KafkaConnect

PROD

Feature List

✓ Pipelines

✓ Pipeline Status

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✓ Usage

✕ Owners

✕ Tags

• Requirements
  • KafkaConnect Versions
• Metadata Ingestion
  • Service Name
  • Connection Details
  • Metadata Ingestion Options
• Troubleshooting
  • Workflow Deployment Error

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Requirements

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KafkaConnect Versions

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Metadata Ingestion

1

Visit the Services Page

Click `Settings` in the side navigation bar and then `Services`. The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first. This Service will be the bridge between OpenMetadata and your source system. Once a Service is created, it can be used to configure your ingestion workflows.

2

Create a New Service

Click on _Add New Service_ to start the Service creation.

3

Select the Service Type

Select KafkaConnect as the Service type and click _Next_.

4

Name and Describe your Service

Provide a name and description for your Service.

Service Name

OpenMetadata uniquely identifies Services by their **Service Name**. Provide a name that distinguishes your deployment from other Services, including the other KafkaConnect Services that you might be ingesting metadata from. Note that when the name is set, it cannot be changed.

5

Configure the Service Connection

In this step, we will configure the connection settings required for KafkaConnect. Please follow the instructions below to properly configure the Service to read from your sources. You will also find helper documentation on the right-hand side panel in the UI.

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Connection Details

1

Connection Details

When using a Hybrid Ingestion Runner, any sensitive credential fields—such as passwords, API keys, or private keys—must reference secrets using the following format:

Copy

password: secret:/my/database/password

This applies only to fields marked as secrets in the connection form (these typically mask input and show a visibility toggle icon). For a complete guide on managing secrets in hybrid setups, see the Hybrid Ingestion Runner Secret Management Guide.

• Host and Port: The hostname or IP address of the Kafka Connect worker with the REST API enabled eg.https://localhost:8083 or https://127.0.0.1:8083 or https://<yourkafkaconnectresthostnamehere>
• Kafka Connect Config: OpenMetadata supports username/password.
  1. Basic Authentication
     • Username: Username to connect to Kafka Connect. This user should be able to send request to the Kafka Connect API and access the Rest API GET endpoints.
     • Password: Password to connect to Kafka Connect.
• verifySSL : Whether SSL verification should be perform when authenticating.
• Kafka Service Name : The Service Name of the Ingested Kafka instance associated with this KafkaConnect instance.

2

Test the Connection

Once the credentials have been added, click on Test Connection and Save the changes.

3

Configure Metadata Ingestion

In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

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Metadata Ingestion Options

• Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
• Pipeline Filter Pattern (Optional): Use to pipeline filter patterns to control whether or not to include pipeline as part of metadata ingestion.
  • Include: Explicitly include pipeline by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all pipeline with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
  • Exclude: Explicitly exclude pipeline by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all pipeline with names matching one or more of the supplied regular expressions. All other schemas will be included.
• Include lineage (toggle): Set the Include lineage toggle to control whether to include lineage between pipelines and data sources as part of metadata ingestion.
• Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug.
• Mark Deleted Pipelines (toggle): Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.

4

Schedule the Ingestion and Deploy

Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.If something doesn’t look right, click the Back button to return to the appropriate step and change the settings as needed.After configuring the workflow, you can click on Deploy to create the pipeline.

5

View the Ingestion Pipeline

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.

If AutoPilot is enabled, workflows like usage tracking, data lineage, and similar tasks will be handled automatically. Users don’t need to set up or manage them - AutoPilot takes care of everything in the system.

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Debezium CDC Support

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What We Provide

1. Detects CDC Envelope Structures - Identifies Debezium’s CDC format with op, before, and after fields
2. Extracts Real Table Columns - Parses actual database columns from the CDC payload instead of CDC envelope metadata
3. Creates Accurate Column-Level Lineage - Maps lineage from source database tables → Kafka topics → target systems

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Recognized Configuration Parameters

• database.server.name - Server identifier (Debezium V1)
• topic.prefix - Topic prefix (Debezium V2)
• table.include.list - Tables to capture (e.g., mydb.customers,mydb.orders)

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Supported Connectors

• MySQL
• PostgreSQL
• MSSQL
• MongoDB
• Amazon S3 For these connectors, lineage information can be obtained provided they are configured with a source or sink and the corresponding metadata ingestion is enabled. Note: All supported database connectors listed above work seamlessly with Debezium CDC connectors for enhanced column-level lineage tracking. When using Debezium, OpenMetadata automatically detects the CDC envelope structure and extracts actual table columns for accurate lineage mapping.

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Missing Lineage

1. Kafka Service Association: Make sure the Kafka service that the data is being ingested from is associated with this Kafka Connect service. Additionally, verify that the correct name is passed on in the Kafka Service Name field during configuration. This field helps establish the lineage between the Kafka service and the Kafka Connect flow.
2. Source and Sink Configuration: Verify that the Kafka Connect connector associated with the service is configured with a source and/or sink database or storage system. Connectors without a defined source or sink cannot provide lineage data.
3. Metadata Ingestion: Ensure that metadata for both the source and sink database/storage systems is ingested and passed to the lineage system. This typically involves configuring the relevant connectors to capture and transmit this information.