Zendesk Source Connector for Confluent Cloud¶

Note

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

Zendesk is a customer service system for tracking, prioritizing, and solving customer support tickets. The Kafka Connect Zendesk Source connector 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.

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).

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

Limitations¶

Be sure to review the following information.

For connector limitations, see Zendesk Source Connector 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.

Supported tables¶

See the following dropdown list for supported Zendesk tables.

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 Platform (GCP).
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.

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:

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- Global Access: Allows your connector to access everything you have access to. With global access, connector access will be linked to your account. This option is not recommended for production.
- Granular access: Limits the access for your connector. You will be able to manage connector access through a service account. This option is recommended for production.
- Use an existing API key: Allows you to enter an API key and secret part you have stored. You can enter an API key and secret (or generate these in the Cloud Console).
Click Continue.

Add the Zendesk authentication details:
- Zendesk Service URL: The URL where the connector gets Zendesk source data. For example, https://<sub-domain>.zendesk.com
- Endpoint Authentication type: Choose either basis or bearer for the authentication type. For more information, see OAuth tokens in the Zendesk docs.
Click Continue.

Add the following details:
- 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.
- Zendesk tables: The 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.
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 Schema contexts.
- Topic Name Pattern: The pattern to use for the topic name, where the ${entityName} literal is replaced with each entity name. If ${entityName} is not specified, the connector writes all records to a single topic named ZD_${entityName}. A valid topic pattern should follow the regex [a-zA-Z0-9\\.\\-\\_]*(\\$\\{entityName\\})?[a-zA-Z0-9\\.\\-\\_]*.
- Zendesk start time (ISO 8601): Rows updated after the time entered are processed by the connector. The value should be formatted using the ISO 8601 format yyyy-MM-dd'T'HH:mm:SS. If left blank, the default time is set to the time the connector is launched minus one minute.
- Transforms and Predicates: For details, see the Single Message Transforms (SMT) documentation.
For all property values and definitions, see Configuration Properties.
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 Connect 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-catalog-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 Connect section.

Configuration Properties¶

Use the following configuration properties with this connector.

Note

These are properties for the 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

A schema context represents an independent scope in Schema Registry, and can be used to create any number of separate ‘sub-registries’ within one Schema Registry cluster. Please refer Confluent documentation for more details.

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
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.