<a id="cc-azure-log-analytics-sink-v2"></a>

# Azure Log Analytics Sink V2 Connector for Confluent Cloud

The fully managed Azure Log Analytics Sink V2 connector for Confluent Cloud streams records from Apache Kafka®
topics to an Azure Log Analytics workspace using the Azure Logs Ingestion API. The connector routes
records to custom Log Analytics tables using Data Collection Rules (DCRs) and authenticates with
Azure using Entra ID service principal credentials.

This quick start is for the fully managed Confluent Cloud connector. If you are
installing the connector locally for Confluent Platform, see [Azure Log Analytics Sink Connector for
Confluent Platform](https://docs.confluent.io/kafka-connectors/azure-log-analytics-sink/current/).

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](networking/internet-resource.md#clusters-connect-cloud).

## V2 improvements

The V2 connector includes the following improvements over the original Azure Log Analytics Sink
connector:

* Authenticates to Azure using Entra ID service principal credentials (OAuth2 client-credentials
  flow) instead of workspace shared keys.
* Routes records to Log Analytics tables through Data Collection Rules (DCRs), supporting up to 20
  tables per connector.
* Supports the Dead Letter Queue (DLQ) for records that fail delivery after retries are exhausted.
* Provides configurable retry behavior with exponential backoff and jitter, honoring Azure’s
  `Retry-After` header.
* Supports Azure Egress Private Link via Azure Monitor Private Link Scope (AMPLS).

## Features

The Azure Log Analytics Sink V2 connector for Confluent Cloud supports the following features:

* **At least once delivery**: Guarantees that records from the Kafka topic are delivered to Azure
  Log Analytics at least once.
* **Multiple tasks**: Supports running one or more tasks. More tasks may improve performance,
  bounded by the total partition count across subscribed topics.
* **Multi-table routing (topic-to-table mapping)**: Each configured table corresponds to a DCR
  stream named `Custom-<table>_CL`, which Azure maps to the destination table. Supports up to 20
  tables per connector, matching Azure’s maximum of 20 streams per DCR. Multiple topics may map
  to the same table.
* **Multiple Data Collection Rule (DCR) support**: Each Log Analytics table is mapped to its own
  DCR using the `table.to.dcr.map` property (for example,
  `table1:dcr-id1,table2:dcr-id2`). Your DCR must include a stream named
  `Custom-<table>_CL` for each table referenced by the connector. Multiple tables may share a
  DCR.
* **Azure Active Directory (Entra ID) authentication**: Authenticates to Azure using the OAuth2
  client-credentials flow with an app registration scoped to specific DCRs using the Monitoring
  Metrics Publisher role.
* **Multiple input data formats**: Supports AVRO, JSON_SR (JSON Schema), PROTOBUF (using Schema Registry),
  and JSON, BYTES (schemaless) for the Kafka record value.
* **Azure Egress Private Link support**: Allows traffic to the Azure Data Collection Endpoint to
  flow through a private endpoint using an Azure Monitor Private Link Scope (AMPLS).
* **Dead Letter Queue (DLQ) support**: Routes records that fail HTTP delivery after retries are
  exhausted to a configurable error topic, with full HTTP request and response context preserved
  as headers.
* **Configurable retry behavior**: Retries on 429 (throttling) and 5xx errors with exponential
  backoff and jitter. Honors Azure’s `Retry-After` header when present.

For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

## Limitations

Be sure to review the following information.

* For connector limitations, see [Azure Log Analytics Sink V2 Connector](limits.md#azure-log-analytics-sink-v2-limits) limitations.
* If you plan to use one or more Single Message Transformations (SMTs), see
  [SMT Limitations](single-message-transforms.md#cc-single-message-transforms-limitations).

## Quick start

Use this quick start to get up and running with the fully managed Azure Log Analytics Sink V2
connector. The quick start provides the basics of selecting the connector and configuring it to
stream events to Azure Log Analytics.

<a id="cc-azure-log-analytics-sink-v2-prereqs"></a>

Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/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](https://docs.confluent.io/confluent-cli/current/install.html).
  - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for
    example, Avro, JSON_SR (JSON Schema), or Protobuf).
  - An [|az| Active Directory app registration](https://learn.microsoft.com/en-us/entra/identity-platform/quickstart-register-app) with
    the Monitoring Metrics Publisher role assigned on each target Data Collection Rule (DCR).
  - At least one [Data Collection Rule (DCR)](https://learn.microsoft.com/en-us/azure/azure-monitor/essentials/data-collection-rule-overview)
    with a stream named `Custom-<table>_CL` for each target Log Analytics table. The DCR
    defines the schema and ingestion-time transformations for that stream.
  - The Data Collection Endpoint (DCE) URL for your Azure Monitor resource.
  - For networking considerations, see [Networking and DNS](overview.md#connect-internet-access-resources). To use a set of
    public egress IP addresses, see [Public Egress IP Addresses for Confluent Cloud Connectors](static-egress-ip.md#cc-static-egress-ips).
  <br/>
  - Kafka cluster credentials. The following lists the different ways you can provide credentials.
    - Enter an existing [service account](service-account.md#s3-cloud-service-account) resource ID.
    - Create a Confluent Cloud [service account](service-account.md#s3-cloud-service-account) for the connector. Make sure to review the ACL entries required in the [service account documentation](service-account.md#s3-cloud-service-account). 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](https://docs.confluent.io/confluent-cli/current/command-reference/api-key/confluent_api-key_create.html) *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](../get-started/index.md#cloud-create-kafka-cluster).

#### 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 **Azure Log Analytics Sink V2** connector card.

![Azure Log Analytics Sink V2 Connector Card](images/ccloud-azure-log-analytics-sink-v2-icon.png)

<a id="cc-azure-log-analytics-sink-v2-setup-connection"></a>

#### Step 4: Enter the connector details

At the **Add Azure Log Analytics Sink V2 Connector** screen, complete the steps under the
following tabs.

#### NOTE
* Ensure you have all your [prerequisites](#cc-azure-log-analytics-sink-v2-prereqs)
  completed.
* An asterisk ( \* ) designates a required entry.

### Topic selection

If you’ve already populated your Kafka topics, select the topics you want
to connect from the **Topics** list.

To create a new topic, click **+Add new topic**.

### Kafka access

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](service-account.md#s3-cloud-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.
2. Click **Continue**.

### Authentication

1. Configure the authentication properties:

   **Authentication**
   - **Azure AD Tenant ID**: Sets the directory (tenant) ID of the Azure Active Directory tenant used to authenticate ingestion requests.
   - **Azure AD Client ID**: Sets the Application (client) ID of the Azure AD app registration. The app must have the `Monitoring Metrics Publisher` role on each Data Collection Rule.
   - **Azure AD Client Secret**: Sets the client secret value for the Azure AD app registration.
   - **Logs Ingestion Endpoint**: Sets the Logs Ingestion endpoint URL (Data Collection Endpoint), for example, `https://my-dce-5kyl.eastus-1.ingest.monitor.azure.com`. Do not include a trailing slash.
2. Click **Continue**.

### Configuration

Configuration properties not shown in the Cloud Console use the default values.
For all property values and definitions, see
[Configuration properties](#cc-azure-log-analytics-sink-v2-config-properties).

- **Input Kafka record value format**: Sets the input Kafka record value format. Valid entries are `AVRO`, `JSON_SR`, `PROTOBUF`, `JSON`, or `BYTES`. You must configure Confluent Cloud Schema Registry if you use a schema-based message format such as `AVRO`, `JSON_SR`, or `PROTOBUF`.

**Tables**

- **Topic to Table Mapping**: Specifies a comma-separated list of topic-to-table mappings, for example, `topic1:table1,topic2:table2,topic3:table1`. Multiple topics can map to the same table. The number of unique tables determines how many APIs the connector creates (maximum `20`).
- **Table to DCR Mapping**: Specifies a comma-separated list of table-to-DCR mappings, for example, `table1:dcr-immutable-id1,table2:dcr-immutable-id2`. Each table referenced in the topic-to-table mapping must have a corresponding DCR entry.

### **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?](../sr/faqs-cc.md#faq-schema-contexts).

**Additional Configs**

- **Value Converter Replace Null With Default**: Specifies whether to replace fields that have a default value and that are null to the default value. When set to `true`, the connector uses the default value; otherwise, it uses `null`. Applies to the `JSON` converter.
- **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers.
- **Value Converter Reference Subject Name Strategy**: Sets the subject reference name strategy for values. Valid entries are `DefaultReferenceSubjectNameStrategy` or `QualifiedReferenceSubjectNameStrategy`. You can use this strategy only with `PROTOBUF` format; the default strategy is `DefaultReferenceSubjectNameStrategy`.
- **Schema ID For Value Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Schemas Enable**: Includes schema within each of the serialized values. Input messages must contain `schema` and `payload` fields and must not contain additional fields. For plain `JSON` data, set this to `false`. Applies to the `JSON` converter.
- **Errors Tolerance**: Use this property to configure the connector’s error handling behavior.

  #### WARNING
  Use this property with caution for sink connectors, as it can lead to data loss. If you set this property to `all`, the connector does not fail on errant records, but logs them (and sends to DLQ for sink connectors) and continues processing. If you set this property to `none`, the connector task fails on errant records.
- **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. Applies to the `AVRO`, `PROTOBUF`, and `JSON_SR` converters.
- **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers.
- **Value Converter Decimal Format**: Specifies the `JSON` or `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 `DECIMAL` logical type values in `JSON` or `JSON_SR` as a number representing the decimal value.
- **Schema GUID For Key Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Schema GUID For Value Converter**: Sets the schema GUID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema GUID for deserializing message values. This property is applicable only when `value.converter.value.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.
- **Value Converter Connect Meta Data**: Enables the Connect converter to add its metadata to the output schema. Applies to Avro converters.
- **Value Converter Value Subject Name Strategy**: Determines how to construct the subject name under which the value schema is registered with Schema Registry.
- **Key Converter Key Subject Name Strategy**: Determines how to construct the subject name for key schema registration.
- **Schema ID For Key Converter**: Sets the schema ID to use for deserialization when using `ConfigSchemaIdDeserializer`. This lets you specify a fixed schema ID for deserializing message keys. This property is applicable only when `key.converter.key.schema.id.deserializer` is set to `ConfigSchemaIdDeserializer`.

**Auto-restart policy**

- **Enable Connector Auto-restart**: Enables 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. If disabled, you must manually restart the connector.

**Consumer configuration**

- **Max poll interval(ms)**: Sets 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**: Sets 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.

**Behavior on error**

- **Behavior On Errors**: Sets the error handling behavior for HTTP error responses. Valid values are `FAIL` and `IGNORE`. Defaults to `FAIL`.

**Retry configurations**

- **Retry Backoff Policy**: Sets the backoff policy to use for retries. Valid values are `CONSTANT_VALUE` or `EXPONENTIAL_WITH_JITTER`. Defaults to `EXPONENTIAL_WITH_JITTER`.
- **Retry Backoff (ms)**: Sets the initial duration in milliseconds to wait before a retry attempt. Defaults to `3000` milliseconds.
- **Retry HTTP Status Codes**: Specifies a comma-separated list of HTTP status codes or ranges to retry on. Azure returns `429` for rate limiting. For example, `429,500-` retries on rate-limit responses and all `5xx` server errors. Defaults to `429,500-`.
- **Maximum Retries**: Sets the maximum number of times to retry on errors before failing the task. Defaults to `3`. Valid range is `1` to `10`.

**Batching**

- **Batch Size**: Sets the number of records to batch per request for all tables. The Azure Logs Ingestion API enforces a `1 MB` payload limit per request. Defaults to `500`. Valid range is `1` to `1000`.

**Transforms**

- **Single Message Transformations**: To add a new SMT, see [Add transforms](single-message-transforms.md#cc-single-message-transforms-ui).
  For more information about unsupported SMTs, see
  [Unsupported transformations](single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).

For all property values and definitions, see
[Configuration properties](#cc-azure-log-analytics-sink-v2-config-properties).

- Click **Continue**.

### Sizing

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 recommended tasks, enter the number of
   [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in the **Tasks**
   field.
2. Click **Continue**.

### Review and Launch

1. Verify the connection details.
2. Click **Launch**.

   The connector status changes from **Provisioning** to **Running**.

#### Step 5: Check for records

Verify that data is exported from Kafka to the Azure Log Analytics workspace. There may be a slight
delay due to data ingestion latency. For details, see
[Checking ingestion time](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/data-ingestion-time#checking-ingestion-time).

For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) 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](#cc-azure-log-analytics-sink-v2-prereqs)
completed.

#### Step 1: List the available connectors

Enter the following command to list available connectors:

```none
confluent connect plugin list
```

#### Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

```none
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.

```json
{
  "name": "AzureLogAnalyticsSinkV2_0",
  "config": {
    "connector.class": "AzureLogAnalyticsSinkV2",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "",
    "kafka.api.secret": "",
    "topics": "orders,events",
    "input.data.format": "AVRO",
    "tasks.max": "1",
    "azure.tenant.id": "",
    "azure.client.id": "",
    "azure.client.secret": "",
    "azure.logs.ingestion.endpoint": "",
    "topic.to.table.map": "orders:Orders_CL,events:Events_CL",
    "table.to.dcr.map": "Orders_CL:dcr-abc123,Events_CL:dcr-def456",
    "batch.size": "500"
   }
 }
```

Note the following property definitions:

* `"name"`: Sets a name for your new connector.
* `"topics"`: Enter the topic name or a comma-separated list of topic names.
* `"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), PROTOBUF, or STRING. You
  must have [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) configured if using a schema-based message
  format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
* `"connector.class"`: Identifies the connector plugin name.

* `"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](service-account.md#s3-cloud-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:
  ```bash
  confluent iam service-account list
  ```

  For example:
  ```bash
  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
  ```

* `"azure.tenant.id"`: Enter the Azure Active Directory (Entra ID) tenant ID.
* `"azure.client.id"`: Enter the client ID (application ID) of the Azure app
  registration.
* `"azure.client.secret"`: Enter the client secret of the Azure app registration.
* `"azure.logs.ingestion.endpoint"`: Enter the Data Collection Endpoint URL for
  your Azure Monitor resource.
* `"table.to.dcr.map"`: Enter one or more comma-separated
  `<table-name>:<dcr-immutable-id>` mappings. For example:
  `Orders:dcr-abc123,Events:dcr-def456`. Each DCR must include a stream named
  `Custom-<table>_CL`.
* `"topic.to.table.map"`: Comma-separated list of topic-to-table mappings, for example,
  `topic1:table1,topic2:table2,topic3:table1`. Multiple topics can map to the same table.
  The number of unique tables determines how many APIs the connector creates (maximum `20`).
* `"tasks.max"`: Enter the maximum number of
  [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use. More tasks may improve
  performance.

For information about adding SMTs using the Confluent CLI, see [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms).

See [Configuration properties](#cc-azure-log-analytics-sink-v2-config-properties) for all property values and
descriptions.

#### Step 4: Load the configuration file and create the connector

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

```none
confluent connect cluster create --config-file <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file azure-log-analytics-sink-v2-config.json
```

Example output:

```none
Created connector AzureLogAnalyticsSinkV2_0 lcc-ix4dl
```

#### Step 5: Check the connector status

Enter the following command to check the connector status:

```none
confluent connect cluster list
```

Example output:

```none
ID          |          Name                    | Status  | Type
+-----------+----------------------------------+---------+------+
lcc-ix4dl   | AzureLogAnalyticsSinkV2_0        | RUNNING | sink
```

#### Step 6: Check for records

Verify that data is exported from Kafka to the Azure Log Analytics workspace. There may be a slight
delay due to data ingestion latency. For details, see
[Checking ingestion time](https://learn.microsoft.com/en-us/azure/azure-monitor/logs/data-ingestion-time#checking-ingestion-time).

For more information and examples to use with the Confluent Cloud API for Connect,
see the [Confluent Cloud API for Connect Usage Examples](connect-api-section.md#ccloud-connect-api) section.

<a id="cc-azure-log-analytics-sink-v2-config-properties"></a>

## 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](/platform/current/connect/kafka_connectors.html).

### 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.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * Type: list
  * Importance: high

`errors.deadletterqueue.topic.name`
: The name of the topic to be used as the dead letter queue (DLQ) for messages that result in an error when processed by this sink connector, or its transformations or converters. Defaults to ‘dlq-${connector}’ if not set. The DLQ topic will be created automatically if it does not exist. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: dlq-${connector}
  * Importance: low

`reporter.result.topic.name`
: The name of the topic to produce records to after successfully processing a sink record. Defaults to ‘success-${connector}’ if not set. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: success-${connector}
  * Importance: low

`reporter.error.topic.name`
: The name of the topic to produce records to after each unsuccessful record sink attempt. Defaults to ‘error-${connector}’ if not set. You can provide `${connector}` in the value to use it as a placeholder for the logical cluster ID.
  <br/>
  * Type: string
  * Default: error-${connector}
  * Importance: low

### Schema Config

`schema.context.name`
: Add a schema context name. A schema context represents an independent scope in Schema Registry. It is a separate sub-schema tied to topics in different Kafka clusters that share the same Schema Registry instance. If not used, the connector uses the default schema configured for Schema Registry in your Confluent Cloud environment.
  <br/>
  * 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, BYTES, 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.
  <br/>
  * Type: string
  * Default: JSON_SR
  * Importance: high

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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, whenever possible.
  <br/>
  * Type: string
  * Valid Values: SERVICE_ACCOUNT, KAFKA_API_KEY
  * Importance: high

`kafka.api.key`
: Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * Type: password
  * Importance: high

### Consumer configuration

`max.poll.interval.ms`
: The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Valid Values: [1,…]
  * Importance: high

### Authentication

`azure.tenant.id`
: The Directory (tenant) ID of the Azure Active Directory tenant used to authenticate ingestion requests.
  <br/>
  * Type: string
  * Importance: high

`azure.client.id`
: The Application (client) ID of the Azure AD app registration. The app must have the `Monitoring Metrics Publisher` role on each Data Collection Rule.
  <br/>
  * Type: string
  * Importance: high

`azure.client.secret`
: The client secret value for the Azure AD app registration.
  <br/>
  * Type: password
  * Importance: high

`azure.logs.ingestion.endpoint`
: The Logs Ingestion endpoint URL (Data Collection Endpoint), for example, `https://my-dce-5kyl.eastus-1.ingest.monitor.azure.com`. Do not include a trailing slash.
  <br/>
  * Type: string
  * Importance: high

### Behavior on error

`behavior.on.error`
: Error handling behavior for HTTP error responses. Valid values are `FAIL` and `IGNORE`.
  <br/>
  * Type: string
  * Default: FAIL
  * Importance: low

### Tables

`topic.to.table.map`
: Comma-separated list of topic-to-table mappings, for example, `topic1:table1,topic2:table2,topic3:table1`. Multiple topics can map to the same table. The number of unique tables determines how many APIs the connector creates (maximum `20`).
  <br/>
  * Type: string
  * Importance: high

`table.to.dcr.map`
: Comma-separated list of table-to-DCR mappings, for example, `table1:dcr-immutable-id1,table2:dcr-immutable-id2`. Each table referenced in the topic-to-table mapping must have a corresponding DCR entry.
  <br/>
  * Type: string
  * Importance: high

### Batching

`batch.size`
: The number of records to batch per request for all tables. The Azure Logs Ingestion API enforces a `1 MB` payload limit per request.
  <br/>
  * Type: int
  * Default: 500
  * Valid Values: [1,…,1000]
  * Importance: medium

### Retry configurations

`retry.backoff.policy`
: The backoff policy to use for retries. Valid values are `CONSTANT_VALUE` or `EXPONENTIAL_WITH_JITTER`.
  <br/>
  * Type: string
  * Default: EXPONENTIAL_WITH_JITTER
  * Importance: medium

`retry.backoff.ms`
: The initial duration in milliseconds to wait before a retry attempt.
  <br/>
  * Type: int
  * Default: 3000 (3 seconds)
  * Valid Values: [100,…]
  * Importance: medium

`retry.on.status.codes`
: Comma-separated list of HTTP status codes or ranges to retry on. Azure returns `429` for rate limiting, for example, `429,500-` retries on rate limit responses and all `5xx` server errors.
  <br/>
  * Type: string
  * Default: 429,500-
  * Importance: medium

`max.retries`
: The maximum number of times to retry on errors before failing the task.
  <br/>
  * Type: int
  * Default: 3
  * Valid Values: [1,…,10]
  * Importance: medium

### 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](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset)
  <br/>
  * 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](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * Type: string
  * Importance: low

`key.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: string
  * Importance: low

`key.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message keys. Only applicable when key.converter.key.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: int
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.schema.guid`
: The schema GUID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema GUID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: string
  * Importance: low

`value.converter.use.schema.id`
: The schema ID to use for deserialization when using ConfigSchemaIdDeserializer. This allows you to specify a fixed schema ID to be used for deserializing message values. Only applicable when value.converter.value.schema.id.deserializer is set to ConfigSchemaIdDeserializer.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`errors.tolerance`
: Use this property if you would like to configure the connector’s error handling behavior. WARNING: This property should be used with CAUTION for SOURCE CONNECTORS as it may lead to dataloss. If you set this property to ‘all’, the connector will not fail on errant records, but will instead log them (and send to DLQ for Sink Connectors) and continue processing. If you set this property to ‘none’, the connector task will fail on errant records.
  <br/>
  * Type: string
  * Default: all
  * Importance: low

`key.converter.key.schema.id.deserializer`
: The class name of the schema ID deserializer for keys. This is used to deserialize schema IDs from the message headers.
  <br/>
  * Type: string
  * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
  * Importance: low

`key.converter.key.subject.name.strategy`
: How to construct the subject name for key schema registration.
  <br/>
  * 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:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Default: false
  * Importance: low

`value.converter.value.schema.id.deserializer`
: The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.
  <br/>
  * Type: string
  * Default: io.confluent.kafka.serializers.schema.id.DualSchemaIdDeserializer
  * 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.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * 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](/platform/current/tutorials/examples/cloud-etl/docs/index.html).
This example also shows how to use Confluent CLI to manage your resources in
Confluent Cloud.

[![image](images/topology.png)](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)