Run DynamoDB using Airflow SDK

In this section, we provide guides and references to use the DynamoDB connector.


Configure and schedule DynamoDB 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.

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

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 DynamoDB:

Source Configuration - Service Connection

  • awsAccessKeyId: Enter your secure access key ID for your DynamoDB connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
  • awsSecretAccessKey: Enter the Secret Access Key (the passcode key pair to the key ID from above).
  • awsRegion: Enter the location of the amazon cluster that your data and account are associated with.
  • awsSessionToken: The AWS session token is an optional parameter. If you want, enter the details of your temporary session token.
  • endPointURL: Your DynamoDB connector will automatically determine the AWS DynamoDB endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL.
  • Connection Options (Optional): Enter the details for any additional connection options that can be sent to DynamoDB during the connection. These details must be added as Key-Value pairs.
  • Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent to DynamoDB during the connection. These details must be added as Key-Value pairs.
    • In case you are using Single-Sign-On (SSO) for authentication, add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "sso_login_url"
    • In case you authenticate with SSO using an external browser popup, then add the authenticator details in the Connection Arguments as a Key-Value pair as follows: "authenticator" : "externalbrowser"

The sourceConfig is defined here:

  • markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
  • includeTables: true or false, to ingest table data. Default is true.
  • includeViews: true or false, to ingest views definitions.
  • schemaFilterPattern and tableFilternPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,
    tableFilterPattern:
      includes:
        - users
        - type_test
    

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: no-auth

We support different security providers. You can find their definitions here. An example of an Auth0 configuration would be the following:

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: auth0
    securityConfig:
      clientId: <client ID>
      secretKey: <secret key>
      domain: <domain>

Create a Python file in your Airflow DAGs directory with the following contents:

import pathlib
import yaml
from datetime import timedelta
from airflow import DAG

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.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago

default_args = {
    "owner": "user_name",
    "email": ["username@org.com"],
    "email_on_failure": False,
    "retries": 3,
    "retry_delay": timedelta(minutes=5),
    "execution_timeout": timedelta(minutes=60)
}

config = """
<your YAML configuration>
"""

def metadata_ingestion_workflow():
    workflow_config = yaml.safe_load(config)
    workflow = Workflow.create(workflow_config)
    workflow.execute()
    workflow.raise_from_status()
    workflow.print_status()
    workflow.stop()

with DAG(
    "sample_data",
    default_args=default_args,
    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,
    )

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.

You can learn more about how to ingest DBT models' definitions and their lineage here.

Still have questions?

You can take a look at our Q&A or reach out to us in Slack

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