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BigQuery

FeatureStatus
StagePROD
Metadata
Query Usage
Data Profiler
Data Quality
Stored Procedures
Owners
TagsPolicy Tags
DBT
Supported Versions--
FeatureStatus
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:

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. If you want to install it manually in an already existing Airflow host, you can follow this guide.

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 run the Ingestion Framework in any orchestrator externally.

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.

If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

OpenMetadata 0.12 or later

To deploy OpenMetadata, check the Deployment guides.

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 PermissionRequired For
1bigquery.datasets.getMetadata Ingestion
2bigquery.tables.getMetadata Ingestion
3bigquery.tables.getDataMetadata Ingestion
4bigquery.tables.listMetadata Ingestion
5resourcemanager.projects.getMetadata Ingestion
6bigquery.jobs.createMetadata Ingestion
7bigquery.jobs.listAllMetadata Ingestion
8bigquery.routines.getStored Procedure
9bigquery.routines.listStored Procedure
10datacatalog.taxonomies.getFetch Policy Tags
11datacatalog.taxonomies.listFetch Policy Tags
12bigquery.readsessions.createBigquery Usage & Lineage Workflow
13bigquery.readsessions.getDataBigquery Usage & Lineage Workflow

If the user has External Tables, please attach relevant permissions needed for external tables, alongwith the above list of permissions.

If you are using BigQuery and have sharded tables, you might want to consider using partitioned tables instead. Partitioned tables allow you to efficiently query data by date or other criteria, without having to manage multiple tables. Partitioned tables also have lower storage and query costs than sharded tables. You can learn more about the benefits of partitioned tables here. If you want to convert your existing sharded tables to partitioned tables, you can follow the steps in this guide. This will help you simplify your data management and optimize your performance in BigQuery.

The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first.

This Service will be the bridge between OpenMetadata and your source system.

Once a Service is created, it can be used to configure your ingestion workflows.

Visit Services Page

Select your Service Type and Add a New Service

Click on Add New Service to start the Service creation.

Create a new Service

Add a new Service from the Services page

Select BigQuery as the Service type and click Next.

Select Service

Select your Service from the list

Provide a name and description for your Service.

OpenMetadata uniquely identifies Services by their Service Name. Provide a name that distinguishes your deployment from other Services, including the other BigQuery Services that you might be ingesting metadata from.

Note that when the name is set, it cannot be changed.

Add New Service

Provide a Name and description for your Service

In this step, we will configure the connection settings required for BigQuery.

Please follow the instructions below to properly configure the Service to read from your sources. You will also find helper documentation on the right-hand side panel in the UI.

Configure Service connection

Configure the Service connection by filling the form

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.

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

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

GCP 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 the type 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.

GCP 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.

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

Advanced Configuration

Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector and, if needed, change the connection Scheme.

This would only be required to handle advanced connectivity scenarios or customizations.

  • 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 during the connection. These details must be added as Key-Value pairs.
Advanced Configuration

Advanced Configuration

Once the credentials have been added, click on Test Connection and Save the changes.

Test Connection

Test the connection and save the Service

In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

Configure Metadata Ingestion

Configure Metadata Ingestion Page

  • 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 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 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 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.
  • 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

Note that the right-hand side panel in the OpenMetadata UI will also share useful documentation when configuring the ingestion.

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 Workflow

Schedule the Ingestion Pipeline and Deploy

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.

View Ingestion Pipeline

View the Ingestion Pipeline from the Service Page

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.