In this section, we provide guides and references to use the Kinesis connector.
Configure and schedule Kinesis metadata workflows from the OpenMetadata UI:
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.
Requirements
OpenMetadata retrieves information about streams and sample data from the streams in the AWS account.
The user must have the following policy set to access the metadata from Kinesis.
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "KinesisPolicy",
"Effect": "Allow",
"Action": [
"kinesis:ListStreams",
"kinesis:DescribeStreamSummary",
"kinesis:ListShards",
"kinesis:GetShardIterator",
"kinesis:GetRecords"
],
"Resource": "*"
}
]
}
For more information on Kinesis permissions visit the AWS Kinesis official documentation.
Python Requirements
We have support for Python versions 3.9-3.11
To run the Kinesis ingestion, you will need to install:
pip3 install "openmetadata-ingestion[kinesis]"
All connectors are defined as JSON Schemas.
Here
you can find the structure to create a connection to Kinesis.
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
1. Define the YAML Config
This is a sample config for Kinesis:
Source Configuration - Service Connection
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.