--- title: Run BigQuery Connector using the CLI slug: /connectors/database/bigquery/cli --- # Run BigQuery using the metadata CLI 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: - [Requirements](#requirements) - [Metadata Ingestion](#metadata-ingestion) - [Query Usage and Lineage Ingestion](#query-usage-and-lineage-ingestion) - [Data Profiler](#data-profiler) - [dbt Integration](#dbt-integration) ## Requirements To 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. ### Python Requirements To run the BigQuery ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[bigquery]" ``` If you want to run the Usage Connector, you'll also need to install: ```bash 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 Permission | GCP Role | Required For | | :---------- | :---------- | :---------- | :---------- | | 1 | bigquery.datasets.get | BigQuery Data Viewer | Metadata Ingestion | | 2 | bigquery.tables.get | BigQuery Data Viewer | Metadata Ingestion | | 3 | bigquery.tables.getData | BigQuery Data Viewer | Metadata Ingestion | | 4 | bigquery.tables.list | BigQuery Data Viewer | Metadata Ingestion | | 5 | resourcemanager.projects.get | BigQuery Data Viewer | Metadata Ingestion | | 6 | bigquery.jobs.create | BigQuery Job User | Metadata Ingestion | | 7 | bigquery.jobs.listAll | BigQuery Job User | Metadata Ingestion | | 8 | datacatalog.taxonomies.get | BigQuery Policy Admin | Fetch Policy Tags | | 9 | datacatalog.taxonomies.list | BigQuery Policy Admin | Fetch Policy Tags | | 10 | bigquery.readsessions.create | BigQuery Admin | Bigquery Usage Workflow | | 11 | bigquery.readsessions.getData | BigQuery Admin | Bigquery Usage Workflow | ## Metadata Ingestion All connectors are defined as JSON Schemas. [Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/bigQueryConnection.json) 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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json) ### 1. Define the YAML Config This is a sample config for BigQuery: ```yaml source: type: bigquery serviceName: "" serviceConnection: config: type: BigQuery credentials: gcsConfig: type: My Type projectId: project ID # ["project-id-1", "project-id-2"] 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: "" authProvider: "" ``` #### 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: - **type**, e.g., `service_account` - **projectId** - **privateKey** - **privateKeyId** - **clientEmail** - **clientId** - **authUri**, https://accounts.google.com/o/oauth2/auth by defaul - **tokenUri**, https://oauth2.googleapis.com/token by default - **authProviderX509CertUrl**, https://www.googleapis.com/oauth2/v1/certs by default - **clientX509CertUrl** 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: ```yaml credentials: gcsConfig: ``` - **Enable Policy Tag Import (Optional)**: Mark as 'True' to enable importing policy tags from BigQuery to OpenMetadata. - **Classification Name (Optional)**: If the Tag import is enabled, the name of the Classification 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](https://cloud.google.com/docs/authentication#adc) for BigQuery you can just leave the GCS credentials empty. This is why they are not marked as required. ```yaml ... config: type: BigQuery credentials: gcsConfig: {} ... ``` #### Source Configuration - Source Config The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json): - `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., ```yaml 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: ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client). You can find the different implementation of the ingestion below. ### Openmetadata JWT Auth ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` ### Auth0 SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Azure SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: azure securityConfig: clientSecret: '{your_client_secret}' authority: '{your_authority_url}' clientId: '{your_client_id}' scopes: - your_scopes ``` ### Custom OIDC SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Google SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: google securityConfig: secretKey: '{path-to-json-creds}' ``` ### Okta SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: http://localhost:8585/api authProvider: okta securityConfig: clientId: "{CLIENT_ID - SPA APP}" orgURL: "{ISSUER_URL}/v1/token" privateKey: "{public/private keypair}" email: "{email}" scopes: - token ``` ### Amazon Cognito SSO The ingestion can be configured by [Enabling JWT Tokens](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens) ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### OneLogin SSO Which uses Custom OIDC for the ingestion ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### KeyCloak SSO Which uses Custom OIDC for the ingestion ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### 2. Run with the CLI First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: ```bash metadata ingest -c ``` 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. ## Query Usage and Lineage Ingestion 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. ### 1. Define the YAML Config This is a sample config for BigQuery Usage: ```yaml source: type: bigquery-usage serviceName: serviceConnection: config: type: BigQuery credentials: gcsConfig: type: My Type projectId: project ID # ["project-id-1", "project-id-2"] 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: # 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: authProvider: ``` #### Source Configuration - Service Connection You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/bigQueryConnection.json). They are the same as metadata ingestion. #### Source Configuration - Source Config The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceQueryUsagePipeline.json). - `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. ### 2. Run with the CLI There is an extra requirement to run the Usage pipelines. You will need to install: ```bash pip3 install --upgrade 'openmetadata-ingestion[bigquery-usage]' ``` After saving the YAML config, we will run the command the same way we did for the metadata ingestion: ```bash metadata ingest -c ``` ## Data Profiler 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. ### 1. Define the YAML Config This is a sample config for the profiler: ```yaml source: type: bigquery serviceName: serviceConnection: config: type: BigQuery credentials: gcsConfig: type: My Type projectId: project ID # ["project-id-1", "project-id-2"] 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: # profileSample: # default will be 100 if omitted # profileQuery: # columnConfig: # excludeColumns: # - # includeColumns: # - columnName: # - metrics: # - MEAN # - MEDIAN # - ... # partitionConfig: # enablePartitioning: # partitionColumnName: # partitionInterval: # partitionIntervalUnit: sink: type: metadata-rest config: {} workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` #### Source Configuration - You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/bigQueryConnection.json). - The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json). Note that the filter patterns support regex as includes or excludes. E.g., ```yaml 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: ```yaml processor: type: orm-profiler config: tableConfig: - fullyQualifiedName:
profileSample: partitionConfig: partitionField: partitionQueryDuration: partitionValues: profileQuery: columnConfig: excludeColumns: - includeColumns: - columnName: - 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](https://docs.open-metadata.org/openmetadata/ingestion/workflows/profiler/metrics) #### Workflow Configuration The same as the metadata ingestion. ### 2. Run with the CLI After saving the YAML config, we will run the command the same way we did for the metadata ingestion: ```bash metadata profile -c ``` Note how instead of running `ingest`, we are using the `profile` command to select the Profiler workflow. ## dbt Integration You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).