Azure Data Explorer (Kusto) Database Sink Connector for Confluent Cloud

The fully managed Azure Data Explorer (Kusto) Database Sink connector for Confluent Cloud streams events from Apache Kafka® topics directly into Azure Data Explorer (Kusto) database tables, so that you can query and analyze the data using the Kusto Query Language (KQL) without managing any connector infrastructure.

Note

This connector does not support private networking. You can create it only on a Kafka cluster that uses public internet networking.

Features

The connector provides the following features:

Flexible ingestion modes: Supports both queued (batch) and streaming ingestion so that you can balance throughput against latency.
Broad input data format support: Supports Avro, JSON_SR (JSON Schema), Protobuf, JSON (schemaless), and BYTES input data. You must enable Schema Registry to use a Schema Registry-based format, such as Avro, JSON_SR, or Protobuf.
Configurable authentication: Authenticates to Azure Data Explorer using a Microsoft Entra ID (formerly Azure Active Directory) service principal, or through a secretless Confluent provider integration. For provider integration setup, see Manage an Microsoft Azure Provider Integration.
Table access validation: Validates at task startup that configured tables and ingestion mappings exist, the connector has ingest permission, and a streaming policy exists when streaming is enabled. If validation fails, the task fails.
Client-side encryption (CSFLE) support: Supports Client-Side Field Level Encryption (CSFLE) for sensitive data. For more information about CSFLE setup, see the connector configuration.
Error handling: Supports configurable retry behavior on ingestion failures and dead letter queue (DLQ) support for records that fail to process.
Offset management capabilities: Supports offset management. For more information, see Manage offsets for sink connectors.

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

Limitations

Be sure to review the following information.

For connector limitations, see Azure Data Explorer (Kusto) Database Sink Connector limitations.
If you plan to use one or more Single Message Transformations (SMTs), see SMT Limitations.

Ingestion Behavior

The connector ingests data using one of two methods, set per topic with the streaming value in the topic-to-table mapping. For details, see Topic-to-Table Mapping.

Queued ingestion

Queued ingestion is the default method. The connector hands off records to Kusto, which batches them before loading them into the target table. By default, Kusto seals a batch after five minutes, 500 files, or 1 GB, whichever comes first, so records can take up to five minutes to become available for queries. This delay is in addition to the time the connector buffers records before flushing them to Kusto (see Maximum flush interval (ms)).

To reduce this delay, lower the connector’s flush interval, or shorten the time limit on the target table’s ingestion batching policy. A common low-latency setting is 20 to 30 seconds, and the minimum is 10 seconds. Low values increase cost, and can increase latency instead of reducing it. For details, see IngestionBatching policy.

Queued ingestion is asynchronous. The connector commits Kafka offsets after Kusto accepts a batch, not after the batch finishes ingesting. If Kusto later rejects the batch, for example because of a missing table or mapping, or the wrong format, those records aren’t retried and aren’t sent to the dead letter queue (DLQ). To find rejected records, see Troubleshooting.

Streaming ingestion

With streaming ingestion, records become available for queries within seconds. The connector falls back to queued ingestion when:

A batch is too large for streaming. The size limit depends on the format and compression of the data.
A streaming attempt fails with a temporary error, such as throttling, three times.

Permanent errors don’t fall back to queued ingestion. They fail. Kusto batches data that falls back to queued ingestion like other queued data, so it takes longer to become available. For more than about 4 GB per hour into a single table, Microsoft recommends queued ingestion instead. For details, see Managed streaming ingestion and Configure streaming ingestion.

Quick Start

Use this quick start to get up and running with the Confluent Cloud Azure Data Explorer (Kusto) Database Sink connector. The quick start provides information on selecting the connector and configuring it to stream events from Kafka topics into Azure Data Explorer (Kusto) database tables.

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, such as Avro, JSON_SR, or Protobuf.
An Azure Data Explorer (Kusto) cluster with a database and table that you want to stream data into. Create the target table and its ingestion mapping before you start the connector. The connector doesn’t create tables or mappings. The mapping name must match the mapping value you set in the topic-to-table mapping.
A Microsoft Entra ID service principal (or a Confluent provider integration for Azure) with the following roles on the target database:
- Database Ingestor, to write data.
- Database Viewer, so the connector can validate your tables, mappings, and streaming policy at startup. Without this role, the connector skips validation, and a misconfigured table or mapping name causes data to never arrive, with no error.
If you plan to use streaming ingestion:
- Turn on streaming ingestion for the Kusto cluster.
- Set a streaming ingestion policy on the target database or table.
For networking considerations, see Networking and DNS. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.

Kafka cluster credentials. The following lists the different ways you can provide credentials.
- Enter an existing service account resource ID.
- Create a Confluent Cloud service account for the connector. Make sure to review the ACL entries required in the service account documentation. Some connectors have specific ACL requirements.
- Create a Confluent Cloud API key and secret. To create a key and secret, you can use confluent api-key create or you can autogenerate the API key and secret directly in the Cloud Console when setting up the connector.

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

To create and launch a Kafka cluster in Confluent Cloud, see Create a kafka cluster in Confluent Cloud.

Step 2: Add a connector

In the left navigation menu, click Connectors. If you already have connectors in your cluster, click + Add connector.

Step 3: Select your connector

Click the Azure Data Explorer (Kusto) Database Sink connector card.

Step 4: Enter the connector details

Complete the following to configure the connector.

Note

Ensure you have all your prerequisites completed.
An asterisk ( * ) designates a required entry.

At the Add Azure Data Explorer (Kusto) Database Sink Connector screen, complete the following:

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

Select the way you want to provide Kafka Cluster credentials. You can choose one of the following options:
- My account: This setting allows your connector to globally access everything that you have access to. With a user account, the connector uses an API key and secret to access the Kafka cluster. This option is not recommended for production.
- Service account: This setting limits the access for your connector by using a service account. This option is recommended for production.
- Use an existing API key: This setting allows you to specify an API key and a secret pair. You can use an existing pair or create a new one. This method is not recommended for production environments.
Note

Freight clusters support only service accounts for Kafka authentication.
Click Continue.

Authentication

Configure the authentication properties:

How should we connect to Azure Data Explorer (Kusto)?
- Kusto cluster ingestion URL: The Azure Data Explorer (Kusto) ingestion endpoint URL, for example https://ingest-<cluster>.<region>.kusto.windows.net.
- Kusto cluster query URL: The Azure Data Explorer (Kusto) query endpoint URL, for example https://<cluster>.<region>.kusto.windows.net. Used for table access validation and for streaming ingestion.
Authentication
- Authentication method: How Confluent Cloud authenticates to Azure Data Explorer. Service Principal uses a Microsoft Entra ID application ID and secret that you supply. Microsoft Entra ID application uses a secretless Confluent provider integration (no stored secret).
- Provider Integration: The Azure provider integration used to generate Microsoft Entra ID application tokens for authentication.
- Microsoft Entra ID application (client) ID: Application (client) ID of the Microsoft Entra ID service principal used to authenticate to Azure Data Explorer.
- Microsoft Entra ID application secret: Client secret (application key) of the Microsoft Entra ID service principal.
- Microsoft Entra ID tenant ID: Microsoft Entra ID tenant (directory) ID that the service principal belongs to.
Click Continue.

Configuration

Note

Configuration properties that are not shown in the Cloud Console use the default values. For all property values and definitions, see Configuration 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 have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

Database and table mapping

Topics to table mapping: A JSON array mapping each topic to a Kusto database and table, for example [{'topic':'topic1','db':'kustoDb','table':'table1','format':'json','mapping':'jsonMapping','streaming':'false'}]. The per-topic 'format' value is the Kusto ingestion data format and is independent of the input record format. The 'mapping' value must be the name of an existing ingestion mapping on the target table. Set 'streaming':'true' to use streaming ingestion. Otherwise, the connector uses queued ingestion.

Ingestion configuration

Maximum flush size (bytes): Maximum buffer size in bytes (per topic and partition) before flushing to Kusto.
Maximum flush interval (ms): Maximum staleness in milliseconds (per topic and partition) before flushing to Kusto.

Error handling

Behavior on error: Behavior when an error occurs while processing or ingesting records: FAIL (stop the task), LOG (log and continue), or IGNORE (continue).
Errors maximum retry time (ms): Maximum time in milliseconds the connector retries ingesting records into Kusto on failure.
Errors retry backoff time (ms): Backoff time in milliseconds between retry attempts.

Data decryption

Enable Client-Side Field Level Encryption for data decryption. Specify a Service Account to access the Schema Registry and associated encryption rules or keys with that schema. Select the connector behavior (ERROR or NONE) on data decryption failure. If set to ERROR, the connector fails and writes the encrypted data in the DLQ. If set to NONE, the connector writes the encrypted data in the target system without decryption. For more information on CSFLE or CSPE setup, see Manage encryption for connectors.

Show advanced configurations

Schema context: Select a schema context to use for this connector, if using a schema-based data format. This property defaults to the Default context, which configures the connector to use the default schema set up for Schema Registry in your Confluent Cloud environment. A schema context allows you to use separate schemas (like schema sub-registries) tied to topics in different Kafka clusters that share the same Schema Registry environment. For example, if you select a non-default context, a Source connector uses only that schema context to register a schema and a Sink connector uses only that schema context to read from. For more information about setting up a schema context, see What are schema contexts and when should you use them?.
Input Kafka record key format: Sets the input Kafka record key format. Valid entries are AVRO, BYTES, JSON, JSON_SR, PROTOBUF, or STRING. You must have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.

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.

Transforms

Single Message Transformations: To add a new SMT, see Add transforms. For more information about unsupported SMTs, see Unsupported transformations.

Processing position

Set offsets: Click Set offsets to define a specific offset for this connector to begin procession data from. For more information on managing offsets, see Manage offsets.

For all property values and definitions, see Configuration Properties.

Click Continue.

Sizing

Based on the number of topic partitions you select, Confluent Cloud recommends a number of tasks.

To change the number of recommended tasks, enter the number of tasks for the connector to use in the Tasks field.
Click Continue.

Review and launch

Verify the connection details.
Click Launch.

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

Step 5: Check Azure Data Explorer (Kusto)

After the connector is running, verify that records are populating your Azure Data Explorer (Kusto) database table. For example, run the following KQL query in the Azure Data Explorer web UI:

<kusto-table>
| take 10

With queued ingestion, records can take up to five minutes (the default ingestion batching policy) to become available for queries. This is in addition to the time the connector buffers records before flushing them (see Maximum flush interval (ms)). To shorten this delay, lower the flush interval or adjust the table’s ingestion batching policy.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. For more information, see View Connector Dead Letter Queue Errors in Confluent Cloud.

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

Using the Confluent CLI

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

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

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

{
  "name": "AzureDataExplorerKustoSink_0",
  "config": {
    "topics": "pageviews",
    "connector.class": "AzureDataExplorerKustoSink",
    "name": "AzureDataExplorerKustoSink_0",
    "input.data.format": "AVRO",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "kusto.ingestion.url": "https://ingest-<cluster>.<region>.kusto.windows.net",
    "kusto.query.url": "https://<cluster>.<region>.kusto.windows.net",
    "authentication.method": "Service Principal",
    "aad.auth.authority": "<microsoft-entra-id-tenant-id>",
    "aad.auth.appid": "<microsoft-entra-id-application-id>",
    "aad.auth.appkey": "<microsoft-entra-id-application-secret>",
    "kusto.tables.topics.mapping": "[{'topic':'pageviews','db':'<kusto-database>','table':'<kusto-table>','format':'json','mapping':'<kusto-mapping>','streaming':'false'}]",
    "tasks.max": "1"
  }
}

Note the following property definitions:

"name": Sets a name for your new connector.
"connector.class": Identifies the connector plugin name.
"topics": Identifies the topic name or a comma-separated list of topic names.
"input.data.format": Sets the input Kafka record value format. Valid entries are AVRO, JSON_SR, PROTOBUF, JSON, or BYTES. You must have Confluent Cloud Schema Registry configured if using a schema-based message format like Avro, JSON_SR, and Protobuf.

"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
```

"kusto.ingestion.url": The Azure Data Explorer (Kusto) ingestion endpoint URL, for example https://ingest-<cluster>.<region>.kusto.windows.net.
"kusto.query.url": The Azure Data Explorer (Kusto) query endpoint URL, for example https://<cluster>.<region>.kusto.windows.net. Used for table access validation and for streaming ingestion.
"authentication.method": How Confluent Cloud authenticates to Azure Data Explorer. Set to Service Principal to supply a Microsoft Entra ID application ID and secret, or Microsoft Entra ID application to use a secretless Confluent provider integration.
"aad.auth.authority", "aad.auth.appid", and "aad.auth.appkey": The Microsoft Entra ID tenant ID, application (client) ID, and application secret of the service principal used to authenticate to Azure Data Explorer. Required when "authentication.method" is set to Service Principal.
"provider.integration.id": The ID of your Azure provider integration. Required when "authentication.method" is set to Microsoft Entra ID application. For setup, see Manage an Microsoft Azure Provider Integration.
"kusto.tables.topics.mapping": A JSON array mapping each topic to a Kusto database and table, for example [{'topic':'topic1','db':'kustoDb','table':'table1','format':'json','mapping':'jsonMapping','streaming':'false'}]. The per-topic 'format' value is the Kusto ingestion data format (for example csv, json, or avro) that the connector converts records into before ingestion. This is independent of "input.data.format", which controls how the connector deserializes the Kafka record. The 'mapping' value must be the name of an existing ingestion mapping on the target table that matches this format. Set 'streaming':'true' to use streaming ingestion. Otherwise, the connector uses queued ingestion.
"tasks.max": Enter the number of tasks for the connector to use.

Note

To enable CSFLE or CSPE for data encryption, specify the following properties:

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

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

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

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

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

SMTs: For more information about adding SMTs using the Confluent CLI, see Single Message Transformations.

For all property values and descriptions, see Configuration Properties.

Step 4: Load the configuration 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 azure-data-explorer-kusto-sink-config.json

Example output:

Created connector AzureDataExplorerKustoSink_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |               Name               | Status  | Type
+-----------+----------------------------------+---------+------+
lcc-ix4dl   | AzureDataExplorerKustoSink_0     | RUNNING | sink

Step 6: Check Azure Data Explorer (Kusto)

After the connector is running, verify that records are populating your Azure Data Explorer (Kusto) database table. For example, run the following KQL query in the Azure Data Explorer web UI:

<kusto-table>
| take 10

With queued ingestion, records can take up to five minutes (the default ingestion batching policy) to become available for queries. This is in addition to the time the connector buffers records before flushing them (see Maximum flush interval (ms)). To shorten this delay, lower the flush interval or adjust the table’s ingestion batching policy.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. For more information, see View Connector Dead Letter Queue Errors in Confluent Cloud.

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

Schema Config

schema.context.name

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

Type: string
Default: default
Importance: medium

Input messages

input.data.format

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

Type: string
Default: JSON_SR
Importance: high

input.key.format

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

Type: string
Default: JSON_SR
Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING
Importance: high

Kafka Cluster credentials

kafka.auth.mode

Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, whenever possible.

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.

Type: password
Importance: high

kafka.service.account.id

The Service Account that will be used to generate the API keys to communicate with Kafka Cluster.

Type: string
Importance: high

kafka.api.secret

Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

Which topics do you want to get data from?

topics.regex

A regular expression that matches the names of the topics to consume from. This is useful when you want to consume from multiple topics that match a certain pattern without having to list them all individually.

Type: string
Importance: low

topics

Identifies the topic name or a comma-separated list of topic names.

Type: list
Importance: high

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.

Type: string
Default: dlq-${connector}
Importance: low

How should we connect to Azure Data Explorer (Kusto)?

kusto.ingestion.url

Azure Data Explorer (Kusto) ingestion endpoint URL, for example https://ingest-<cluster>.<region>.kusto.windows.net.

Type: string
Default: “”
Importance: high

kusto.query.url

Azure Data Explorer (Kusto) query endpoint URL, for example https://<cluster>.<region>.kusto.windows.net. Used for table access validation and for streaming ingestion.

Type: string
Default: “”
Importance: high

Authentication

authentication.method

How Confluent Cloud authenticates to Azure Data Explorer. Service Principal uses a Microsoft Entra ID application ID and secret that you supply. Microsoft Entra ID application uses a secretless Confluent provider integration (no stored secret).

Type: string
Default: Service Principal
Valid Values: Microsoft Entra ID application, Service Principal
Importance: high

provider.integration.id

Azure provider-integration to mint Microsoft Entra ID application tokens.

Type: string
Importance: high

aad.auth.appid

Application (client) ID of the Microsoft Entra ID service principal used to authenticate to Azure Data Explorer.

Type: string
Importance: high

aad.auth.appkey

Client secret (application key) of the Microsoft Entra ID service principal.

Type: password
Importance: high

aad.auth.authority

Microsoft Entra ID tenant (directory) ID that the service principal belongs to.

Type: string
Importance: high

Database and table mapping

kusto.tables.topics.mapping

A JSON array mapping each topic to a Kusto database/table, for example [{'topic':'topic1','db':'kustoDb','table':'table1','format':'csv','mapping':'csvMapping','streaming':'false'}]. Set 'streaming':'true' to use streaming ingestion; otherwise queued ingestion is used.

Type: string
Default: “”
Importance: high

Ingestion configuration

flush.size.bytes

Maximum buffer size in bytes (per topic and partition) before flushing to Kusto.

Type: long
Default: 1048576 (1 mebibyte)
Valid Values: [100,…]
Importance: medium

flush.interval.ms

Maximum staleness in milliseconds (per topic and partition) before flushing to Kusto.

Type: long
Default: 30000 (30 seconds)
Valid Values: [100,…]
Importance: medium

Error handling

behavior.on.error

Behavior when an error occurs while processing or ingesting records: FAIL (stop the task), LOG (log and continue), or IGNORE (continue).

Type: string
Default: FAIL
Importance: low

errors.retry.max.time.ms

Maximum time in milliseconds the connector retries ingesting records into Kusto on failure.

Type: long
Default: 300000 (5 minutes)
Importance: low

errors.retry.backoff.time.ms

Backoff time in milliseconds between retry attempts.

Type: long
Default: 10000 (10 seconds)
Valid Values: [1,…]
Importance: low

Consumer configuration

max.poll.interval.ms

The maximum delay between subsequent consume requests to Kafka. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 300000 milliseconds (5 minutes).

Type: long
Default: 300000 (5 minutes)
Valid Values: [60000,…,1800000] for non-dedicated clusters and [60000,…] for dedicated clusters
Importance: low

max.poll.records

The maximum number of records to consume from Kafka in a single request. This configuration property may be used to improve the performance of the connector, if the connector cannot send records to the sink system. Defaults to 500 records.

Type: long
Default: 500
Valid Values: [1,…,500] for non-dedicated clusters and [1,…] for dedicated clusters
Importance: low

Number of tasks for this connector

tasks.max

Maximum number of tasks for the connector.

Type: int
Valid Values: [1,…]
Importance: high

Additional Configs

consumer.override.auto.offset.reset

Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset

Type: string
Importance: low

consumer.override.isolation.level

Controls how to read messages written transactionally. If set to read_committed, consumer.poll() will only return transactional messages which have been committed. If set to read_uncommitted (the default), consumer.poll() will return all messages, even transactional messages which have been aborted. Non-transactional messages will be returned unconditionally in either mode. More details: https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#isolation-level

Type: string
Importance: low

header.converter

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

Type: string
Importance: low

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.

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.

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.

Type: boolean
Importance: low

value.converter.auto.register.schemas

Specify if the Serializer should attempt to register the Schema.

Type: boolean
Importance: low

value.converter.connect.meta.data

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

Type: boolean
Importance: low

value.converter.enhanced.avro.schema.support

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

Type: boolean
Importance: low

value.converter.enhanced.protobuf.schema.support

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

Type: boolean
Importance: low

value.converter.flatten.unions

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

Type: boolean
Importance: low

value.converter.generate.index.for.unions

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

Type: boolean
Importance: low

value.converter.generate.struct.for.nulls

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

Type: boolean
Importance: low

value.converter.int.for.enums

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

Type: boolean
Importance: low

value.converter.latest.compatibility.strict

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

Type: boolean
Importance: low

value.converter.object.additional.properties

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

Type: boolean
Importance: low

value.converter.optional.for.nullables

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

Type: boolean
Importance: low

value.converter.optional.for.proto2

Whether proto2 optionals are supported. Applicable for Protobuf Converters.

Type: boolean
Importance: low

value.converter.scrub.invalid.names

Whether to scrub invalid names by replacing invalid characters with valid characters. Applicable for Avro and Protobuf Converters.

Type: boolean
Importance: low

value.converter.use.latest.version

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

Type: boolean
Importance: low

value.converter.use.optional.for.nonrequired

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

Type: boolean
Importance: low

value.converter.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.

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.

Type: int
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

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.

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.

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.

Type: string
Default: TopicNameStrategy
Importance: low

key.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Key Converter.

Type: boolean
Default: true
Importance: low

key.converter.schemas.enable

Include schemas within each of the serialized keys. Input message keys must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Key Converter.

Type: boolean
Default: false
Importance: low

value.converter.decimal.format

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

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

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

Type: string
Default: BASE64
Importance: low

value.converter.flatten.singleton.unions

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

Type: boolean
Default: false
Importance: low

value.converter.ignore.default.for.nullables

When set to true, this property ensures that the corresponding record in Kafka is NULL, instead of showing the default column value. Applicable for AVRO,PROTOBUF and JSON_SR Converters.

Type: boolean
Default: false
Importance: low

value.converter.reference.subject.name.strategy

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

Type: string
Default: DefaultReferenceSubjectNameStrategy
Importance: low

value.converter.replace.null.with.default

Whether to replace fields that have a default value and that are null to the default value. When set to true, the default value is used, otherwise null is used. Applicable for JSON Converter.

Type: boolean
Default: true
Importance: low

value.converter.schemas.enable

Include schemas within each of the serialized values. Input messages must contain schema and payload fields and may not contain additional fields. For plain JSON data, set this to false. Applicable for JSON Converter.

Type: boolean
Default: false
Importance: low

value.converter.value.schema.id.deserializer

The class name of the schema ID deserializer for values. This is used to deserialize schema IDs from the message headers.

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.

Type: string
Default: TopicNameStrategy
Importance: low

Auto-restart policy

auto.restart.on.user.error

Enable connector to automatically restart on user-actionable errors.

Type: boolean
Default: true
Importance: medium

Topic-to-Table Mapping

The kusto.tables.topics.mapping property tells the connector which Kusto table each Kafka topic goes to. One connector can send different topics to different tables and databases.

The property is a JSON array with one entry per topic. You can use single quotes.

[{'topic':'orders','db':'salesdb','table':'Orders','format':'json','mapping':'Orders_json','streaming':'false'},
 {'topic':'*','db':'salesdb','table':'Events','format':'json'}]

In this example, records from the orders topic go to the Orders table. Records from every other topic go to the Events table.

Key	Required	Description
`topic`	Yes	The Kafka topic. Use `*` as a catch-all for any topic that doesn’t have its own entry.
`db`	Yes	The Kusto database.
`table`	Yes	The Kusto table. The table must already exist. The connector doesn’t create it.
`format`	Yes (always set it)	The format the connector uses to send data to Kusto: `json`, `avro`, or `csv`. See Choose a format.
`mapping`	No	The name of the table’s ingestion mapping. The mapping must already exist. The connector doesn’t create it. If you set `mapping`, you must also set `format`.
`streaming`	No	`true` for streaming ingestion, or `false` for queued ingestion. The default is `false`. Streaming requires extra setup in Azure. See Prerequisites.

Choose a format

format is the format the connector sends to Kusto. It’s separate from input.data.format, which is the format of your Kafka records. Choose format based on your Kafka record format:

Kafka record format (`input.data.format`)	Set `format` to
AVRO, JSON_SR, or PROTOBUF	`json` (preferred) or `avro`
JSON	`json`
BYTES	The format of the data in your records, for example `json` or `csv`

If you leave out format, the connector sends data as CSV. For JSON records, this creates unreadable rows. For AVRO, JSON_SR, and PROTOBUF records, the task fails.

Rules

Map every topic the connector reads from, or add a * entry to catch the rest. If a topic has its own entry, that entry is used instead of *. Each topic can appear only once.
Database, table, and mapping names can contain only letters, numbers, underscores (_), periods (.), and hyphens (-). Other characters, such as spaces, are rejected.
The mapping’s kind must match format. Use a JSON mapping with json, an Avro mapping with avro, and a CSV mapping with csv. Kusto looks up a mapping by both name and kind, so a mapping of the wrong kind is treated as not found, and the data doesn’t arrive.
Mapping paths are case-sensitive. They must match your record’s field names exactly ($.Id is not $.id). The connector checks that the mapping exists, but not its paths.
If you leave out mapping, Kusto maps the data automatically. JSON fields are matched to columns by name (exact case), and CSV fields by column order.

Troubleshooting

Connector is running, but rows are missing

Symptom

The connector is running and Kafka offsets are committed, but rows are missing from the target table.

Explanation

Queued ingestion is asynchronous. The connector commits offsets after Kusto accepts a batch, not after the batch finishes ingesting. If Kusto rejects the batch afterward, for example because of a missing table, a missing or mismatched mapping, or the wrong format, those records aren’t retried and aren’t sent to the DLQ.

Resolution

Check Kusto’s ingestion failures for the target table:

.show ingestion failures | where Table == '<table>'
.show streaming ingestion failures | where Table == '<table>'

Run these commands as a user with the Database Admin or Database Monitor role. Otherwise, you see only your own operations, not the connector’s. Kusto keeps ingestion failures for 14 days. Common causes include a missing mapping, a mapping whose kind doesn’t match format, or a missing streaming policy.

Rows arrive, but some columns are null

Symptom: Rows appear in the target table, but some columns contain null values.
Explanation: Either the mapping paths don’t match your record’s field names (mapping paths are case-sensitive), or the values don’t match the column types.
Resolution: Compare your ingestion mapping’s paths against your record’s field names, and check that each field’s value matches its target column type.

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.