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The BigQuery Data Transfer API allows users to transfer data from partner SaaS applications to Google BigQuery on a scheduled, managed basis.

Quick Start

In order to use this library, you first need to go through the following steps:

  1. Select or create a Cloud Platform project.

  2. Enable the BigQuery Data Transfer API.

  3. Setup Authentication.

Installation

Install this library in a virtualenv using pip. virtualenv is a tool to create isolated Python environments. The basic problem it addresses is one of dependencies and versions, and indirectly permissions.

With virtualenv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Supported Python Versions

Python >= 3.5

Deprecated Python Versions

Python == 2.7. Python 2.7 support will be removed on January 1, 2020.

Mac/Linux

pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-bigquery-datatransfer

Windows

pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-bigquery-datatransfer

Example Usage

DataTransferServiceClient

from google.cloud import bigquery_datatransfer_v1

client = bigquery_datatransfer_v1.DataTransferServiceClient()

parent = client.location_path('[PROJECT]', '[LOCATION]')


# Iterate over all results
for element in client.list_data_sources(parent):
    # process element
    pass

# Or iterate over results one page at a time
for page in client.list_data_sources(parent).pages:
    for element in page:
        # process element
        pass

Next Steps

Project links

Key dates

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  • Released:
Newer release available (3.23.0)

2 maintainers

PyPI data
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Avatar for gcloudpypi from gravatar.com gcloudpypi Avatar for google_opensource from gravatar.com google_opensource

Credits

Author: Google LLC

License

Apache Software License (Apache 2.0)

Requires

Python >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*

Classifiers

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