Request Interval (ms): The time in milliseconds to wait before checking for updated records. Defaults to 15000 ms (15 seconds).

    • Maximum Retries: The maximum number of times the connector retries a task before the task fails. Defaults to 10 retries.

    • Retry Backoff (ms): The time in milliseconds to wait following an error before a retry attempt is made. Defaults to 3000 ms (3 seconds).

    Auto-restart policy

    • Enable Connector Auto-restart: Control the auto-restart behavior of the connector and its task in the event of user-actionable errors. Defaults to true, enabling the connector to automatically restart in case of user-actionable errors. Set this property to false to disable auto-restart for failed connectors. In such cases, you would need to manually restart the connector.

    Transforms

    For all property values and definitions, see Configuration Properties.

  3. Click Continue.

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 tasks, use the range slider to select the desired number of tasks.

  2. Click Continue.

  1. Verify the connection details by previewing the running configuration.

  2. After you’ve validated that the properties are configured to your satisfaction, click Launch.

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

Step 5: Check for records

Verify that records are being produced in the Kafka topic.

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

Using the Confluent CLI

To set up and run the connector using the Confluent CLI, complete the following steps.

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": "JiraSource",
  "name": "JiraSourceConnector_0",
  "kafka.auth.mode": "SERVICE_ACCOUNT",
  "kafka.service.account.id": "<service-account-resource-ID>",
  "jira.url": "https://<server-name>.atlassian.net",
  "jira.username": "<authorized-user>",
  "jira.api.token": "*********************************",
  "jira.since": "2020-06-14 09:30",
  "jira.resources": "issues, users, worklogs",
  "output.data.format": "JSON",
  "tasks.max": "1",
}

Note the following property definitions:

  • "connector.class": Identifies the connector plugin name.

  • "name": Sets a name for your new connector.

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

    confluent iam service-account list

    For example:

    confluent iam service-account list

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

  • Enter the GitHub connection details.

  • "jira.url": The Jira server URL. For example: https://<server-name>.atlassian.net.

  • "jira.api.token": API key for the Jira account.

  • "jira.since": Records created or updated after this time will be processed by the connector. The expected format for this property is yyyy-MM-dd HH:mm. Note that the value should be configured according to the timezone set in the Jira environment for the jira.username configured.

  • "jira.resources": One or more resources that the connector extracts and writes to Kafka. See Jira Resources for details.

  • output.data.format": Enter an output data format (data going to the Kafka topic): AVRO, JSON_SR (JSON Schema), PROTOBUF, or JSON (schemaless). Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). For additional information, see Schema Registry Enabled Environments and Cloud connector limitations.

    Note

    For Schema Registry-based output formats, the connector attempts to deduce the schema based on the source API response returned. The connector registers a new schema for every NULL and NOT NULL value of an optional field in the API response. For this reason, the connector may register schema versions at a much higher rate than expected.

  • "tasks.max": The connector supports running one or more tasks. More tasks may improve performance.

See Configuration Properties for all property values and descriptions.

Step 4: Load the properties file and create the connector

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

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

For example:

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

Example output:

Created connector JiraSourceConnector_0 lcc-do6vzd

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name            | Status  | Type   | Trace
+------------+-----------------------------+---------+--------+-------+
lcc-do6vzd   | JiraSourceConnector_0       | RUNNING | source |       |

Step 6: Check the Kafka topic.

Verify that records are being produced at the Kafka topic.

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

Configuration Properties

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

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

Which topic name pattern do you want to send data to?

topic.name.pattern

The pattern to use for the topic name, where the ${resourceName} literal will be replaced with each resource name.

  • Type: string

  • Default: ${resourceName}

  • Importance: high

Schema Config

schema.context.name

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

  • Type: string

  • Default: default

  • Importance: medium

How should we connect to your Jira server?

jira.url

Jira server url, e.g. “https://server-name.atlassian.net”.

  • Type: string

  • Importance: high

jira.username

Username or email associated with the Jira account.

  • Type: string

  • Importance: high

jira.api.token

API key for this Jira account.

  • Type: password

  • Importance: high

Jira details

jira.since

Records created or updated after this time will be processed by the connector. The expected format for jira.since is yyyy-MM-dd HH:mm. Note that the value should be configured according to timezone set by the user(defined in Jira Username config) in Jira environment.

  • Type: string

  • Importance: medium

jira.resources

The resources that are to be extracted and written to Kafka.

  • Type: list

  • Importance: high

jira.lookback.ms

The number of milliseconds to look back when querying for issues. This creates a time window (lastUpdatedDate - X ms) to (currentTime) to handle potential race conditions and ensure no issues are missed. Default is 0 (disabled) and the maximum value is 9,00,000 ms (15 min)

  • Type: long

  • Default: 0

  • Valid Values: [0,…,900000]

  • Importance: medium

Connection details

max.batch.size

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

  • Type: int

  • Default: 100

  • Importance: low

max.in.flight.requests

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

  • Type: int

  • Default: 10

  • Importance: low

max.poll.interval.ms

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

  • Type: long

  • Default: 3000 (3 seconds)

  • Importance: low

request.interval.ms

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

  • Type: long

  • Default: 15000 (15 seconds)

  • Importance: low

max.retries

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

  • Type: int

  • Default: 10

  • Importance: low

retry.backoff.ms

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

  • Type: long

  • Default: 3000 (3 seconds)

  • Importance: low

Output messages

output.data.format

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

  • Type: string

  • Default: JSON

  • Importance: high

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

  • Type: int

  • Valid Values: [1,…]

  • Importance: high

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

  • Type: boolean

  • Default: true

  • Importance: medium

Additional Configs

header.converter

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

  • Type: string

  • Importance: low

producer.override.compression.type

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

  • Type: string

  • Importance: low

value.converter.allow.optional.map.keys

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

  • Type: boolean

  • Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

  • Type: boolean

  • Importance: low

value.converter.connect.meta.data

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

  • Type: boolean

  • Importance: low

value.converter.enhanced.avro.schema.support

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

  • Type: boolean

  • Importance: low

value.converter.enhanced.protobuf.schema.support

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

  • Type: boolean

  • Importance: low

value.converter.flatten.unions

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

  • Type: boolean

  • Importance: low

value.converter.generate.index.for.unions

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

  • Type: boolean

  • Importance: low

value.converter.generate.struct.for.nulls

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

  • Type: boolean

  • Importance: low

value.converter.int.for.enums

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

  • Type: boolean

  • Importance: low

value.converter.latest.compatibility.strict

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

  • Type: boolean

  • Importance: low

value.converter.object.additional.properties

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

  • Type: boolean

  • Importance: low

value.converter.optional.for.nullables

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

  • Type: boolean

  • Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

  • Type: boolean

  • Importance: low

value.converter.use.latest.version

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

  • Type: boolean

  • Importance: low

value.converter.use.optional.for.nonrequired

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

  • Type: boolean

  • Importance: low

value.converter.wrapper.for.nullables

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

  • Type: boolean

  • Importance: low

value.converter.wrapper.for.raw.primitives

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

  • Type: boolean

  • Importance: low

key.converter.key.subject.name.strategy

How to construct the subject name for key schema registration.

  • Type: string

  • Default: TopicNameStrategy

  • Importance: low

value.converter.decimal.format

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

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

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

  • Type: string

  • Default: BASE64

  • Importance: low

value.converter.flatten.singleton.unions

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

  • Type: boolean

  • Default: false

  • Importance: low

value.converter.reference.subject.name.strategy

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

  • Type: string

  • Default: DefaultReferenceSubjectNameStrategy

  • Importance: low

value.converter.value.subject.name.strategy

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

  • Type: string

  • Default: TopicNameStrategy

  • Importance: low

Egress allowlist

connector.egress.whitelist

  • Type: string

  • Default: “”

  • Importance: high

Next Steps

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

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