# Azure Synapse Analytics Sink Connector for Confluent Cloud The fully managed Azure Synapse Analytics Sink connector for Confluent Cloud allows you to export data from Apache Kafka® topics to Azure Synapse Analytics. The connector polls data from Kafka and writes data to the data warehouse based on a topic subscription. Auto-creation of tables and limited auto-evolution are also supported. This connector is compatible with [Azure Synapse Analytics SQL pool](https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/create-data-warehouse-portal). Confluent Cloud is available through [Azure Marketplace](https://azuremarketplace.microsoft.com/en/marketplace/apps/confluentinc.confluent-cloud-azure-prod?tab=Overview) or [directly from Confluent](https://www.confluent.io/get-started/). #### NOTE * This Quick Start is for the fully managed Confluent Cloud connector. If you are installing the connector locally for Confluent Platform, see [Azure Synapse Analytics Sink connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/azure-sql-dw/current/). * If you require private networking for fully managed connectors, make sure to set up the proper networking beforehand. For more information, see [Manage Networking for Confluent Cloud Connectors](networking/internet-resource.md#clusters-connect-cloud). ## Features The Azure Synapse Analytics Sink connector supports the following features: * **At least once delivery**: This connector guarantees that records from the Kafka topic are delivered at least once. * **Supports multiple tasks**: The connector supports running one or more tasks. More tasks may improve performance. * **Supports auto-creation and auto-evolution**: - If **Auto create table** (`auto.create`) is enabled, the connector can create the destination table if it is missing. The connector uses the record schema as the basis for the table definition, and the table is created with records consumed from the topic. - If **Auto add columns** (`auto.evolve`) is enabled, the connector can perform limited auto-evolution by issuing the `alter` command on the destination table for a new record with a missing column. The connector will only add a column to a new record. Existing records will have `"null"` as the value for the new column. #### IMPORTANT For backward-compatible schema evolution, new fields in record schemas must be optional or have a default value. * **Supported data formats**: The connector supports Avro, JSON Schema (JSON_SR), and Protobuf input formats. [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use these Schema Registry-based formats. 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 Synapse Analytics Sink Connector](limits.md#azure-synapse-analytics-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). ## Quick Start Use this quick start to get up and running with the fully managed Azure Synapse Analytics Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream events. Prerequisites : - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on Microsoft Azure (Azure). - An authorized SQL data warehouse user and password for the connector configuration. - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html). - [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). - At least one source Kafka topic must exist in your Confluent Cloud cluster before creating the sink 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 Synapse Analytics Sink** connector card.  #### Step 4: Enter the connector details #### NOTE * Ensure you have all your [prerequisites](#cc-azure-synapse-analytics-sink-prereqs) completed. * An asterisk ( \* ) designates a required entry. At the **Add Azure Synapse Analytics 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: - **SQL Server Name**: Enter your Azure **SQL Server Name**. The SQL data warehouse server name is in the following format: `.database.windows.net`. - **SQL login**: Enter the login for the dedicated SQL pool (or SQL database) in the **SQL login** field. - **Login password**: For **Login password**, enter the password associated with the SQL login. - **Dedicated SQL pool**: Enter the name of the dedicated SQL pool in the **Dedicated SQL pool** field. 2. Click **Continue**. ### Configuration #### NOTE Configuration properties that are not shown in the Cloud Console use the default values. See [Configuration Properties](#cc-azure-synapse-analytics-sink-config-properties) for all property values and definitions. - **Input Kafka record value format**: Select an input Kafka record value format (data coming from the Kafka topic). Valid entries are AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) to use a schema-based message format (for example, Avro, JSON_SR (JSON Schema) or Protobuf). ### **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). - **Table name format**: A format string for the destination table name, which may contain `${topic}` as a placeholder for the originating topic name. For example, to create a table named `kafka-orders` based on a Kafka topic named `orders`, you would enter `kafka-${topic}` in this field. - **Database timezone**: Name of the JDBC timezone that should be sed in the connector when inserting time-based values. - **Batch size**: Specifies how many records to attempt to batch together for insertion into the destination table, when possible. - **Auto create table**: Whether to automatically create the destination table if it is found to be missing by issuing `CREATE`. - **Auto add columns**: Designates whether to automatically add columns in the table schema when found to missing relative to the record schema by issuing `ALTER`. - **When to quote SQL identifiers**: When to quote table names, column names, and other identifiers in SQL statements. - **Fields included**: List of comma-separated record value field names. If empty, all fields from the record value are used. Otherwise, used to filter to the desired fields. **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. **Additional Configs** - **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`. - **Value Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for values. The deserializer reads schema IDs from message headers. - **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 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 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. - **Key Converter Schema ID Deserializer**: Sets the class name of the schema ID deserializer for keys. The deserializer reads schema IDs from message headers. - **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`. **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). See [Configuration Properties](#cc-azure-synapse-analytics-sink-config-properties) for all property values and definitions. - Click **Continue**. ### Sizing Based on the number of topic partitions you select, you will be provided with a recommended number of tasks. 1. To change the number of recommended tasks, enter the number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use in the **Tasks** field. 2. Click **Continue**. ### Review and Launch 1. Verify the connection details. 2. Click **Launch**. The status for the connector should go from **Provisioning** to **Running**. #### Step 5: Check for records Verify that data is exported from Kafka to the data warehouse. 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-synapse-analytics-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 the required connector properties. ```json { "name": "AzureSqlDwSinkConnector_0", "config": { "topics": "pageviews", "input.data.format": "AVRO", "connector.class": "AzureSqlDwSink", "name": "AzureSqlDwSinkConnector_0", "kafka.auth.mode": "KAFKA_API_KEY", "kafka.api.key": "", "kafka.api.secret": "", "azure.sql.dw.server.name": "azure-sql-dw-sink.db.windows.net", "azure.sql.dw.user": "", "azure.sql.dw.password": "**************", "azure.sql.dw.database.name": "", "db.timezone": "UTC", "auto.create": "true", "auto.evolve": "true", "tasks.max": "1" } } ``` Note the following property definitions: * `"name"`: Sets a name for your new connector. * `"topics"`: Enter the topic name or a comma-separated list of topic names. * `"input.data.format"`: Sets the input Kafka record value format (data coming from the Kafka topic). Valid entries are **AVRO**, **JSON_SR**, and **PROTOBUF**. You must have [Schema Registry](../get-started/schema-registry.md#cloud-sr-config) configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). * `"connector.class"`: Identifies the connector plugin name. * `"kafka.auth.mode"`: Identifies the connector authentication mode you want to use. There are two options: `SERVICE_ACCOUNT` or `KAFKA_API_KEY` (the default). To use an API key and secret, specify the configuration properties `kafka.api.key` and `kafka.api.secret`, as shown in the example configuration (above). To use a [service account](service-account.md#s3-cloud-service-account), specify the **Resource ID** in the property `kafka.service.account.id=`. To list the available service account resource IDs, use the following command: ```bash confluent iam service-account list ``` For example: ```bash confluent iam service-account list Id | Resource ID | Name | Description +---------+-------------+-------------------+------------------- 123456 | sa-l1r23m | sa-1 | Service account 1 789101 | sa-l4d56p | sa-2 | Service account 2 ``` * `"azure.sql.<>""`: Enter the Azure SQL data warehouse connection details. Note that the Azure SQL data warehouse server name is in this format: `.db.windows.net`. * `"db.timezone""`: Enter a [valid database timezone](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones). Defaults to `UTC`. * `"auto.create"`: If set to `true`, the connector creates the destination table if it is missing. The connector uses the record schema as the basis for the table definition. The table is created with records consumed from the topic. * `"auto.evolve"`: If set to `true`, the connector can perform limited auto-evolution. The connector issues the `alter` command on the destination table for a new record with a missing column. The connector will only add a column to a new record. Existing records will have `"null"` as the value for the new column. * `"tasks.max"`: Enter the maximum number of [tasks](/platform/current/connect/concepts.html#tasks) for the connector to use. More tasks may improve performance. **SMTs**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](single-message-transforms.md#cc-single-message-transforms) documentation. See [Configuration Properties](#cc-azure-synapse-analytics-sink-config-properties) for all property values and descriptions. #### Step 4: Load the properties file and create the connector Enter the following command to load the configuration and start the connector: ```none confluent connect cluster create --config-file .json ``` For example: ```none confluent connect cluster create --config-file azure-synapse-analytics-sink-config.json ``` Example output: ```none Created connector AzureSqlDwSinkConnector_0 lcc-do6vzd ``` #### 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 | Trace +------------+----------------------------+---------+------+-------+ lcc-do6vzd | AzureSqlDwSinkConnector_0 | RUNNING | sink | | ``` #### Step 6: Check for records. Verify that data is exported from Kafka to the data warehouse. 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). ### 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 ### 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, or PROTOBUF. Note that you need to have Confluent Cloud Schema Registry configured if using a schema-based message format like AVRO, JSON_SR, and PROTOBUF.
* Type: string * Importance: high ### How should we connect to your data? `name` : Sets a name for your connector.
* Type: string * Valid Values: A string at most 64 characters long * Importance: high ### Kafka Cluster credentials `kafka.auth.mode` : Kafka Authentication mode. It can be one of KAFKA_API_KEY or SERVICE_ACCOUNT. It defaults to KAFKA_API_KEY mode, 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 ### Azure SQL Data Warehouse `azure.sql.dw.server.name` : Full Azure SQL server name in a valid format. For example, .database.windows.net.
* Type: string * Importance: high `azure.sql.dw.user` : Login for the dedicated SQL pool (or SQL database).
* Type: string * Importance: high `azure.sql.dw.password` : Password associated with the SQL login.
* Type: password * Importance: high `azure.sql.dw.database.name` : Name of the dedicated SQL pool (or SQL database).
* Type: string * Importance: high ### Data Mapping `table.name.format` : A format string for the destination table name, which may contain ‘${topic}’ as a placeholder for the originating topic name.
For example, `kafka_${topic}` for the topic ‘orders’ will map to the table name ‘kafka_orders’.
* Type: string * Default: ${topic} * Importance: medium `fields.whitelist` : List of comma-separated record value field names. If empty, all fields from the record value are utilized, otherwise used to filter to the desired fields.
* Type: list * Importance: medium `db.timezone` : Name of the JDBC timezone that should be used in the connector when inserting time-based values. Defaults to UTC.
* Type: string * Default: UTC * Importance: medium ### Writes `batch.size` : Specifies how many records to attempt to batch together for insertion into the destination table, when possible.
* Type: int * Default: 3000 * Valid Values: [1,…,3000] * Importance: medium ### SQL/DDL Support `auto.create` : Whether to automatically create the destination table based on record schema if it is found to be missing by issuing `CREATE`.
* Type: boolean * Default: false * Importance: medium `auto.evolve` : Whether to automatically add columns in the table schema when found to be missing relative to the record schema by issuing `ALTER`.
* Type: boolean * Default: false * Importance: medium `quote.sql.identifiers` : When to quote table names, column names, and other identifiers in SQL statements. For backward compatibility, the default is ‘always’.
* Type: string * Default: ALWAYS * Valid Values: ALWAYS, NEVER * Importance: medium ### 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 ### Auto-restart policy `auto.restart.on.user.error` : Enable connector to automatically restart on user-actionable errors.
* Type: boolean * Default: true * Importance: medium ### Additional Configs `consumer.override.auto.offset.reset` : Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. You can choose either to reset the position to the “earliest” offset (the default) or the “latest” offset. You can also select “none” if you would rather set the initial offset yourself and you are willing to handle out of range errors manually. More details: [https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset](https://docs.confluent.io/platform/current/installation/configuration/consumer-configs.html#auto-offset-reset)
* 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.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 `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 `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.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.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 ## Frequently asked questions Find answers to frequently asked questions about the Azure Synapse Analytics Sink connector for Confluent Cloud. ### Configuration and setup #### Why does my connector fail to start with an invalid server name error? This error occurs when the `azure.sql.dw.server.name` property is not in the expected format. The connector requires the server name to follow the format `.database.windows.net`. Server names ending in `.datawarehouse.fabric.microsoft.com` or other formats are not supported. ##### Resolution 1. Verify the server name is in the format `.database.windows.net`. 2. Ensure you are using an Azure Synapse Analytics dedicated SQL pool endpoint, not a serverless or Fabric endpoint. #### Why do you get errors when enabling auto-creation or auto-evolution of tables? This error occurs when the connector cannot automatically create or alter tables in the destination database. ##### Common causes * **Insufficient permissions:** The database user does not have the required `CREATE TABLE` or `ALTER TABLE` permissions. * **Schema incompatibility:** The Kafka record schema is not compatible with the destination table structure. New fields must be optional or have default values for auto-evolution to succeed. ##### Resolution 1. Ensure the database user has `CREATE TABLE` and `ALTER TABLE` permissions. 2. Verify that record schemas are backward-compatible. New fields must be optional or have default values. ### Authentication #### Why does my connector fail with authentication errors? This error occurs when the connector cannot authenticate with the Azure Synapse Analytics server. ##### Common causes * **Incorrect credentials:** The SQL login username or password is incorrect. * **Expired password:** The SQL login password has expired. * **Firewall restrictions:** The Azure Synapse Analytics firewall is blocking connections from Confluent Cloud. ##### Resolution 1. Verify the SQL login credentials are correct and the password has not expired. 2. Ensure the Azure Synapse Analytics firewall allows connections from Confluent Cloud egress IP addresses. For more information, see [Use Public Egress IP Addresses on Confluent Cloud for Connectors and Cluster Linking](../networking/static-egress-ip-addresses.md#static-egress-ip-addresses). ### Data formatting #### Why do you see data type mismatch errors when sinking data? This error occurs when the Kafka record schema does not match the column types in the destination Synapse table. ##### Resolution 1. Ensure schema compatibility between the Kafka record and the destination table. 2. Use Confluent Cloud Schema Registry to manage and enforce schemas. 3. Check the `db.timezone` setting for time-based values. The default value is UTC. #### What data formats does the connector support? The connector supports Avro, JSON Schema (JSON_SR), and Protobuf input formats. Confluent Cloud Schema Registry must be enabled for the connector to function. ### Performance #### Why is my connector experiencing slow write performance? Slow write performance can be caused by several factors. ##### Common causes * **Batch size configuration:** The default batch size might not be optimal for your workload. * **Provisioned capacity:** Insufficient DWU (Data Warehouse Units) provisioning on the Azure Synapse side. * **Network latency:** High latency between the Confluent Cloud region and the Azure Synapse Analytics region. ##### Resolution 1. Tune the `batch.size` parameter to optimize write throughput. 2. Ensure adequate DWU provisioning on the Azure Synapse Analytics side. 3. Consider increasing the number of tasks to parallelize writes. ## 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. [](https://docs.confluent.io/platform/current/tutorials/examples/cloud-etl/docs/index.html)