BigQuery
Feature | Status |
---|---|
Stage | PROD |
Metadata | |
Query Usage | |
Data Profiler | |
Data Quality | |
Lineage | |
DBT | |
Supported Versions | -- |
Feature | Status |
---|---|
Lineage | |
Table-level | |
Column-level |
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:
If you don't want to use the OpenMetadata Ingestion container to configure the workflows via the UI, then you can check the following docs to connect using Airflow SDK or with the CLI.
Ingest with Airflow
Configure the ingestion using Airflow SDKIngest with the CLI
Run a one-time ingestion using the metadata CLIRequirements
OpenMetadata 0.12 or laterTo deploy OpenMetadata, check the Deployment guides.
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.
Data Catalog API Permissions
- Go to https://console.cloud.google.com/apis/library/datacatalog.googleapis.com
- Select the
GCP Project ID
that you want to enable theData Catalog API
on. - Click on
Enable API
which will enable the data catalog api on the respective project.
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 Permission | Required For |
---|---|---|
1 | bigquery.datasets.get | Metadata Ingestion |
2 | bigquery.tables.get | Metadata Ingestion |
3 | bigquery.tables.getData | Metadata Ingestion |
4 | bigquery.tables.list | Metadata Ingestion |
5 | resourcemanager.projects.get | Metadata Ingestion |
6 | bigquery.jobs.create | Metadata Ingestion |
7 | bigquery.jobs.listAll | Metadata Ingestion |
8 | datacatalog.taxonomies.get | Fetch Policy Tags |
9 | datacatalog.taxonomies.list | Fetch Policy Tags |
10 | bigquery.readsessions.create | Bigquery Usage & Lineage Workflow |
11 | bigquery.readsessions.getData | Bigquery Usage & Lineage Workflow |
Metadata Ingestion
1. Visit the Services Page
The first step is ingesting the metadata from your sources. Under Settings, you will find a Services link an external source system to OpenMetadata. Once a service is created, it can be used to configure metadata, usage, and profiler workflows.
To visit the Services page, select Services from the Settings menu.

Find Databases option on left panel of the settings page

Add a new Service from the Database Services page

Select your service from the list
4. Name and Describe your Service
Provide a name and description for your service as illustrated below.
Service Name
OpenMetadata uniquely identifies services by their Service Name. Provide a name that distinguishes your deployment from other services, including the other {connector} services that you might be ingesting metadata from.

Provide a Name and description for your Service
5. Configure the Service Connection
In this step, we will configure the connection settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your bigquery service as desired.

Configure the service connection by filling the form
Connection Options
Host and Port: BigQuery APIs URL. By default the API URL is bigquery.googleapis.com
you can modify this if you have custom implementation of BigQuery.
GCS 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.
GCS Credentials Values: Passing the raw credential values provided by BigQuery. This requires us to provide the following information, all provided by BigQuery:
- Credentials 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 thetype
key in the service account key file. - Project ID: 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. - Private Key ID: 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. - Private Key: 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. - Client Email: 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. - Client ID: 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. - Auth URI: 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. - Token URI: 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. - Authentication Provider X509 Certificate URL: 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 - Client X509Cert URL: 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.
GCS Credentials Path: Passing a local file path that contains the credentials.
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.
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.
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.
6. Test the Connection
Once the credentials have been added, click on Test Connection
and Save the changes.

Test the connection and save the Service
7. Configure Metadata Ingestion
In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

Configure Metadata Ingestion Page
Metadata Ingestion Options
Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
Database Filter Pattern (Optional): Use to database filter patterns to control whether or not to include database as part of metadata ingestion.
- Include: Explicitly include databases by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all databases with names matching one or more of the supplied regular expressions. All other databases will be excluded.
- Exclude: Explicitly exclude databases by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all databases with names matching one or more of the supplied regular expressions. All other databases will be included.
Schema Filter Pattern (Optional): Use to schema filter patterns to control whether or not to include schemas as part of metadata ingestion.
- Include: Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
- Exclude: Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.
Table Filter Pattern (Optional): Use to table filter patterns to control whether or not to include tables as part of metadata ingestion.
- Include: Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded.
- Exclude: Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included.
Include views (toggle): Set the Include views toggle to control whether or not to include views as part of metadata ingestion.
Include tags (toggle): Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.
Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug, these logs can be viewed later in Airflow.
Mark Deleted Tables (toggle): Set the Mark Deleted Tables toggle to flag tables as soft-deleted if they are not present anymore in the source system.
Mark Deleted Tables from Filter Only (toggle): Set the Mark Deleted Tables from Filter Only toggle to flag tables as soft-deleted if they are not present anymore within the filtered schema or database only. This flag is useful when you have more than one ingestion pipelines. For example if you have a schema
8. Schedule the Ingestion and Deploy
Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.
Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.
If something doesn't look right, click the Back button to return to the appropriate step and change the settings as needed.
After configuring the workflow, you can click on Deploy to create the pipeline.

Schedule the Ingestion Pipeline and Deploy
Troubleshooting
Workflow Deployment Error
If there were any errors during the workflow deployment process, the Ingestion Pipeline Entity will still be created, but no workflow will be present in the Ingestion container.
You can then edit the Ingestion Pipeline and Deploy it again.
From the Connection tab, you can also Edit the Service if needed.

Edit and Deploy the Ingestion Pipeline