Databricks Delta Lake Sink Connector for Confluent Cloud

The fully-managed Databricks Delta Lake Sink connector for Confluent Cloud periodically polls data from Apache Kafka® and copies the data into an Amazon S3 staging bucket, and then commits these records to a Databricks Delta Lake instance.

Note the following considerations:

• The connector is available only on Amazon Web Services (AWS).
• The connector appends data only.
• The Amazon S3 bucket, the Delta Lake instance, and the Kafka cluster must be in the same region.
• The connector adds a field named partition. Your Delta Lake table must include a field named partition using type INT (partition INT).

Refer to the Cloud connector limitations for additional information.

Note

This is a Quick Start for the fully-managed cloud connector. If you are installing the connector locally for Confluent Platform, see Databricks Delta Lake Sink Connector for Confluent Platform.

Features

The Databricks Delta Lake Sink connector provides the following features:

• Supports multiple tasks: The connector supports running one or more tasks. Refer to the limitations for additional information.
• Supported data formats: The connector supports input data from Kafka topics in Avro, JSON Schema, and Protobuf formats. You must enable Schema Registry to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• Automatically creates tables: If you do not provide a table name, the connector can create a table using the originating Kafka topic name (that is–the configuration property defaults to ${topic}).

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.

Refer to the Cloud connector limitations for additional information.

See Configuration Properties for configuration property values and descriptions.

Quick Start

Important

Be sure to review and complete the tasks in Set up Databricks Delta Lake (AWS) before configuring the connector.

Use this quick start to get up and running with the Confluent Cloud Databricks Delta Lake Sink connector. The quick start provides the basics of selecting the connector and configuring it to stream data.

Prerequisites

• Authorized access to a Confluent Cloud cluster on AWS.
• All Databricks Delta Lake and AWS CloudFormation procedures completed. See Set up Databricks Delta Lake (AWS).
• The Confluent CLI installed and configured for the cluster. See Install the Confluent CLI.
• You must enable Schema Registry to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf). See Schema Registry Enabled Environments for additional information.
• For networking considerations, see Networking, DNS, and service endpoints. To use a set of public egress IP addresses, see Public Egress IP Addresses for Confluent Cloud Connectors.
• An AWS account configured with Access Keys. You use these access keys when setting up the connector.
• An AWS User Account IAM Policy configured for bucket access. Note that if you have an access policy (or policies) for Amazon S3 storage that includes a condition for NAT IPs, you must update your policy to also include Databricks VPC IDs for these S3 gateway endpoints. Resources to help you make this change and an example of the S3 policy can be found in the Databricks Community page.

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

Using the Confluent Cloud Console

Step 1: Launch your Confluent Cloud cluster

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 Databricks Delta Lake 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 Databricks Delta Lake Sink Connector screen, complete the following:

Select topicKafka credentialsAuthenticationConfigurationSizingReview and Launch

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 the S3 bucket

Check that records are populating the staging Amazon S3 bucket and then populating the Databricks Delta Lake table.

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.

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.

Using the Confluent CLI

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

Note

Make sure you have all your prerequisites completed.

Step 1: List the available connectors

Enter the following command to list available connectors:

confluent connect plugin list

Step 2: List the connector configuration properties

Enter the following command to show the connector configuration properties:

confluent connect plugin describe <connector-plugin-name>

The command output shows the required and optional configuration properties.

Step 3: Create the connector configuration file

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

{
  "name": "DatabricksDeltaLakeSinkConnector_0",
  "config": {
    "topics": "clickstreams, pageviews",
    "input.data.format": "AVRO",
    "connector.class": "DatabricksDeltaLakeSink",
    "name": "DatabricksDeltaLakeSinkConnector_0",
    "kafka.auth.mode": "KAFKA_API_KEY",
    "kafka.api.key": "****************",
    "kafka.api.secret": "**************************************************",
    "delta.lake.host.name": "dbc-e12345cd-e12345ed.cloud.databricks.com",
    "delta.lake.http.path": "sql/protocolv1/o/1234567891811460/0000-01234-str6jlpz",
    "delta.lake.token": "************************************",
    "delta.lake.topic2table.map": "pageviews:pageviews,clickstreams:clickstreams-test",
    "delta.lake.table.auto.create": "false",
    "staging.s3.access.key.id": "********************",
    "staging.s3.secret.access.key": "****************************************",
    "staging.bucket.name": "databricks0",
    "flush.interval.ms": "300000",
    "tasks.max": "1"
  }
}

Note the following required property definitions:

• "name": Sets a name for your new connector.
• "connector.class": Identifies the connector plugin name.
• "topics": Enter the topic name or a comma-separated list of topic names.

• "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
  

• "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 Confluent Cloud Schema Registry configured if using a schema-based message format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).

• "delta.lake...."”: See the Databricks Delta Lake setup procedure for where you can get this information. See Configuration Properties for additional property values and descriptions.

• "staging....": These properties use information you get from Databricks and AWS. See the Databricks Delta Lake setup procedure.

• "flush.interval.ms": The time interval in milliseconds (ms) to periodically invoke file commits. This property ensures the connector invokes file commits at every configured interval. The commit time is adjusted to 00:00 UTC. The commit is performed at the scheduled time, regardless of the last commit time or number of messages. This configuration is useful when you have to commit your data based on current server time, like at the beginning of each hour. The default value used is 300000 ms (5 minutes).

• "tasks.max": Enter the maximum number of tasks for the connector to use. Refer to the limitations for additional information.

Single Message Transforms: See the Single Message Transforms (SMT) documentation for details about adding SMTs using the CLI. See Unsupported transformations for a list of SMTs that are not supported with this connector.

See Configuration Properties for configuration 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 databricks-delta-lake-sink-config.json

Example output:

Created connector DatabricksDeltaLakeSinkConnector_0 lcc-ix4dl

Step 5: Check the connector status

Enter the following command to check the connector status:

confluent connect cluster list

Example output:

ID          |       Name                         | Status  | Type
+-----------+------------------------------------+---------+------+
lcc-ix4dl   | DatabricksDeltaLakeSinkConnector_0 | RUNNING | sink

Step 6: Check the S3 bucket.

Check that records are populating the staging Amazon S3 bucket and then populating the Databricks Delta Lake table.

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

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
• Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING
• Importance: high

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: 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

How should we connect to your Databricks Delta Lake?

delta.lake.host.name

The host name used to connect to Delta Lake.

• Type: string
• Importance: high

delta.lake.http.path

The HTTP path used to connect to Delta Lake.

• Type: string
• Importance: high

delta.lake.token

The personal access token used to authenticate the user when connecting to Delta Lake via JDBC.

• Type: password
• Importance: high

delta.lake.catalog

The destination catalog under which the destination database and tables are located.

• Type: string
• Default: “”
• Importance: low

delta.lake.database

The destination database under which the destination tables are located.

• Type: string
• Default: default
• Importance: low

delta.lake.table.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

delta.lake.topic2table.map

Map of topics to tables (optional). Format: comma-seperated tuples, e.g. <topic-1>:<table-1>,<topic-2>:<table-2>,…

• Type: string
• Default: “”
• Importance: low

delta.lake.table.auto.create

Whether to automatically create the destination table based on record schema if it does not exist.

• Type: boolean
• Default: false
• Importance: medium

delta.lake.tables.location

The underlying location where the data in the Delta Lake table(s) is stored. If you set s3://<your-s3-bucket>/tmp/, Delta Lake data will be stored under s3://<your-s3-bucket>/tmp/. Make sure the AWS IAM for the Databricks Delta Lake instance has the permision to write records to the specified directory, and the specified directory exists (e.g. tmp)

• Type: string
• Default: “”
• Importance: medium

delta.lake.table2partition.map

Map of tables to partition fields (optional). Format: comma-separated tuples. For example: <table-1>:<partition-1>,<table-2>:<partition-2>,… Note that you can specify multiple partitions per table. Be sure to add a separate tuple for each partition. For example: <table-1>:<partition-1>, <table-1>:<partition-2>), <table-2>:<partition-3>

• Type: string
• Default: “”
• Importance: low

Amazon S3 details

staging.s3.access.key.id

• Type: password
• Importance: high

staging.s3.secret.access.key

• Type: password
• Importance: high

flush.interval.ms

The time interval in milliseconds to periodically invoke file commits. This configuration ensures that file commits are invoked at every configured interval. Time of commit will be adjusted to 00:00 of selected timezone. The commit will be performed at the scheduled time, regardless of the previous commit time or number of messages. This configuration is useful when you have to commit your data based on current server time, for example at the beginning of every hour.

• Type: long
• Default: 300000 (5 minutes)
• Importance: medium

staging.bucket.name

The S3 staging bucket where files get written to from Kafka and subsequently copied into the Databricks Delta Lake table. Must be in the same region as your Confluent Cloud cluster.

• Type: string
• Importance: high

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]
• 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]
• 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.