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:

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.

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 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:
    gcsCredentialsPath: <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"

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.
  • schemaFilterPattern and tableFilternPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,
    tableFilterPattern:
      includes:
        - users
        - type_test
    

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:

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

We support different security providers. You can find their definitions here. An example of an Auth0 configuration would be the following:

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: auth0
    securityConfig:
      clientId: <client ID>
      secretKey: <secret key>
      domain: <domain>

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

metadata ingest -c <path-to-yaml>

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:

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

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

To specify where the staging files will be located.

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]'

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

metadata ingest -c <path-to-yaml>

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:

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

Note that the fqnFilterPattern supports regex as includes or excludes. E.g.,

fqnFilterPattern:
    includes:
    - service.database.schema.*

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:
    test_suite:
        name: <Test Suite name>
        tests:
        - table: <Table FQN>
            table_tests:
            - testCase:
                config:
                    value: 100
                tableTestType: tableRowCountToEqual
            column_tests:
            - columnName: <Column Name>
                testCase:
                config:
                    minValue: 0
                    maxValue: 99
                columnTestType: columnValuesToBeBetween
tests is a list of test definitions that will be applied to table, informed by its FQN. For each table, one can then define a list of table_tests and column_tests. Review the supported tests and their definitions to learn how to configure the different cases here. // TODO: Link to tests

The same as the metadata ingestion.

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

metadata profile -c <path-to-yaml>

Note how instead of running ingest, we are using the profile command to select the Profiler 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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