OpenMetadata/openmetadata-docs/content/v1.0.0/connectors/dashboard/quicksight/airflow.md

title	slug
Run QuickSight Connector using Airflow SDK	/connectors/dashboard/quicksight/airflow

Run QuickSight using the Airflow SDK

Stage	PROD
Dashboards	{% icon iconName="check" /%}
Charts	{% icon iconName="check" /%}
Owners	{% icon iconName="cross" /%}
Tags	{% icon iconName="cross" /%}
Datamodels	{% icon iconName="cross" /%}
Lineage	{% icon iconName="check" /%}

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

Configure and schedule QuickSight metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion

Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

AWS QuickSight Permissions To execute metadata extraction and usage workflow successfully the IAM User should have enough access to fetch required data. Following table describes the minimum required permissions

#	AWS QuickSight Permission
1	DescribeDashboard
2	ListAnalyses
3	ListDataSources
4	ListDashboards
5	DescribeAnalysis
6	DescribeDataSet
7	ListDataSets
8	DescribeDataSource

Here is how to add Permissions to an IAM user.

• Navigate to the IAM console in the AWS Management Console.

• Choose the IAM user or group to which you want to attach the policy, and click on the "Permissions" tab.

• Click on the "Add permissions" button and select "Attach existing policies directly".

• Search for the policy by name or by filtering the available policies, and select the one you want to attach.

• Review the policy and click on "Add permissions" to complete the process.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "quicksight:DescribeDashboard",
                "quicksight:ListAnalyses",
                "quicksight:ListDataSources",
                "quicksight:ListDashboards",
                "quicksight:DescribeAnalysis",
                "quicksight:DescribeDataSet",
                "quicksight:ListDataSets",
                "quicksight:DescribeDataSource"
            ],
            "Resource": "*"
        }
    ]
}

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.

Python Requirements

To run the QuickSight ingestion, you will need to install:

pip3 install "openmetadata-ingestion[QuickSight]"

Metadata Ingestion

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

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 QuickSightled around the following JSON Schema

1. Define the YAML Config

This is a sample config for QuickSight:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

awsConfig

• AWS Access Key ID: Enter your secure access key ID for your Glue connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
• AWS Secret Access Key: Enter the Secret Access Key (the passcode key pair to the key ID from above).
• AWS Region: Enter the location of the amazon cluster that your data and account are associated with.
• AWS Session Token (optional): The AWS session token is an optional parameter. If you want, enter the details of your temporary session token.
• Endpoint URL (optional): Your Glue connector will automatically determine the AWS QuickSight endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

awsAccountId: AWS Account ID

{% /codeInfo %}

{% codeInfo srNumber=3 %}

identityType: The authentication method that the user uses to sign in.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

namespace: The Amazon QuickSight namespace that contains the dashboard IDs in this request ( To be provided when identityType is ANONYMOUS )

{% /codeInfo %}

Source Configuration - Source Config

{% codeInfo srNumber=5 %}

The sourceConfig is defined here:

• dbServiceNames: Database Service Names for ingesting lineage if the source supports it.
• dashboardFilterPattern, chartFilterPattern, dataModelFilterPattern: Note that all of them support regex as include or exclude. E.g., "My dashboard, My dash.*, .*Dashboard".
• includeOwners: Set the 'Include Owners' toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.
• includeTags: Set the 'Include Tags' toggle to control whether to include tags in metadata ingestion.
• includeDataModels: Set the 'Include Data Models' toggle to control whether to include tags as part of metadata ingestion.
• markDeletedDashboards: Set the 'Mark Deleted Dashboards' toggle to flag dashboards as soft-deleted if they are not present anymore in the source system.

{% /codeInfo %}

Sink Configuration

{% codeInfo srNumber=6 %}

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

{% /codeInfo %}

Workflow Configuration

{% codeInfo srNumber=7 %}

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:

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

source:
  type: quicksight
  serviceName: local_quicksight
  serviceConnection:
    config:
      type: QuickSight

      awsConfig:
        awsAccessKeyId: KEY
        awsSecretAccessKey: SECRET
        awsRegion: us-east-2
        awsSessionToken: Token

      awsAccountId: <aws-account-id>

      identityType: IAM #QUICKSIGHT, ANONYMOUS

      namespace: #to be provided if identityType is Anonymous

  sourceConfig:
    config:
      type: DashboardMetadata
      markDeletedDashboards: True
      # dbServiceNames:
      #   - service1
      #   - service2
      # dashboardFilterPattern:
      #   includes:
      #     - dashboard1
      #     - dashboard2
      #   excludes:
      #     - dashboard3
      #     - dashboard4
      # chartFilterPattern:
      #   includes:
      #     - chart1
      #     - chart2
      #   excludes:
      #     - chart3
      #     - chart4

```yaml {% srNumber=6 %}
sink:
  type: metadata-rest
  config: {}

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

{% /codeBlock %}

{% /codePreview %}

Workflow Configs for Security Provider

We support different security providers. You can find their definitions here.

Openmetadata JWT Auth

• JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.

workflowConfig:
  openMetadataServerConfig:
    hostPort: "http://localhost:8585/api"
    authProvider: openmetadata
    securityConfig:
      jwtToken: "{bot_jwt_token}"

• You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.

2. Prepare the Ingestion DAG

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

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=8 %}

Import necessary modules

The Workflow class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.

Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.

{% /codeInfo %}

{% codeInfo srNumber=9 %}

Default arguments for all tasks in the Airflow DAG.

• Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.

{% /codeInfo %}

{% codeInfo srNumber=10 %}

• config: Specifies config for the metadata ingestion as we prepare above.

{% /codeInfo %}

{% codeInfo srNumber=11 %}

• metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a Workflow object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.

{% /codeInfo %}

{% codeInfo srNumber=12 %}

• DAG: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
• For more Airflow DAGs creation details visit here.

{% /codeInfo %}

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.

{% /codeInfoContainer %}

{% codeBlock fileName="filename.py" %}

import pathlib
import yaml
from datetime import timedelta
from airflow import DAG
from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago

try:
    from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
    from airflow.operators.python_operator import PythonOperator

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,
    )

{% /codeBlock %}

{% /codePreview %}