Run Superset using the Airflow SDK

hostPort: The Host and Post parameter is common for all three modes of authentication which specifies the host and port of the Superset instance. This should be specified as a string in the format http://hostname:port or https://hostname:port. For example, you might set the hostPort parameter to https://org.superset.com:8088.

connection: Add the connection details to fetch metadata from Superset either through APIs or Database.

For Superset API Connection:

Superset API connection is the default mode of authentication where we fetch the metadata using Superset APIs.

Note: Superset only supports basic or ldap authentication through APIs so if you have SSO enabled on your Superset instance then this mode of authentication will not work for you and you can opt for MySQL or Postgres Connection to fetch metadata directly from the database in the backend of Superset.

username: Username to connect to Superset, for ex. user@organization.com. This user should have access to relevant dashboards and charts in Superset to fetch the metadata.

password: Password of the user account to connect with Superset.

provider: Choose between db(default) or ldap mode of Authentication provider for the Superset service. This parameter is used internally to connect to Superset's REST API.

For MySQL Connection:

You can use Mysql Connection when you have SSO enabled and your Superset is backed by Mysql database.

username: Specify the User to connect to MySQL. It should have enough privileges to read all the metadata. Make sure the user has select privileges on dashboards, tables & slices tables of superset schema.

password: Password to connect to MySQL.

hostPort: Enter the fully qualified hostname and port number for your MySQL deployment in the Host and Port field.

• databaseSchema: Enter the database schema which is associated with the Superset instance..

Connection Options (Optional): Enter the details for any additional connection options that can be sent to MySQL 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 MySQL 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"

For Postgres Connection:

You can use Postgres Connection when you have SSO enabled and your Superset is backed by Postgres database.

• username: Specify the User to connect to Postgres. Make sure the user has select privileges on dashboards, tables & slices tables of superset schema.

password: Password to connect to Postgres.

hostPort: Enter the fully qualified hostname and port number for your Postgres deployment in the Host and Port field.

• database: Initial Postgres database to connect to. Specify the name of database associated with Superset instance.

Connection Options (Optional): Enter the details for any additional connection options that can be sent to Postgres 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 Postgres 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:

• 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.

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:

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.

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.

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

• 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.

• 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.