In this step, we’ll create a Python virtual environment. Using a virtual environment enables us to avoid conflicts with other Python installations and packages on your host system.

In a later step, you will install the Python module for this connector and its dependencies in this virtual environment.

1. Create a directory for openmetadata

Throughout the docs, we use a consistent directory structure for OpenMetadata services and connector installation. If you have not already done so by following another guide, please create an openmetadata directory now and change into that directory in your command line environment.

mkdir openmetadata; cd openmetadata

2. Create a virtual environment

Run the following command to create a Python virtual environment called, env. You can try multiple connectors in the same virtual environment.

python3 -m venv env

3. Activate the virtual environment

Run the following command to activate the virtual environment.

source env/bin/activate

Once activated, you should see your command prompt change to indicate that your commands will now be executed in the environment named env.

4. Upgrade pip and setuptools to the latest versions

Ensure that you have the latest version of pip by running the following command. If you have followed the steps above, this will upgrade pip in your virtual environment.

pip3 install --upgrade pip setuptools

2.2 Install the Python module for this connector

Once the virtual environment is set up and activated as described in Step 1, run the following command to install the Python module for the BigQuery connector.

pip3 install 'openmetadata-ingestion[bigquery]'

3. Configure BigQuery Connector

Please follow the steps relevant to your use case.

3.1 Create a configuration file using template JSON

Create a new file called bigquery.json in the current directory. Note that the current directory should be the openmetadata directory you created in Step 1.

Copy and paste the configuration template below into the bigquery.json file you created.

{% hint style="info" %} Note: The source.config field in the configuration JSON will include the majority of the settings for your connector. In the steps below we describe how to customize the key-value pairs in the source.config field to meet your needs. {% endhint %}

{% code title="bigquery.json" %}

{
  "source": {
    "type": "bigquery",
    "config": {
      "project_id": "project_id",
      "host_port": "bigquery.googleapis.com",
      "username": "username",
      "service_name": "gcp_bigquery",
      "data_profiler_enabled": "true",
      "data_profiler_offset": "0",
      "data_profiler_limit": "50000",
      "options": {
        "credentials_path": "examples/creds/bigquery-cred.json"
      },
      "table_filter_pattern": {
        "excludes": ["demo.*","orders.*"]
      },
      "schema_filter_pattern": {
        "excludes": [
          "[\\w]*cloudaudit.*",
          "[\\w]*logging_googleapis_com.*",
          "[\\w]*clouderrorreporting.*"
        ]
      }
    }
  },
  "sink": {
    "type": "metadata-rest",
    "config": {
      "api_endpoint": "http://localhost:8585/api"
    }
  },
  "metadata_server": {
    "type": "metadata-server",
    "config": {
      "api_endpoint": "http://localhost:8585/api",
      "auth_provider_type": "no-auth"
    }
  }
}

{% endcode %}

When adding the details for the credentials path, you can either choose to pass the credentials file, or add the credentials_path, or use a secure way to pass the credentials path using the environment variables, i.e., Application Default Credentials (ADC).

3.1.1 Using Credentials File or Credentials Path

{% code title="bigquery-creds.json (boilerplate)" %}

{
  "type": "service_account",
  "project_id": "project_id",
  "private_key_id": "private_key_id",
  "private_key": "",
  "client_email": "gcpuser@project_id.iam.gserviceaccount.com",
  "client_id": "",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": ""
}

{% endcode %}

3.1.2 Using Application Default Credentials (ADC)

{% code title="env variables" %}

export GOOGLE_APPLICATION_CREDENTIALS=<path-to-your-credentials-file>

{% endcode %}

Users can export the path to the credentials file. Using this option, you can export the env in terminal and run BigQuery config without providing credentials_path.

3.2 Configure service settings

In this step we will configure the BigQuery service 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.

project_id

Edit the value for source.config.project_id in bigquery.json. The project_id is a mandatory field.

   "project_id": "project_id"

host_port

Edit the value for source.config.host_port in bigquery.json for your BigQuery deployment. Use the host:port format illustrated in the example below.

"host_port": "bigquery.googleapis.com"

Please ensure that your BigQuery deployment is reachable from the host you are using to run metadata ingestion.

username

Edit the value for source.config.username to identify your BigQuery user.

"username": "username"

{% hint style="danger" %} Note: The user specified should be authorized to read all databases you want to include in the metadata ingestion workflow. {% endhint %}

password

Edit the value for source.config.password with the password for your BigQuery user.

"password": "strong_password"

service_name

OpenMetadata uniquely identifies services by their service_name. Edit the value for source.config.service_name with a name that distinguishes this deployment from other services, including other BigQuery services that you might be ingesting metadata from.

"service_name": "bigquery"

database (optional)

If you want to limit metadata ingestion to a single database, include the source.config.database field in your configuration file. If this field is not included, the connector will ingest metadata from all databases that the specified user is authorized to read.

To specify a single database to ingest metadata from, provide the name of the database as the value for the source.config.database key as illustrated in the example below.

"database": "bigquery_db"

3.3 Enable/disable the data profiler

The data profiler ingests usage information for tables. This enables you to assess the frequency of use, reliability, and other details.

data_profiler_enabled

When enabled, the data profiler will run as part of metadata ingestion. Running the data profiler increases the amount of time it takes for metadata ingestion, but provides the benefits mentioned above.

You may disable the data profiler by setting the value for the key source.config.data_profiler_enabled to "false" as follows. We’ve done this in the configuration template provided.

"data_profiler_enabled": "false"

If you want to enable the data profiler, update your configuration file as follows.

"data_profiler_enabled": "true"

{% hint style="info" %} Note: The data profiler is enabled by default if no setting is provided for data_profiler_enabled {% endhint %}

3.4 Install the data profiler Python module (optional)

If you’ve enabled the data profiler in Step 3.3, run the following command to install the Python module for the data profiler. You’ll need this to run the ingestion workflow.

pip3 install 'openmetadata-ingestion[data-profiler]'

The data profiler module takes a few minutes to install. While it installs, continue through the remaining steps in this guide.

3.5 Configure data filters (optional)

include_views (optional)

Use source.config.include_views to control whether or not to include views as part of metadata ingestion and data profiling.

Explicitly include views by adding the following key-value pair in the source.config field of your configuration file.

"include_views": "true"

Exclude views as follows.

"include_views": "false"

{% hint style="info" %} Note: source.config.include_views is set to true by default. {% endhint %}

include_tables (optional)

Use source.config.include_tables to control whether or not to include tables as part of metadata ingestion and data profiling.

Explicitly include tables by adding the following key-value pair in the source.config field of your configuration file.

"include_tables": "true"

Exclude tables as follows.

"include_tables": "false"

{% hint style="info" %} Note: source.config.include_tables is set to true by default. {% endhint %}

table_filter_pattern (optional)

Use source.config.table_filter_pattern to select tables for metadata ingestion by name.

Use source.config.table_filter_pattern.excludes to exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included. See below for an example. This example is also included in the configuration template provided.

"table_filter_pattern": {
"excludes": ["information_schema.*", "[\\w]*event_vw.*"]
}

Use source.config.table_filter_pattern.includes to include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded. See below for an example.

"table_filter_pattern": {
"includes": ["corp.*", "dept.*"]
}

See the documentation for the Python re module for information on how to construct regular expressions.

{% hint style="info" %} You may use either excludes or includes but not both in table_filter_pattern. {% endhint %}

schema_filter_pattern (optional)

Use source.config.schema_filter_pattern.excludes and source.config.schema_filter_pattern.includes field to select the schemas for metadata ingestion by name. The configuration template provides an example.

The syntax and semantics for schema_filter_pattern are the same as for table_filter_pattern. Please check that section for details.

3.6 Configure sample data (optional)

generate_sample_data (optional)

Use the source.config.generate_sample_data field to control whether or not to generate sample data to include in table views in the OpenMetadata user interface. The image below provides an example.

Explicitly include sample data by adding the following key-value pair in the source.config field of your configuration file.

"generate_sample_data": "true"

If set to true, the connector will collect the first 50 rows of data from each table included in ingestion, and catalog that data as sample data, which users can refer to in the OpenMetadata user interface.

You can exclude the collection of sample data by adding the following key-value pair in the source.config field of your configuration file.

"generate_sample_data": "false"

{% hint style="info" %} Note: generate_sample_data is set to true by default. {% endhint %}

3.7 Configure DBT (optional)

DBT provides transformation logic that creates tables and views from raw data. OpenMetadata’s integration for DBT enables you to view the models used to generate a table from that table's details page in the OpenMetadata UI. The image below provides an example.

To include DBT models and metadata in your ingestion workflows, specify the location of the DBT manifest and catalog files as fields in your configuration file.

dbt_manifest_file (optional)

Use the field source.config.dbt_manifest_file to specify the location of your DBT manifest file. See below for an example.

"dbt_manifest_file": "./dbt/manifest.json"

dbt_catalog_file (optional)

Use the field source.config.dbt_catalog_file to specify the location of your DBT catalog file. See below for an example.

"dbt_catalog_file": "./dbt/catalog.json"

3.8 Confirm sink settings

You need not make any changes to the fields defined for sink in the template code you copied into bigquery.json in Step 4. This part of your configuration file should be as follows.

"sink": {
    "type": "metadata-rest",
    "config": {}
},

3.9 Confirm metadata_server settings

You need not make any changes to the fields defined for metadata_server in the template code you copied into bigquery.json in Step 4. This part of your configuration file should be as follows.

"metadata_server": {
    "type": "metadata-server",
    "config": {
        "api_endpoint": "http://localhost:8585/api",
        "auth_provider_type": "no-auth"
    }
}

4. Run BigQuery Connector

Your bigquery.json configuration file should now be fully configured and ready to use in an ingestion workflow.

To run an ingestion workflow, execute the following command from the openmetadata directory you created in Step 1.

metadata ingest -c ./bigquery.json

Setup BigQuery connector in production (optional)

If you already have a production Airflow instance on which you would like to schedule OpenMetadata ingestion workflows, follow the procedure Ingest Metadata in Production.

Next Steps

To view the metadata ingested from BigQuery, visit http://localhost:8585/explore/tables. Select the BigQuery service to filter for the data you’ve ingested using the workflow you configured and ran following this guide.

5. Troubleshooting

ERROR: Failed building wheel for cryptography

When attempting to install the openmetadata-ingestion[bigquery] Python package in Step 2, you might encounter the following error. The error might include a mention of a Rust compiler.

Failed to build cryptography
ERROR: Could not build wheels for cryptography which use PEP 517 and cannot be installed directly

pip3 install --upgrade pip setuptools

Then re-run the install command in Step 2.

requests.exceptions.ConnectionError

If you encounter the following error when attempting to run the ingestion workflow, this is probably because there is no OpenMetadata server running at http://localhost:8585.

requests.exceptions.ConnectionError: HTTPConnectionPool(host='localhost', port=8585): 
Max retries exceeded with url: /api/v1/services/databaseServices/name/bigquery 
(Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1031fa310>: 
Failed to establish a new connection: [Errno 61] Connection refused'))

To correct this problem, follow the procedure Try OpenMetadata in Docker to deploy OpenMetadata.

Then re-run the metadata ingestion workflow in Run BigQuery Connector

README.md Unescape Escape

BigQuery

1. Requirements

OpenMetadata (version 0.9.0 or later)

Python (version 3.8.0 or later)

2. Install BigQuery Connector

2.1 Prepare a Python virtual environment

1. Create a directory for openmetadata

2. Create a virtual environment

3. Activate the virtual environment

4. Upgrade pip and setuptools to the latest versions

2.2 Install the Python module for this connector

3. Configure BigQuery Connector

3.1 Create a configuration file using template JSON

3.1.1 Using Credentials File or Credentials Path

3.1.2 Using Application Default Credentials (ADC)

3.2 Configure service settings

project_id

host_port

username

password

service_name

database (optional)

3.3 Enable/disable the data profiler

data_profiler_enabled

3.4 Install the data profiler Python module (optional)

3.5 Configure data filters (optional)

include_views (optional)

include_tables (optional)

table_filter_pattern (optional)

schema_filter_pattern (optional)

3.6 Configure sample data (optional)

generate_sample_data (optional)

3.7 Configure DBT (optional)

dbt_manifest_file (optional)

dbt_catalog_file (optional)

3.8 Confirm sink settings

3.9 Confirm metadata_server settings

4. Run BigQuery Connector

Setup BigQuery connector in production (optional)

Next Steps

5. Troubleshooting

ERROR: Failed building wheel for cryptography

requests.exceptions.ConnectionError

README.md