In this section, we provide guides and references to use the Sagemaker connector.
Configure and schedule Sagemaker metadata and profiler workflows from the OpenMetadata UI:
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.OpenMetadata 0.12 or later
To deploy OpenMetadata, check the Deployment guides.
OpenMetadata retrieves information about models and tags associated with the models in the AWS account. The user must have following policy set to ingest the metadata from Sagemaker.
For more information on Sagemaker permissions visit the AWS Sagemaker official documentation.
To run the Sagemaker ingestion, you will need to install:
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Sagemaker.
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
This is a sample config for Sagemaker:
- awsAccessKeyId & awsSecretAccessKey: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and authorize your requests (docs).
Access keys consist of two parts: An access key ID (for example,
AKIAIOSFODNN7EXAMPLE), and a secret access key (for example,
You must use both the access key ID and secret access key together to authenticate your requests.
You can find further information on how to manage your access keys here.
awsSessionToken: If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID and AWS Secrets Access Key. Also, these will include an AWS Session Token.
awsRegion: Each AWS Region is a separate geographic area in which AWS clusters data centers (docs).
As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.
Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.
You can find further information about configuring your credentials here.
endPointURL: To connect programmatically to an AWS service, you use an endpoint. An endpoint is the URL of the entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.
Find more information on AWS service endpoints.
profileName: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command. When you specify a profile to run a command, the settings and credentials are used to run that command. Multiple named profiles can be stored in the config and credentials files.
You can inform this field if you'd like to use a profile other than
Find here more information about Named profiles for the AWS CLI.
assumeRoleArn: Typically, you use
AssumeRole within your account or for cross-account access. In this field you'll set the
ARN (Amazon Resource Name) of the policy of the other account.
A user who wants to access a role in a different account must also have permissions that are delegated from the account administrator. The administrator must attach a policy that allows the user to call
AssumeRole for the
ARN of the role in the other account.
This is a required field if you'd like to
Find more information on AssumeRole.
assumeRoleSessionName: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role is assumed by different principals or for different reasons.
By default, we'll use the name
Find more information about the Role Session Name.
assumeRoleSourceIdentity: The source identity specified by the principal that is calling the
AssumeRole operation. You can use source identity information in AWS CloudTrail logs to determine who took actions with a role.
Find more information about Source Identity.
The sourceConfig is defined here:
markDeletedMlModels: Set the Mark Deleted Ml Models toggle to flag ml models as soft-deleted if they are not present anymore in the source system.
To send the metadata to OpenMetadata, it needs to be specified as
The main property here is the
openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
You can specify the
loggerLevel depending on your needs. If you are trying to troubleshoot an ingestion, running with
DEBUG will give you far more traces for identifying issues.
JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration.
If you have added SSL to the OpenMetadata server, then you will need to handle the certificates when running the ingestion too. You can either set
ignore, or have it as
validate, which will require you to set the
sslConfig.certificatePath with a local path where your ingestion runs that points to the server certificate file.
Find more information on how to troubleshoot SSL issues here.
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
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.