OpenMetadata/openmetadata-docs/content/v1.2.0-SNAPSHOT/connectors/messaging/kafka/yaml.md

---
title: Run the Kafka Connector Externally
slug: /connectors/messaging/kafka/yaml
---

# Run the Kafka Connector Externally

In this section, we provide guides and references to use the Kafka connector.

Configure and schedule Kafka metadata and profiler workflows from the OpenMetadata UI:

- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)

{% partial file="/v1.2.0/connectors/external-ingestion-deployment.md" /%}

## Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%}
To deploy OpenMetadata, check the Deployment guides.
{%/inlineCallout%}



### Python Requirements

To run the Kafka ingestion, you will need to install:

```bash
pip3 install "openmetadata-ingestion[kafka]"
```

## Metadata Ingestion

All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/messaging/kafkaConnection.json)
you can find the structure to create a connection to Kafka.

In order to create and run a Metadata Ingestion workflow, we will follow
the steps to create a YAML configuration able to connect to the source,
process the Entities if needed, and reach the OpenMetadata server.

The workflow is modeled around the following
[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)

### 1. Define the YAML Config

This is a sample config for Kafka:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**bootstrapServers**: List of brokers as comma separated values of broker `host` or `host:port`.

Example: `host1:9092,host2:9092`

{% /codeInfo %}

{% codeInfo srNumber=2 %}

**schemaRegistryURL**: URL of the Schema Registry used to ingest the schemas of the topics.

**NOTE**: For now, the schema will be the last version found for the schema name `{topic-name}-value`. An [issue](https://github.com/open-metadata/OpenMetadata/issues/10399) to improve how it currently works has been opened.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**saslUsername**: SASL username for use with the PLAIN and SASL-SCRAM mechanisms.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**saslPassword**: SASL password for use with the PLAIN and SASL-SCRAM mechanisms.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

**saslMechanism**: SASL mechanism to use for authentication.

Supported: _GSSAPI, PLAIN, SCRAM-SHA-256, SCRAM-SHA-512, OAUTHBEARER_.

**NOTE**: Despite the name only one mechanism must be configured.

{% /codeInfo %}

{% codeInfo srNumber=6 %}

**basicAuthUserInfo**: Schema Registry Client HTTP credentials in the form of `username:password`.

By default, user info is extracted from the URL if present.

{% /codeInfo %}

{% codeInfo srNumber=7 %}

**consumerConfig**: The accepted additional values for the consumer configuration can be found in the following 
[link](https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md).

{% /codeInfo %}

{% codeInfo srNumber=8 %}

**schemaRegistryConfig**: The accepted additional values for the Schema Registry configuration can be found in the 
following [link](https://docs.confluent.io/5.5.1/clients/confluent-kafka-python/index.html#confluent_kafka.schema_registry.SchemaRegistryClient).

**Note:** To ingest the topic schema, `schemaRegistryURL` must be passed.

{% /codeInfo %}

#### Source Configuration - Source Config

{% codeInfo srNumber=9 %}

The sourceConfig is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/messagingServiceMetadataPipeline.json):

**generateSampleData:** Option to turn on/off generating sample data during metadata extraction.

**topicFilterPattern:** Note that the `topicFilterPattern` supports regex as include or exclude.

{% /codeInfo %}

#### Sink Configuration

{% codeInfo srNumber=10 %}

To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.

{% /codeInfo %}

{% partial file="/v1.2.0/connectors/workflow-config.md" /%}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

```yaml
source:
  type: kafka
  serviceName: local_kafka
  serviceConnection:
    config:
      type: Kafka
```
```yaml {% srNumber=1 %}
      bootstrapServers: localhost:9092
```
```yaml {% srNumber=2 %}
      schemaRegistryURL: http://localhost:8081  # Needs to be a URI
```
```yaml {% srNumber=3 %}
      saslUsername: username
```
```yaml {% srNumber=4 %}
      saslPassword: password
```
```yaml {% srNumber=5 %}
      saslMechanism: PLAIN
```
```yaml {% srNumber=6 %}
      basicAuthUserInfo: username:password
```
```yaml {% srNumber=7 %}
      consumerConfig: {}
```
```yaml {% srNumber=8 %}
      schemaRegistryConfig: {}
```
```yaml {% srNumber=9 %}
  sourceConfig:
    config:
      type: MessagingMetadata
      topicFilterPattern:
        excludes:
          - _confluent.*
      # includes:
      #   - topic1
      # generateSampleData: true
```
```yaml {% srNumber=10 %}
sink:
  type: metadata-rest
  config: {}
```

{% partial file="/v1.2.0/connectors/workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

### 2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

```bash
metadata ingest -c <path-to-yaml>
```

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration,
you will be able to extract metadata from different sources.