Azure Synapse Analytics Sink Connector for Confluent Cloud

Note

This is a Quick Start for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see Azure Synapse Analytics Sink Connector for Confluent Platform.

The Azure Synapse Analytics Sink connector 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.

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 Connect section.

Limitations

Be sure to review the following information.

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.

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 topic(s) you want to connect from the Topics list.

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

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 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.
  • The example commands use Confluent CLI version 2. For more information see, Confluent CLI v2.

Step 1: List the available connectors.

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: Show the required connector configuration properties.

Enter the following command to show the required connector properties:

confluent connect plugin describe <connector-catalog-name>

For example:

confluent connect plugin describe AzureSqlDwSink

Example output:

Following are the required configs:
connector.class: AzureSqlDwSink
input.data.format
name
kafka.auth.mode
kafka.api.key
kafka.api.secret
azure.sql.dw.server.name
azure.sql.dw.user
azure.sql.dw.password
azure.sql.dw.database.name
tasks.max
topics

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 create --config <file-name>.json

For example:

confluent connect create --config 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 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 Connect 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 this connector.

Note

These are properties for the managed cloud connector. If you are installing the connector locally for Confluent Platform, see Azure Synapse Analytics Sink Connector 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

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
  • 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
  • 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

Number of tasks for this connector

tasks.max
  • Type: int
  • Valid Values: [1,…]
  • Importance: high

Next Steps

See also

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.

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