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KafkaConnect

KafkaConnect

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In this section, we provide guides and references to use the KafkaConnect connector. Configure and schedule KafkaConnect metadata and profiler workflows from the OpenMetadata UI:

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How to Run the Connector Externally

To run the Ingestion via the UI you’ll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

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Requirements

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Python Requirements

We have support for Python versions 3.9-3.11
To run the KafkaConnect ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[kafkaconnect]"

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Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to KafkaConnect. 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

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1. Define the YAML Config

This is a sample config for KafkaConnect:

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2. Run with the CLI

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

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Debezium CDC Support

The KafkaConnect connector provides full support for Debezium CDC connectors with intelligent column extraction and accurate lineage tracking.

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What We Provide

When you ingest Debezium connectors, OpenMetadata automatically:
  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

OpenMetadata recognizes the following Debezium configuration parameters for intelligent CDC detection:
  • 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)