mirror of https://github.com/open-metadata/OpenMetadata.git synced 2025-07-13 12:08:47 +00:00

Suresh Srinivas 758c976cba

Fixes #9259 Change Tags APIs to conform with rest of the APIs (#9260 )

2022-12-26 12:32:17 -08:00

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title	slug
Run BigQuery Connector using the CLI	/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
Metadata Ingestion
Query Usage and Lineage Ingestion
Data Profiler
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:

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 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 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

1. Define the YAML Config

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 # ["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: "<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:
  - 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:

credentials:
  gcsConfig: <path to file>

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 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: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

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

Openmetadata JWT Auth

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

Auth0 SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Azure SSO

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

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Google SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: google
    securityConfig:
      secretKey: '{path-to-json-creds}'

Okta SSO

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

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

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

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:

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.

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:

source:
  type: bigquery-usage
  serviceName: <service name>
  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: <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.

2. Run with the CLI

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>

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:

source:
  type: bigquery
  serviceName: <service name>
  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: <table fqn>
  #     profileSample: <number between 0 and 99> # default will be 100 if omitted
  #     profileQuery: <query to use for sampling data for the profiler>
  #     columnConfig:
  #       excludeColumns:
  #         - <column name>
  #       includeColumns:
  #         - columnName: <column name>
  #         - metrics:
  #           - MEAN
  #           - MEDIAN
  #           - ...
  #     partitionConfig:
  #       enablePartitioning: <set to true to use partitioning>
  #       partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
  #       partitionInterval: <partition interval>
  #       partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>
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>
        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>
        columnConfig:
          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.

2. Run with the CLI

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.

dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage here.

21 KiB Raw Blame History

Run BigQuery using the metadata CLI

Requirements

Python Requirements

GCP Permissions

Metadata Ingestion

1. Define the YAML Config

Source Configuration - Service Connection

Source Configuration - Source Config

Sink Configuration

Workflow Configuration

Openmetadata JWT Auth

Auth0 SSO

Azure SSO

Custom OIDC SSO

Google SSO

Okta SSO

Amazon Cognito SSO

OneLogin SSO

KeyCloak SSO

2. Run with the CLI

Query Usage and Lineage Ingestion

1. Define the YAML Config

Source Configuration - Service Connection

Source Configuration - Source Config

Processor, Stage and Bulk Sink

Workflow Configuration

2. Run with the CLI

Data Profiler

1. Define the YAML Config

Source Configuration

Processor

Workflow Configuration

2. Run with the CLI

dbt Integration

21 KiB

Raw Blame History