<a id="cc-databaricks-delta-lake-sink-configure-connector"></a>

# 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`).
* The connector requires Java version 11 or later.

Refer to the [Cloud connector limitations](../limits.md#databricks-delta-lake-sink-limits) for additional information.

#### NOTE
* This Quick Start is for the fully managed Confluent Cloud connector. If you are
  installing the connector locally for Confluent Platform, see [Databricks Delta Lake Sink
  Connector for Confluent Platform](https://docs.confluent.io/kafka-connectors/databricks-delta-lake-sink/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 Databricks Delta Lake Sink connector provides the following features:

* **Supports multiple tasks**: The connector supports running one or more tasks. Refer to the [limitations](../limits.md#databricks-delta-lake-sink-limits) 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](../../get-started/schema-registry.md#cloud-sr-config) to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
* **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 Connect Usage Examples](../connect-api-section.md#ccloud-connect-api) section.

Refer to the [Cloud connector limitations](../limits.md#databricks-delta-lake-sink-limits) for additional information.

See [Configuration Properties](#cc-databricks-delta-lake-sink-config-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) Sink Connector for Confluent Cloud](databricks-aws-setup.md#cc-databricks-setup-requirements) 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.

<a id="cc-databricks-delta-lake-sink-prereqs"></a>

Prerequisites
: - Authorized access to a [Confluent Cloud](https://www.confluent.io/confluent-cloud/) cluster on AWS.
  - All Databricks Delta Lake and AWS CloudFormation procedures completed. See [Set up Databricks Delta Lake (AWS) Sink Connector for Confluent Cloud](databricks-aws-setup.md#cc-databricks-setup-requirements).
  - The Confluent CLI installed and configured for the cluster. See [Install the Confluent CLI](https://docs.confluent.io/confluent-cli/current/install.html).
  - You must enable [Schema Registry](../../get-started/schema-registry.md#cloud-sr-config) to use a Schema Registry-based format (for example, Avro, JSON_SR (JSON Schema), or Protobuf).
  - 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).
  - An AWS account configured with [Access Keys](https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html#access-keys-and-secret-access-keys). You use these access keys when setting up the connector.
  - An AWS [User Account IAM Policy](../cc-s3-sink/cc-s3-sink.md#cc-s3-bucket-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](https://community.databricks.com/t5/product-platform-updates/update-your-aws-s3-access-rules-to-include-databricks-control/ba-p/57398).
  <br/>
  - 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 **Databricks Delta Lake Sink** connector card.

![Databricks Delta Lake Sink Connector Card](images/ccloud-databricks-delta-lake-sink-icon.png)

<a id="cc-databricks-delta-lake-sink-sink-setup-connection"></a>

#### Step 4: Enter the connector details

#### NOTE
* Ensure you have all your [prerequisites](#cc-databricks-delta-lake-sink-prereqs) completed.
* An asterisk ( \* ) designates a required entry.

At the **Add Databricks Delta Lake 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:
   - **Delta Lake Host Name**: The host name used to connect to Delta Lake. For example: `dbc-acdefg123456-hi78.cloud.databricks.com`.
   - **Delta Lake HTTP Path**: The HTTP path used to connect to Delta Lake. For example: `sql/protocolv1/a/123456789/1234-5678-abcd9efg`.
   - **Delta Lake Token**: The personal access token to authenticate your credentials when connecting to Delta Lake using JDBC.
   - **Delta Lake Catalog**: The destination catalog under which the destination database and tables are located.
   - **Delta Lake Database**: The destination database under which the destination tables are located.

   **Amazon S3 staging bucket name**
   - **S3 Staging Bucket Name**: The S3 staging bucket where files get written to from Kafka and subsequently copied into the Databricks Delta Lake table.

   **Amazon S3 credentials**
   - **Staging S3 Access Key ID**: The Access Key ID to provide S3 access to this Confluent connector. For more information on S3 Access Key, see [AWS security credentials](https://docs.aws.amazon.com/general/latest/gr/aws-sec-cred-types.html#access-keys-and-secret-access-keys).
   - **Staging S3 Secret Access Key**: The S3 secret access key.
2. Click **Continue**.

### Configuration

- **Input Kafka record value format**: Select the input Kafka record value format (data coming from the
  Kafka topic). Valid entires 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).
- **Delta Lake Table Format**: A format string for the
  destination table name, which may contain `${topic}` as a
  placeholder for the originating topic name. For example, to
  create a table named `kafka-orders` based on a Kafka topic
  named `orders`, you would enter `kafka-${topic}` in this
  field. Note that you must use the `${topic}` placeholder if
  you have multiple originating topics.
- **Input Kafka record key format**: Sets the input Kafka record
  key format. Valid entries are AVRO, BYTES, JSON, JSON_SR,
  PROTOBUF, or STRING. 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).
- **Delta Lake Topic2Table Map**: Map of topics to tables
  (optional). Create mapping as comma-separated tuples. For
  example: `<topic-1>:<table-1>,<topic-2>:<table-2>,...`. If
  you use this property, the connector ignores any string
  entered for **Delta Lake Table Format**.
- **Delta Lake Table Auto Create**: Specifies whether to create the destination table based on record schema if it does not exist.
- **Delta Lake Tables Location**: The underlying location where
  the data in the Delta Lake tables is stored. If you set
  `s3://<your-s3-bucket>/tmp/`, data is stored in the `tmp`
  directory. Be sure the AWS IAM role used for the Databricks
  Delta Lake instance is permitted to write records to the
  bucket directory and that the directory exists (in this case,
  `tmp`).
- **Delta Lake Table2Partition Map**: Map of tables to partition
  fields (optional). Create mapping as 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>"`.
- **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.
  Defaults to 300,000 milliseconds (5 minutes).

**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`.
- **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.
- **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.
- **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.
- **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`.

**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).

See [Configuration Properties](#cc-databricks-delta-lake-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. Refer to the [limitations](../limits.md#databricks-delta-lake-sink-limits)
for additional information.

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 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 Connect Usage Examples](../connect-api-section.md#ccloud-connect-api) section.

#### SEE ALSO
For an example that shows fully managed Confluent Cloud connectors in action with Confluent Cloud ksqlDB, 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)

### 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-databricks-delta-lake-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 <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.

```json
{
  "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](../service-account.md#s3-cloud-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:
  ```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
  ```

* `"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](databricks-aws-setup.md#cc-databricks-setup-gather-info) for where you can get this information. See [Configuration Properties](#cc-databricks-delta-lake-sink-config-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](databricks-aws-setup.md#cc-databricks-setup-gather-info).
* `"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](/platform/current/connect/concepts.html#tasks) for the connector to use. Refer to the [limitations](../limits.md#databricks-delta-lake-sink-limits) for additional information.

**Single Message Transformation (SMT)**: For details about adding SMTs using the Confluent CLI, see the [Single Message Transformations](../single-message-transforms.md#cc-single-message-transforms) documentation. For a list of SMTs that are not supported with this connector, see [Unsupported transformations](../single-message-transforms.md#cc-single-message-transforms-unsupported-transforms).

See [Configuration Properties](#cc-databricks-delta-lake-sink-config-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:

```none
confluent connect cluster create --config-file <file-name>.json
```

For example:

```none
confluent connect cluster create --config-file databricks-delta-lake-sink-config.json
```

Example output:

```none
Created connector DatabricksDeltaLakeSinkConnector_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   | 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 Connect Usage Examples](../connect-api-section.md#ccloud-connect-api) section.

<a id="cc-databricks-delta-lake-sink-config-properties"></a>

## 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.
  <br/>
  * Type: string
  * Importance: low

`topics`
: Identifies the topic name or a comma-separated list of topic names.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: dlq-${connector}
  * Importance: low

### 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.
  <br/>
  * 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.
  <br/>
  * 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
  <br/>
  * Type: string
  * Default: JSON
  * Valid Values: AVRO, BYTES, JSON, JSON_SR, PROTOBUF, STRING
  * Importance: high

### How should we connect to your data?

`name`
: Sets a name for your connector.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`kafka.api.secret`
: Secret associated with Kafka API key. Required when kafka.auth.mode==KAFKA_API_KEY.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Importance: high

`delta.lake.http.path`
: The HTTP path used to connect to Delta Lake.
  <br/>
  * Type: string
  * Importance: high

`delta.lake.token`
: The personal access token used to authenticate the user when connecting to Delta Lake via JDBC.
  <br/>
  * Type: password
  * Importance: high

`delta.lake.catalog`
: The destination catalog under which the destination database and tables are located.
  <br/>
  * Type: string
  * Default: “”
  * Importance: low

`delta.lake.database`
: The destination database under which the destination tables are located.
  <br/>
  * 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’.
  <br/>
  * 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>,…
  <br/>
  * 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.
  <br/>
  * 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)
  <br/>
  * 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>
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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).
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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)
  <br/>
  * 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)
  <br/>
  * Type: string
  * Importance: low

`header.converter`
: The converter class for the headers. This is used to serialize and deserialize the headers of the messages.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.auto.register.schemas`
: Specify if the Serializer should attempt to register the Schema.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.avro.schema.support`
: Enable enhanced schema support to preserve package information and Enums. Applicable for Avro Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.enhanced.protobuf.schema.support`
: Enable enhanced schema support to preserve package information. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.flatten.unions`
: Whether to flatten unions (oneofs). Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.index.for.unions`
: Whether to generate an index suffix for unions. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.generate.struct.for.nulls`
: Whether to generate a struct variable for null values. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.int.for.enums`
: Whether to represent enums as integers. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.latest.compatibility.strict`
: Verify latest subject version is backward compatible when use.latest.version is true.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.object.additional.properties`
: Whether to allow additional properties for object schemas. Applicable for JSON_SR Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.nullables`
: Whether nullable fields should be specified with an optional label. Applicable for Protobuf Converters.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.optional.for.proto2`
: Whether proto2 optionals are supported. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.latest.version`
: Use latest version of schema in subject for serialization when auto.register.schemas is false.
  <br/>
  * Type: boolean
  * Importance: low

`value.converter.use.optional.for.nonrequired`
: Whether to set non-required properties to be optional. Applicable for JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: int
  * Importance: low

`value.converter.wrapper.for.nullables`
: Whether nullable fields should use primitive wrapper messages. Applicable for Protobuf Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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:
  <br/>
  BASE64 to serialize DECIMAL logical types as base64 encoded binary data and
  <br/>
  NUMERIC to serialize Connect DECIMAL logical type values in JSON/JSON_SR as a number representing the decimal value.
  <br/>
  * Type: string
  * Default: BASE64
  * Importance: low

`value.converter.flatten.singleton.unions`
: Whether to flatten singleton unions. Applicable for Avro and JSON_SR Converters.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * 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.
  <br/>
  * Type: string
  * Default: TopicNameStrategy
  * Importance: low

### Auto-restart policy

`auto.restart.on.user.error`
: Enable connector to automatically restart on user-actionable errors.
  <br/>
  * Type: boolean
  * Default: true
  * Importance: medium

<a id="cc-databricks-delta-lake-sink-faq"></a>

## Frequently asked questions

Find answers to common questions about the Databricks Delta Lake Sink connector
for Confluent Cloud.

### Is the connector available on all cloud providers?

No. The fully managed Databricks Delta Lake Sink connector is available only
on Amazon Web Services (AWS). The connector does not run on Google Cloud or Azure hosted
Confluent Cloud clusters.

Additionally, the Amazon S3 staging bucket, the Databricks Delta Lake
instance, and the Kafka cluster must all be in the same AWS region.

### Does the connector support append-only writes or can it update existing records?

The connector performs append-only writes. It does not perform `UPSERT` or
`DELETE` operations on Delta Lake tables. All records from the Kafka topic are
appended to the destination Delta Lake table.

The connector also automatically adds a `partition` field to each record.
Your Delta Lake table must include this field using the type `INT`
(`partition INT`).

### The connector fails with an `Invalid SessionHandle` error. What does this mean?

This error occurs when the Databricks cluster goes idle and automatically
terminates the JDBC session after a period of inactivity. When the connector
attempts to use the closed session to execute a `COPY INTO` statement, the
connection fails.

To resolve this:

* Restart the failed connector task from the Confluent Cloud Console or using
  the Confluent CLI.
* Configure your Databricks cluster to use a longer auto-termination timeout,
  or disable auto-termination for clusters used by the connector.
* Ensure that `flush.interval.ms` is set low enough to keep the Databricks
  connection active. Increasing the flush interval beyond the cluster’s
  idle timeout can trigger this error.

### What S3 bucket permissions does the connector require?

The connector uses an intermediate Amazon S3 staging bucket to buffer records
before committing them to the Delta Lake table. The AWS IAM user or role
used by the connector must have the following permissions on the staging
bucket:

* `s3:PutObject`
* `s3:GetObject`
* `s3:DeleteObject`
* `s3:ListBucket`
* `s3:GetBucketLocation`

If your S3 bucket policy includes conditions on NAT IP addresses, you must
also add the Databricks VPC IDs for the S3 gateway endpoints to your policy.
For more information and an example policy, see the
[Databricks Community documentation](https://community.databricks.com/t5/product-platform-updates/update-your-aws-s3-access-rules-to-include-databricks-control/ba-p/57398).

For the full bucket policy, see [User Account IAM Policy](../cc-s3-sink/cc-s3-sink.md#cc-s3-bucket-policy).

### How does the connector handle data commits to Delta Lake?

The connector periodically flushes records from the S3 staging bucket to the
Delta Lake table using a `COPY INTO` statement. The flush interval is
controlled by the `flush.interval.ms` property (default: `300000` ms,
or five minutes).

The commit time is adjusted to `00:00` UTC. A commit is performed at each
scheduled interval, regardless of the last commit time or the number of
messages processed. This makes the connector suitable for use cases that
require time-based commits (for example, at the beginning of each hour).

### How do I map Kafka topics to specific Delta Lake table names?

Use the `delta.lake.topic2table.map` property to map specific Kafka topics
to Delta Lake table names. Specify mappings as comma-separated
`<topic>:<table>` pairs.

For example:

```json
{
  "delta.lake.topic2table.map": "pageviews:pageviews,clickstreams:clickstreams-test"
}
```

If a topic is not listed in the mapping, and `delta.lake.table.auto.create`
is set to `true`, the connector creates a table using the originating Kafka
topic name.

## 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)