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Managing Credentials

On the release 0.12 we updated how services credentials are handled from an Ingestion Workflow. We are covering now two scenarios: 1. If we are running a metadata workflow for the first time, pointing to a service that does not yet exist, then the service will be created from the Metadata Ingestion pipeline. It does not matter if the workflow is run from the CLI or any other scheduler. 2. If instead, there is an already existing service to which we are pointing with a Metadata Ingestion pipeline, then we will be using the stored credentials, not the ones incoming from the YAML config.

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Existing Services

What this means is that once a service is created, the only way to update its connection credentials is via the UI or directly running an API call. This prevents the scenario where a new YAML config is created, using a name of a service that already exists, but pointing to a completely different source system. One of the main benefits of this approach is that if an admin in our organisation creates the service from the UI, then we can prepare any Ingestion Workflow without having to pass the connection details. For example, for an Athena YAML, instead of requiring the full set of credentials as below: 
source:
  type: athena
  serviceName: my_athena_service
  serviceConnection:
    config:
      type: Athena
      awsConfig:
        awsAccessKeyId: KEY
        awsSecretAccessKey: SECRET
        awsRegion: us-east-2
      s3StagingDir: s3 directory for datasource
      workgroup: workgroup name
  sourceConfig:
    type: DatabaseMetadata
    config:
      markDeletedTables: true
      includeTables: true
      includeViews: true
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>
We can use a simplified version: 
source:
  type: athena
  serviceName: my_athena_service
  sourceConfig:
    config:
      type: DatabaseMetadata
      markDeletedTables: true
      includeTables: true
      includeViews: true
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>
The workflow will then dynamically pick up the service connection details for my_athena_service and ingest the metadata accordingly. If instead, you want to have the full source of truth in your DAGs or processes, you can keep reading on different ways to secure the credentials in your environment and not have them at plain sight.

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Securing Credentials

Note that these are just a few examples. Any secure and automated approach to retrieve a string would work here, as our only requirement is to pass the string inside the YAML configuration.
When running Workflow with the CLI or your favourite scheduler, it’s safer to not have the services’ credentials visible. For the CLI, the ingestion package can load sensitive information from environment variables. For example, if you are using the Glue connector you could specify the AWS configurations as follows in the case of a JSON config file 
[...]
"awsConfig": {
    "awsAccessKeyId": "${AWS_ACCESS_KEY_ID}",
    "awsSecretAccessKey": "${AWS_SECRET_ACCESS_KEY}",
    "awsRegion": "${AWS_REGION}",
    "awsSessionToken": "${AWS_SESSION_TOKEN}"
},
[...]
Or 
[...]
awsConfig:
  awsAccessKeyId: '${AWS_ACCESS_KEY_ID}'
  awsSecretAccessKey: '${AWS_SECRET_ACCESS_KEY}'
  awsRegion: '${AWS_REGION}'
  awsSessionToken: '${AWS_SESSION_TOKEN}'
[...]
for a YAML configuration.

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AWS Credentials

The AWS Credentials are based on the following JSON Schema. Note that the only required field is the awsRegion. This configuration is rather flexible to allow installations under AWS that directly use instance roles for permissions to authenticate to whatever service we are pointing to without having to write the credentials down.

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AWS Vault

If using aws-vault, it gets a bit more involved to run the CLI ingestion as the credentials are not globally available in the terminal. In that case, you could use the following command after setting up the ingestion configuration file: 
aws-vault exec <role> -- $SHELL -c 'metadata ingest -c <path to connector>'

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GCP Credentials

The GCP Credentials are based on the following JSON Schema. These are the fields that you can export when preparing a Service Account. Once the account is created, you can see the fields in the exported JSON file from: 
IAM & Admin > Service Accounts > Keys
You can validate the whole Google service account setup here.

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Using GitHub Actions Secrets

If running the ingestion in a GitHub Action, you can create encrypted secrets to store sensitive information such as users and passwords. In the end, we’ll map these secrets to environment variables in the process, that we can pick up with os.getenv, for example: 
import os
import yaml

from metadata.workflow.metadata import MetadataWorkflow



CONFIG = f"""
source:
  type: snowflake
  serviceName: snowflake_from_github_actions
  serviceConnection:
    config:
      type: Snowflake
      username: {os.getenv('SNOWFLAKE_USERNAME')}
...
"""


def run():
    workflow_config = yaml.safe_load(CONFIG)
    workflow = MetadataWorkflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()


if __name__ == "__main__":
    run()
Make sure to update your step environment to pass the secrets as environment variables: 
- name: Run Ingestion
  run: |
    source env/bin/activate
    python ingestion-github-actions/snowflake_ingestion.py
  # Add the env vars we need to load the snowflake credentials
  env:
     SNOWFLAKE_USERNAME: ${{ secrets.SNOWFLAKE_USERNAME }}
     SNOWFLAKE_PASSWORD: ${{ secrets.SNOWFLAKE_PASSWORD }}
     SNOWFLAKE_WAREHOUSE: ${{ secrets.SNOWFLAKE_WAREHOUSE }}
     SNOWFLAKE_ACCOUNT: ${{ secrets.SNOWFLAKE_ACCOUNT }}
You can see a full demo setup here.

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Next Steps

For a step-by-step guide on using Airflow Connections to securely retrieve service credentials in your DAGs, see Using Airflow Connections.