> ## Documentation Index > Fetch the complete documentation index at: https://docs.getcollate.io/llms.txt > Use this file to discover all available pages before exploring further. # Managing Credentials # 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. ## 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: ```yaml theme={null} 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: authProvider: ``` We can use a simplified version: ```yaml theme={null} 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: authProvider: ``` 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. ## 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](/connectors/database/glue) connector you could specify the AWS configurations as follows in the case of a JSON config file ```json theme={null} [...] "awsConfig": { "awsAccessKeyId": "${AWS_ACCESS_KEY_ID}", "awsSecretAccessKey": "${AWS_SECRET_ACCESS_KEY}", "awsRegion": "${AWS_REGION}", "awsSessionToken": "${AWS_SESSION_TOKEN}" }, [...] ``` Or ```yaml theme={null} [...] awsConfig: awsAccessKeyId: '${AWS_ACCESS_KEY_ID}' awsSecretAccessKey: '${AWS_SECRET_ACCESS_KEY}' awsRegion: '${AWS_REGION}' awsSessionToken: '${AWS_SESSION_TOKEN}' [...] ``` for a YAML configuration. ### AWS Credentials The AWS Credentials are based on the following [JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/security/credentials/awsCredentials.json). 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. #### AWS Vault If using [aws-vault](https://github.com/99designs/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: ```bash theme={null} aws-vault exec -- $SHELL -c 'metadata ingest -c ' ``` ### GCP Credentials The GCP Credentials are based on the following [JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/security/credentials/gcpCredentials.json). 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](/deployment/security/google). ### Using GitHub Actions Secrets If running the ingestion in a GitHub Action, you can create [encrypted secrets](https://docs.github.com/en/actions/security-guides/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: ```python theme={null} 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: ```yaml theme={null} - 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](https://github.com/open-metadata/openmetadata-demo/tree/main/ingestion-github-actions). ### Using Airflow Connections In any connector page, you might have seen an example on how to build a DAG to run the ingestion with Airflow (e.g., [Athena](/connectors/database/athena/yaml)). A possible approach to retrieving sensitive information from Airflow would be using Airflow's [Connections](https://airflow.apache.org/docs/apache-airflow/stable/howto/connection.html). Note that these connections can be stored as environment variables, to Airflow's underlying DB or to multiple external services such as Hashicorp Vault. Note that for external systems, you'll need to provide the necessary package and configure the [Secrets Backend](https://airflow.apache.org/docs/apache-airflow/stable/security/secrets/secrets-backend/index.html). The best way to choose how to store these credentials is to go through Airflow's [docs](https://airflow.apache.org/docs/apache-airflow/stable/concepts/connections.html). #### Example Let's go over an example on how to create a connection to extract data from MySQL and how a DAG would look like afterwards. #### Step 1 - Create the Connection From our Airflow host, (e.g., `docker exec -it openmetadata_ingestion bash` if testing in Docker), you can run: ```bash theme={null} airflow connections add 'my_mysql_db' \ --conn-uri 'mysql+pymysql://openmetadata_user:openmetadata_password@mysql:3306/openmetadata_db' ``` You will see an output like ``` Successfully added `conn_id`=my_mysql_db : mysql+pymysql://openmetadata_user:openmetadata_password@mysql:3306/openmetadata_db ``` Checking the credentials from the Airflow UI, we will see: Airflow Connection #### Step 2 - Understanding the shape of a Connection In the same host, we can open a Python shell to explore the Connection object with some more details. To do so, we first need to pick up the connection from Airflow. We will use the `BaseHook` for that as the connection is not stored in any external system. ```python theme={null} from airflow.hooks.base import BaseHook # Retrieve the connection connection = BaseHook.get_connection("my_mysql_db") # Access the connection details connection.host # 'mysql' connection.port # 3306 connection.login # 'openmetadata_user' connection.password # 'openmetadata_password' ``` Based on this information, we now know how to prepare the DAG! #### Step 3 - Write the DAG A full example on how to write a DAG to ingest data from our Connection can look like this: ```python theme={null} import pathlib import yaml from datetime import timedelta from airflow import DAG from airflow.utils.dates import days_ago try: from airflow.operators.python import PythonOperator except ModuleNotFoundError: from airflow.operators.python_operator import PythonOperator from metadata.config.common import load_config_file from metadata.workflow.metadata import MetadataWorkflow # Import the hook from airflow.hooks.base import BaseHook # Retrieve the connection connection = BaseHook.get_connection("my_mysql_db") # Use the connection details when setting the YAML # Note how we escaped the braces as {{}} to not be parsed by the f-string config = f""" source: type: mysql serviceName: mysql_from_connection serviceConnection: config: type: Mysql username: {connection.login} password: {connection.password} hostPort: {connection.host}:{connection.port} # databaseSchema: schema sourceConfig: config: markDeletedTables: true includeTables: true includeViews: true sink: type: metadata-rest config: {{}} workflowConfig: openMetadataServerConfig: hostPort: "" authProvider: "" """ def metadata_ingestion_workflow(): workflow_config = yaml.safe_load(config) workflow = MetadataWorkflow.create(workflow_config) workflow.execute() workflow.raise_from_status() workflow.print_status() workflow.stop() with DAG( "mysql_connection_ingestion", description="An example DAG which runs a OpenMetadata ingestion workflow", start_date=days_ago(1), is_paused_upon_creation=False, schedule_interval='*/5 * * * *', catchup=False, ) as dag: ingest_task = PythonOperator( task_id="ingest_using_recipe", python_callable=metadata_ingestion_workflow, ) ``` #### Option B - Reuse an existing Service Following the explanation at the beginning of this doc, we can reuse the credentials from an existing service in a DAG as well, and just omit the `serviceConnection` YAML entries: ```python theme={null} import pathlib import yaml from datetime import timedelta from airflow import DAG from airflow.utils.dates import days_ago try: from airflow.operators.python import PythonOperator except ModuleNotFoundError: from airflow.operators.python_operator import PythonOperator from metadata.config.common import load_config_file from metadata.workflow.metadata import MetadataWorkflow config = """ source: type: mysql serviceName: existing_mysql_service sourceConfig: config: markDeletedTables: true includeTables: true includeViews: true sink: type: metadata-rest config: {} workflowConfig: openMetadataServerConfig: hostPort: "" authProvider: "" """ def metadata_ingestion_workflow(): workflow_config = yaml.safe_load(config) workflow = MetadataWorkflow.create(workflow_config) workflow.execute() workflow.raise_from_status() workflow.print_status() workflow.stop() with DAG( "mysql_connection_ingestion", description="An example DAG which runs a OpenMetadata ingestion workflow", start_date=days_ago(1), is_paused_upon_creation=False, schedule_interval='*/5 * * * *', catchup=False, ) as dag: ingest_task = PythonOperator( task_id="ingest_using_recipe", python_callable=metadata_ingestion_workflow, ) ```