Run BigQuery using the Airflow SDK

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

Configure and schedule BigQuery 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.

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

pip3 install "openmetadata-ingestion[bigquery]"

If you want to run the Usage Connector, you'll also need to install:

pip3 install "openmetadata-ingestion[bigquery-usage]"

GCP Permissions

To execute metadata extraction and usage workflow successfully the user or the service account should have enough access to fetch required data. Following table describes the minimum required permissions

#GCP PermissionGCP RoleRequired For
1bigquery.datasets.getBigQuery Data ViewerMetadata Ingestion
2bigquery.tables.getBigQuery Data ViewerMetadata Ingestion
3bigquery.tables.getDataBigQuery Data ViewerMetadata Ingestion
4bigquery.tables.listBigQuery Data ViewerMetadata Ingestion
5resourcemanager.projects.getBigQuery Data ViewerMetadata Ingestion
6bigquery.jobs.createBigQuery Job UserMetadata Ingestion
7bigquery.jobs.listAllBigQuery Job UserMetadata Ingestion
8datacatalog.taxonomies.getBigQuery Policy AdminFetch Policy Tags
9datacatalog.taxonomies.listBigQuery Policy AdminFetch Policy Tags
10bigquery.readsessions.createBigQuery AdminBigquery Usage Workflow
11bigquery.readsessions.getDataBigQuery AdminBigquery Usage Workflow

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

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 BigQuery:

source:
  type: bigquery
  serviceName: "<service name>"
  serviceConnection:
    config:
      type: BigQuery
      credentials:
        gcsConfig:
          type: My Type
          projectId: project ID
          privateKeyId: us-east-2
          privateKey: |
            -----BEGIN PRIVATE KEY-----
            Super secret key
            -----END PRIVATE KEY-----
          clientEmail: client@mail.com
          clientId: 1234
          # authUri: https://accounts.google.com/o/oauth2/auth (default)
          # tokenUri: https://oauth2.googleapis.com/token (default)
          # authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
          clientX509CertUrl: https://cert.url
  sourceConfig:
    config:
      markDeletedTables: true
      includeTables: true
      includeViews: true
      # includeTags: true
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
      # For DBT, choose one of Cloud, Local, HTTP, S3 or GCS configurations
      # dbtConfigSource:
      # # For cloud
      #   dbtCloudAuthToken: token
      #   dbtCloudAccountId: ID
      # # For Local
      #   dbtCatalogFilePath: path-to-catalog.json
      #   dbtManifestFilePath: path-to-manifest.json
      # # For HTTP
      #   dbtCatalogHttpPath: http://path-to-catalog.json
      #   dbtManifestHttpPath: http://path-to-manifest.json
      # # For S3
      #   dbtSecurityConfig:  # These are modeled after all AWS credentials
      #     awsAccessKeyId: KEY
      #     awsSecretAccessKey: SECRET
      #     awsRegion: us-east-2
      #   dbtPrefixConfig:
      #     dbtBucketName: bucket
      #     dbtObjectPrefix: "dbt/"
      # # For GCS
      #   dbtSecurityConfig:  # These are modeled after all GCS credentials
      #     type: My Type
      #     projectId: project ID
      #     privateKeyId: us-east-2
      #     privateKey: |
      #       -----BEGIN PRIVATE KEY-----
      #       Super secret key
      #       -----END PRIVATE KEY-----
      #     clientEmail: client@mail.com
      #     clientId: 1234
      #     authUri: https://accounts.google.com/o/oauth2/auth (default)
      #     tokenUri: https://oauth2.googleapis.com/token (default)
      #     authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
      #     clientX509CertUrl: https://cert.url (URI)
      #   dbtPrefixConfig:
      #     dbtBucketName: bucket
      #     dbtObjectPrefix: "dbt/"
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: "<OpenMetadata host and port>"
    authProvider: "<OpenMetadata auth provider>"

Source Configuration - Service Connection

  • hostPort: This is the BigQuery APIs URL.
  • username: (Optional) Specify the User to connect to BigQuery. It should have enough privileges to read all the metadata.
  • projectID: (Optional) The BigQuery Project ID is required only if the credentials path is being used instead of values.
  • credentials: We support two ways of authenticating to BigQuery inside gcsConfig
    1. Passing the raw credential values provided by BigQuery. This requires us to provide the following information, all provided by BigQuery:
    2. Passing a local file path that contains the credentials:
      • gcsCredentialsPath

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

credentials:
  gcsConfig: <path to file>
  • Enable Policy Tag Import (Optional): Mark as 'True' to enable importing policy tags from BigQuery to OpenMetadata.
  • Tag Category Name (Optional): If the Tag import is enabled, the name of the Tag Category will be created at OpenMetadata.
  • Database (Optional): The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases.
  • Connection Options (Optional): Enter the details for any additional connection options that can be sent to BigQuery 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 BigQuery 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"

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.

...
  config:
    type: BigQuery
    credentials:
      gcsConfig: {}
...

Source Configuration - Source Config

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 they support regex as include or exclude. E.g.,
tableFilterPattern:
  includes:
    - users
    - type_test

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:

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

We support different security providers. You can find their definitions here. You can find the different implementation of the ingestion below.

chevron_rightConfigure SSO in the Ingestion Workflows

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.

To ingest the Query Usage and Lineage information, the serviceConnection configuration will remain the same. However, the sourceConfig is now modeled after this JSON Schema.

This is a sample config for BigQuery Usage:

source:
  type: bigquery-usage
  serviceName: <service name>
  serviceConnection:
    config:
      type: BigQuery
      credentials:
        gcsConfig:
          type: My Type
          projectId: project ID
          privateKeyId: us-east-2
          privateKey: |
            -----BEGIN PRIVATE KEY-----
            Super secret key
            -----END PRIVATE KEY-----
          clientEmail: client@mail.com
          clientId: 1234
          # authUri: https://accounts.google.com/o/oauth2/auth (default)
          # tokenUri: https://oauth2.googleapis.com/token (default)
          # authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
          clientX509CertUrl: https://cert.url
  sourceConfig:
    config:
      # Number of days to look back
      queryLogDuration: 7
      # This is a directory that will be DELETED after the usage runs
      stageFileLocation: <path to store the stage file>
      # resultLimit: 1000
      # If instead of getting the query logs from the database we want to pass a file with the queries
      # queryLogFilePath: path-to-file
processor:
  type: query-parser
  config: {}
stage:
  type: table-usage
  config:
    filename: /tmp/bigquery_usage
bulkSink:
  type: metadata-usage
  config:
    filename: /tmp/bigquery_usage
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

Source Configuration - Service Connection

You can find all the definitions and types for the serviceConnection here. They are the same as metadata ingestion.

Source Configuration - Source Config

The sourceConfig is defined here.

  • queryLogDuration: Configuration to tune how far we want to look back in query logs to process usage data.
  • resultLimit: Configuration to set the limit for query logs

Processor, Stage and Bulk Sink

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 same as the metadata ingestion.

There is an extra requirement to run the Usage pipelines. You will need to install:

pip3 install --upgrade 'openmetadata-ingestion[bigquery-usage]'

For the usage workflow creation, the Airflow file will look the same as for the metadata ingestion. Updating the YAML configuration will be enough.

The Data Profiler workflow will be using the orm-profiler processor. While the serviceConnection will still be the same to reach the source system, the sourceConfig will be updated from previous configurations.

This is a sample config for the profiler:

source:
  type: bigquery
  serviceName: <service name>
  serviceConnection:
    config:
      type: BigQuery
      credentials:
        gcsConfig:
          type: My Type
          projectId: project ID
          privateKeyId: us-east-2
          privateKey: |
            -----BEGIN PRIVATE KEY-----
            Super secret key
            -----END PRIVATE KEY-----
          clientEmail: client@mail.com
          clientId: 1234
          # authUri: https://accounts.google.com/o/oauth2/auth (default)
          # tokenUri: https://oauth2.googleapis.com/token (default)
          # authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
          clientX509CertUrl: https://cert.url
  sourceConfig:
    config:
      type: Profiler
      # generateSampleData: true
      # profileSample: 85
      # threadCount: 5 (default)
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
processor:
  type: orm-profiler
  config: {}  # Remove braces if adding properties
  # tableConfig:
  #   - fullyQualifiedName: <table fqn>
  #     profileSample: <number between 0 and 99>
  #     columnConfig:
  #       profileQuery: <query to use for sampling data for the profiler>
  #       excludeColumns:
  #         - <column name>
  #       includeColumns:
  #         - columnName: <column name>
  #         - metrics:
  #           - MEAN
  #           - MEDIAN
  #           - ...
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

Source Configuration

  • You can find all the definitions and types for the serviceConnection here.
  • The sourceConfig is defined here.

Note that the filter patterns support regex as includes or excludes. E.g.,

tableFilterPattern:
  includes:
  - *users$

Processor

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

processor:
  type: orm-profiler
  config:
    tableConfig:
      - fullyQualifiedName: <table fqn>
        profileSample: <number between 0 and 99>
        columnConfig:
          partitionConfig:
            partitionField: <field to use as a partition field>
            partitionQueryDuration: <for date/datetime partitioning based set the offset from today>
            partitionValues: <values to uses as a predicate for the query>
          profileQuery: <query to use for sampling data for the profiler>
          excludeColumns:
            - <column name>
          includeColumns:
            - columnName: <column name>
            - metrics:
                - MEAN
                - MEDIAN
                - ...

tableConfig allows you to set up some configuration at the table level. All the properties are optional. metrics should be one of the metrics listed here

Workflow Configuration

The same as the metadata ingestion.

Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:

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 airflow.utils.dates import days_ago

from metadata.orm_profiler.api.workflow import ProfilerWorkflow


default_args = {
   "owner": "user_name",
   "email_on_failure": False,
   "retries": 3,
   "retry_delay": timedelta(seconds=10),
   "execution_timeout": timedelta(minutes=60),
}

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

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

with DAG(
   "profiler_example",
   default_args=default_args,
   description="An example DAG which runs a OpenMetadata ingestion workflow",
   start_date=days_ago(1),
   is_paused_upon_creation=False,
   catchup=False,
) as dag:
   ingest_task = PythonOperator(
       task_id="profile_and_test_using_recipe",
       python_callable=metadata_ingestion_workflow,
   )

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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