Run BigQuery using the metadata CLI

hostPort: BigQuery APIs URL. By default the API URL is bigquery.googleapis.com you can modify this if you have custom implementation of BigQuery.

credentials: You can authenticate with your bigquery instance using either GCS Credentials Path where you can specify the file path of the service account key or you can pass the values directly by choosing the GCS Credentials Values from the service account key file.

You can checkout this documentation on how to create the service account keys and download it.

gcsConfig:

1. Passing the raw credential values provided by BigQuery. This requires us to provide the following information, all provided by BigQuery:

• type: Credentials Type is the type of the account, for a service account the value of this field is service_account. To fetch this key, look for the value associated with the type key in the service account key file.
• projectId: A project ID is a unique string used to differentiate your project from all others in Google Cloud. To fetch this key, look for the value associated with the project_id key in the service account key file. You can also pass multiple project id to ingest metadata from different BigQuery projects into one service.
• privateKeyId: This is a unique identifier for the private key associated with the service account. To fetch this key, look for the value associated with the private_key_id key in the service account file.
• privateKey: This is the private key associated with the service account that is used to authenticate and authorize access to BigQuery. To fetch this key, look for the value associated with the private_key key in the service account file.
• clientEmail: This is the email address associated with the service account. To fetch this key, look for the value associated with the client_email key in the service account key file.
• clientId: This is a unique identifier for the service account. To fetch this key, look for the value associated with the client_id key in the service account key file.
• authUri: This is the URI for the authorization server. To fetch this key, look for the value associated with the auth_uri key in the service account key file. The default value to Auth URI is https://accounts.google.com/o/oauth2/auth.
• tokenUri: The Google Cloud Token URI is a specific endpoint used to obtain an OAuth 2.0 access token from the Google Cloud IAM service. This token allows you to authenticate and access various Google Cloud resources and APIs that require authorization. To fetch this key, look for the value associated with the token_uri key in the service account credentials file. Default Value to Token URI is https://oauth2.googleapis.com/token.
• authProviderX509CertUrl: This is the URL of the certificate that verifies the authenticity of the authorization server. To fetch this key, look for the value associated with the auth_provider_x509_cert_url key in the service account key file. The Default value for Auth Provider X509Cert URL is https://www.googleapis.com/oauth2/v1/certs
• clientX509CertUrl: This is the URL of the certificate that verifies the authenticity of the service account. To fetch this key, look for the value associated with the client_x509_cert_url key in the service account key file.

2. Passing a local file path that contains the credentials:

• gcsCredentialsPath

Taxonomy Project ID (Optional): Bigquery uses taxonomies to create hierarchical groups of policy tags. To apply access controls to BigQuery columns, tag the columns with policy tags. Learn more about how yo can create policy tags and set up column-level access control here

If you have attached policy tags to the columns of table available in Bigquery, then OpenMetadata will fetch those tags and attach it to the respective columns.

In this field you need to specify the id of project in which the taxonomy was created.

Taxonomy Location (Optional): Bigquery uses taxonomies to create hierarchical groups of policy tags. To apply access controls to BigQuery columns, tag the columns with policy tags. Learn more about how yo can create policy tags and set up column-level access control here

If you have attached policy tags to the columns of table available in Bigquery, then OpenMetadata will fetch those tags and attach it to the respective columns.

In this field you need to specify the location/region in which the taxonomy was created.

Usage Location (Optional): Location used to query INFORMATION_SCHEMA.JOBS_BY_PROJECT to fetch usage data. You can pass multi-regions, such as us or eu, or your specific region such as us-east1. Australia and Asia multi-regions are not yet supported.

• If you prefer to pass the credentials file, you can do so as follows:

• If you want to use ADC authentication for BigQuery you can just leave the GCS credentials empty. This is why they are not marked as required.

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.

databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here

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:

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

Source Configuration - Source Config

You can find all the definitions and types for the sourceConfig here.

queryLogDuration: Configuration to tune how far we want to look back in query logs to process usage data.

stageFileLocation: Temporary file name to store the query logs before processing. Absolute file path required.

resultLimit: Configuration to set the limit for query logs

queryLogFilePath: Configuration to set the file path for query logs

Processor, Stage and Bulk Sink Configuration

To specify where the staging files will be located.

Note that the location is a directory that will be cleaned at the end of the ingestion.

Workflow Configuration

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:

Source Configuration - Source Config

You can find all the definitions and types for the sourceConfig here.

generateSampleData: Option to turn on/off generating sample data.

profileSample: Percentage of data or no. of rows we want to execute the profiler and tests on.

threadCount: Number of threads to use during metric computations.

processPiiSensitive: Optional configuration to automatically tag columns that might contain sensitive information.

confidence: Set the Confidence value for which you want the column to be marked

timeoutSeconds: Profiler Timeout in Seconds

databaseFilterPattern: Regex to only fetch databases that matches the pattern.

schemaFilterPattern: Regex to only fetch tables or databases that matches the pattern.

tableFilterPattern: Regex to only fetch tables or databases that matches the pattern.

Processor Configuration

Choose the orm-profiler. Its config can also be updated to define tests from the YAML itself instead of the UI:

tableConfig: tableConfig allows you to set up some configuration at the table level.

Sink Configuration

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

Workflow Configuration

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 ProfilerWorkflow class that is being imported is a part of a metadata orm_profiler framework, which defines a process of extracting Profiler data.

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 profiler as we prepare above.

• metadata_ingestion_workflow(): This code defines a function metadata_ingestion_workflow() that loads a YAML configuration, creates a ProfilerWorkflow 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.