Zendesk Source Connector for Confluent Cloud

Zendesk is a customer service system for tracking, prioritizing, and solving customer support tickets. The fully-managed Zendesk Source connector for Confluent Cloud copies data into Apache Kafka® from various Zendesk support tables such as tickets, ticket_audits, ticket_fields, groups, organizations, satisfaction_ratings, among others. The connector streams data to Zendesk using the Zendesk Support API. See Supported tables for more information.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Zendesk Source Connector for Confluent Platform.

Features

The Zendesk Source connector provides the following features:

  • Topics created automatically: The connector can automatically create Kafka topics.
  • At least once delivery: The connector guarantees that records are delivered at least once to the Kafka topic.
  • Supported data formats: The connector supports Avro, JSON Schema (JSON-SR), Protobuf, and JSON (schemaless) output formats. You must enable Schema Registry to use a Schema Registry-based format (for example, Avro, JSON Schema, or Protobuf).
  • 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 Managed and Custom Connectors section.

Limitations

Be sure to review the following information.

Supported tables

See the following dropdown list for supported Zendesk tables.

Supported tables

  • activities

  • apps

  • audit_logs

  • automations

  • bookmarks

  • brands

  • custom_roles

  • groups

  • group_memberships

  • locales

  • macros

  • organizations

  • organization_fields

  • organization_subscriptions

  • organization_memberships

  • recipient_addresses

  • requests

  • resource_collections

  • satisfaction_ratings

  • satisfaction_reasons

  • sharing_agreements

  • suspended_tickets

  • targets

  • target_failures

  • tickets

  • ticket_audits

  • ticket_fields

  • ticket_forms

  • ticket_metrics

  • triggers

  • trigger_categories

  • users

  • user_fields

  • views

  • workspaces

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": "tickets"
           },
           "offset": {
           "updated_at": 1712559408
           }
        },
        {
           "partition": {
           "name": "targets"
           },
           "offset": {
           "created_at": 1607376776000
           }
        },
        {
           "partition": {
           "name": "users"
           },
           "offset": {
           "updated_at": 1712639446
           }
        },
        {
           "partition": {
           "name": "ticket_audits"
           },
           "offset": {
           "created_at": 1607359500000
           }
        }
     ],
    "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.

JSON payload

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

Field Definition Required/Optional
created_at The UNIX timestamp when the table row was created. Required
updated_at The UNIX timestamp when the row was last updated. Required

Quick Start

Use this quick start to get up and running with the Confluent Cloud Zendesk Source connector. The quick start provides the basics of selecting the connector and configuring it to stream events.

Prerequisites
  • Authorized access to a Confluent Cloud cluster on Amazon Web Services (AWS), Microsoft Azure (Azure), or Google Cloud.
  • The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
  • Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
  • Authorization and credentials to access the Zendesk service URL.
  • Zendesk API: Support APIs must be enabled for the Zendesk account.
  • Either the oauth2 or password mechanisms should be enabled for the Zendesk account. For additional information, see Using the API dashboard: Enabling password or token access.
  • Certain tables, such as custom_roles, can only be accessed if the Zendesk Account is an Enterprise account. For more information, see Custom Agent Roles.
  • A few Zendesk configuration settings may need to be enabled to ensure export is possible. For example, satisfaction_ratings can only be exported if this option is enabled. For more information, see Support API: Satisfaction Ratings.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

See the Quick Start for Confluent Cloud for installation instructions.

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 Zendesk Source connector card.

Zendesk 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 Zendesk 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.
  2. Click Continue.

Step 5: Check for records

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 Managed and Custom Connectors 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. See Configuration Properties for additional configuration property values and descriptions.

{
  "connector.class": "ZendeskSource",
  "name": "ZendeskSource_0",
  "kafka.auth.mode": "KAFKA_API_KEY",
  "kafka.api.key": "<my-kafka-api-key>",
  "kafka.api.secret": "<my-kafka-api-secret>",
  "zendesk.url": "https://<sub-domain>.zendesk.com",
  "zendesk.tables": "tickets, groups, users",
  "zendesk.user": "<username>",
  "zendesk.password": "*********************************",
  "output.data.format": "AVRO",
  "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 Zendesk connection details.

    • "zendesk.url": The URL where the connector gets Zendesk source data. For example, https://<sub-domain>.zendesk.com``.
    • "zendesk.tables": A comma-separated list of Zendesk tables the connector exports and writes to Kafka. To balance the load between workers, order the tables by their expected size or throughput requirement. For the list of supported tables, see Supported tables.

  • Enter the authentication details. The example shows the default basic authentication properties "zendesk.user" and "zendesk.password". You can use the properties "zendesk.auth.type": "bearer" and "bearer.token": "<token-string>" to authenticate. This is a single string that is sent in the HTTP Authorization header.

  • "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). See Schema Registry Enabled Environments for additional information.

    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": Enter the number of tasks to use with the connector. Only one task per connector is supported.

  • Transforms and Predicates: See the Single Message Transforms (SMT) documentation for details.

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 zendesk-source-config.json

Example output:

Created connector ZendeskSource_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   | ZendeskSource_0          | RUNNING | source |       |

Step 6: Check for records.

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 Managed and Custom Connectors 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.

Note

These are properties for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Zendesk Source Connector 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

How do you want to name your topic(s)?

topic.name.pattern

The pattern to use for the topic name, where the ${entityName} literal will be replaced with each entity name. If ${entityName} is not specified all the records will be written to a single topic. A valid topic pattern should follow the regex [a-zA-Z0-9.-_]*(${entityName})?[a-zA-Z0-9.-_]*

  • Type: string
  • Default: ZD_${entityName}
  • Valid Values: Must match the regex [a-zA-Z0-9\.\-\_]*(\$\{entityName\})?[a-zA-Z0-9\.\-\_]*
  • 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 Zendesk?

zendesk.url

The zendesk service url that connector will connect to.

  • Type: string
  • Importance: high
zendesk.auth.type

Authentication type of the endpoint. Valid values are basic and bearer

  • Type: string
  • Default: basic
  • Valid Values: basic, bearer
  • Importance: high
zendesk.tables

The Zendesk tables that are to be exported and written to Kafka. To avail a reasonable load balance between workers, the tables could be ordered by their expected size or throughput.

  • Type: list
  • Importance: high
zendesk.since

Rows updated after this time will be processed by the connector. If left blank, the default time will be set to the time this connector is launched minus 1 minute. The value should be formatted as ISO 8601. Example format yyyy-MM-dd’T’HH:mm:SS.

  • Type: string
  • Importance: medium

Authorization: Basic

zendesk.user

The username to be used with an endpoint requiring authentication.

  • Type: string
  • Importance: high
zendesk.password

The password to be used with an endpoint requiring authentication.

  • Type: password
  • Importance: high

Authorization: Bearer

bearer.token

The bearer authentication token to be used when auth.type=bearer. The supplied token will be used as the value of Authorization header in HTTP requests.

  • Type: password
  • Importance: high

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

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.

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