# 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](../get-started/schema-registry.md#cloud-sr-config) 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](provider-integration.md#connector-az-pi). * **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](#cc-azure-data-explorer-kusto-sink-setup-connection). * **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](offsets.md#custom-offsets-sink-proc). 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 Data Explorer (Kusto) Database Sink Connector](limits.md#cc-azure-data-explorer-kusto-sink-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). ## 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](#cc-azure-data-explorer-kusto-sink-topic-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](https://learn.microsoft.com/kusto/management/batching-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](#cc-azure-data-explorer-kusto-sink-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](https://learn.microsoft.com/kusto/api/get-started/app-managed-streaming-ingest) and [Configure streaming ingestion](https://learn.microsoft.com/azure/data-explorer/ingest-data-streaming). ## 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](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, 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](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). - 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 Data Explorer (Kusto) Database Sink** connector card. ![Azure Data Explorer (Kusto) Database Sink Connector Card](images/ccloud-azure-data-explorer-kusto-sink-icon.png) #### Step 4: Enter the connector details Complete the following to configure the connector. #### NOTE * Ensure you have all your [prerequisites](#cc-azure-data-explorer-kusto-sink-prereqs) 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 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: **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-..kusto.windows.net`. - **Kusto cluster query URL**: The Azure Data Explorer (Kusto) query endpoint URL, for example `https://..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. 2. 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](#cc-azure-data-explorer-kusto-sink-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 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](csfle.md#connect-csfle). ### **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). - **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](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). **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](offsets.md#connect-custom-offsets). For all property values and definitions, see [Configuration Properties](#cc-azure-data-explorer-kusto-sink-config-properties). - Click **Continue**. ### Sizing Based on the number of topic partitions you select, Confluent Cloud recommends a 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 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: ```none | 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. 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-data-explorer-kusto-sink-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 ``` 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. ```none { "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": "", "kafka.api.secret": "", "kusto.ingestion.url": "https://ingest-..kusto.windows.net", "kusto.query.url": "https://..kusto.windows.net", "authentication.method": "Service Principal", "aad.auth.authority": "", "aad.auth.appid": "", "aad.auth.appkey": "", "kusto.tables.topics.mapping": "[{'topic':'pageviews','db':'','table':'','format':'json','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](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.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 ``` * `"kusto.ingestion.url"`: The Azure Data Explorer (Kusto) ingestion endpoint URL, for example `https://ingest-..kusto.windows.net`. * `"kusto.query.url"`: The Azure Data Explorer (Kusto) query endpoint URL, for example `https://..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](provider-integration.md#connector-az-pi). * `"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](/platform/current/connect/concepts.html#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](csfle.md#connect-csfle). **SMTs**: For more information about adding SMTs using the Confluent CLI, see [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms). For all property values and descriptions, see [Configuration Properties](#cc-azure-data-explorer-kusto-sink-config-properties). #### 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 .json ``` For example: ```none confluent connect cluster create --config-file azure-data-explorer-kusto-sink-config.json ``` Example output: ```none Created connector AzureDataExplorerKustoSink_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 | 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: ```none | 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. 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. ## 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). ### 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-..kusto.windows.net`.
* Type: string * Default: “” * Importance: high `kusto.query.url` : Azure Data Explorer (Kusto) query endpoint URL, for example `https://..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](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](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. ```none [{'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](#cc-azure-data-explorer-kusto-sink-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](#cc-azure-data-explorer-kusto-sink-prereqs). | ### 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:
```none .show ingestion failures | where Table == '' .show streaming ingestion failures | where 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](/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)