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.

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.
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.

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 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 Managed and Custom Connectors section.

Limitations¶

Be sure to review the following information.

For connector limitations, see Azure Synapse Analytics Sink Connector limitations.
If you plan to use one or more Single Message Transforms (SMTs), see SMT Limitations.
If you plan to use Confluent Cloud Schema Registry, see Schema Registry Enabled Environments.

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 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.
Schema Registry must be enabled to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
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¶

See the Quick Start for Confluent Cloud for installation instructions.

Step 2: Add a connector¶

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

Step 3: Select your connector¶

Click the Azure Synapse Analytics Sink connector card.

Step 4: Enter the connector details¶

Note

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

At the Add Azure Synapse Analytics Sink Connector screen, complete the following:

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.

Enter your Azure SQL Server Name. The SQL data warehouse server name is in the following format: <my_server_name>.database.windows.net.
Enter the login for the dedicated SQL pool (or SQL database) in the SQL login field.
For Login password, enter the password associated with the SQL login.
Enter the name of the dedicated SQL pool in the Dedicated SQL pool field.

Note

Configuration properties that are not shown in the Cloud Console use the default values. See Configuration Properties for all property values and definitions.

Select the Input Kafka record value format (data coming from the Kafka topic): AVRO, JSON_SR (JSON Schema), or PROTOBUF. A valid schema must be available in Schema Registry 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?.
- 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.
See Configuration Properties for all property values and definitions.
Click Continue.

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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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 the required connector properties.

{
  "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": "<my-kafka-api-key>",
    "kafka.api.secret": "<my-kafka-api-secret>",
    "azure.sql.dw.server.name": "azure-sql-dw-sink.db.windows.net",
    "azure.sql.dw.user": "<db_user>",
    "azure.sql.dw.password": "**************",
    "azure.sql.dw.database.name": "<db_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 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, 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
```
"azure.sql.<>"": Enter the Azure SQL data warehouse connection details. Note that the Azure SQL data warehouse server name is in this format: <my_server_name>.db.windows.net.
"db.timezone"": Enter a valid database timezone. 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 for the connector to use. More tasks may improve performance.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI.

See Configuration Properties for all property values and descriptions.

Step 4: Load the properties file and create the connector¶

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

confluent connect cluster create --config-file <file-name>.json

For example:

confluent connect cluster create --config-file azure-synapse-analytics-sink-config.json

Example output:

Created connector AzureSqlDwSinkConnector_0 lcc-do6vzd

Step 5: Check the connector status¶

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID           |             Name           | Status  | Type | Trace
+------------+----------------------------+---------+------+-------+
lcc-do6vzd   | 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 Managed and Custom Connectors section.

Tip

When you launch a connector, a Dead Letter Queue topic is automatically created. See Confluent Cloud Dead Letter Queue for details.

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.

Which topics do you want to get data from?¶

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.

Type: string
Default: KAFKA_API_KEY
Valid Values: KAFKA_API_KEY, SERVICE_ACCOUNT
Importance: high

kafka.api.key

Kafka API Key. Required when kafka.auth.mode==KAFKA_API_KEY.

Type: password
Importance: high

kafka.service.account.id

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

Type: string
Importance: high

kafka.api.secret

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

Type: password
Importance: high

Azure SQL Data Warehouse¶

azure.sql.dw.server.name

Full Azure SQL server name in a valid format. For example, <server-name>.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

Next Steps¶

For an example that shows fully-managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, see the Cloud ETL Demo. This example also shows how to use Confluent CLI to manage your resources in Confluent Cloud.