Generate BigQuery tables, load and extract data, based on JSON Table Schema descriptors.
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Latest commit aec6f05 Oct 1, 2017


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Generate and load BigQuery tables based on Table Schema descriptors.


  • implements tableschema.Storage interface

Getting Started


The package use semantic versioning. It means that major versions could include breaking changes. It's highly recommended to specify package version range in your setup/requirements file e.g. package>=1.0,<2.0.

pip install tableschema-bigquery

To start using Google BigQuery service:

  • Create a new project - link
  • Create a service key - link
  • Download json credentials and set GOOGLE_APPLICATION_CREDENTIALS environment variable


Code examples in this readme requires Python 3.3+ interpreter. You could see even more example in examples directory.

import io
import os
import json
from tableschema import Table
from apiclient.discovery import build
from oauth2client.client import GoogleCredentials

# Prepare BigQuery credentials
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '.credentials.json'
credentials = GoogleCredentials.get_application_default()
service = build('bigquery', 'v2', credentials=credentials)
project = json.load('.credentials.json', encoding='utf-8'))['project_id']

# Load and save table to BigQuery
table = Table('data.csv', schema='schema.json')'data', storage='bigquery', service=service, project=project, dataset='dataset')


The whole public API of this package is described here and follows semantic versioning rules. Everyting outside of this readme are private API and could be changed without any notification on any new version.


Package implements Tabular Storage interface (see full documentation on the link):


This driver provides an additional API:

Storage(service, project, dataset, prefix='')

  • service (object) - BigQuery Service object
  • project (str) - BigQuery project name
  • dataset (str) - BigQuery dataset name
  • prefix (str) - prefix for all buckets


The project follows the Open Knowledge International coding standards.

Recommended way to get started is to create and activate a project virtual environment. To install package and development dependencies into active environment:

$ make install

To run tests with linting and coverage:

$ make test

For linting pylama configured in pylama.ini is used. On this stage it's already installed into your environment and could be used separately with more fine-grained control as described in documentation -

For example to sort results by error type:

$ pylama --sort <path>

For testing tox configured in tox.ini is used. It's already installed into your environment and could be used separately with more fine-grained control as described in documentation -

For example to check subset of tests against Python 2 environment with increased verbosity. All positional arguments and options after -- will be passed to py.test:

tox -e py27 -- -v tests/<path>

Under the hood tox uses pytest configured in pytest.ini, coverage and mock packages. This packages are available only in tox envionments.


Here described only breaking and the most important changes. The full changelog and documentation for all released versions could be found in nicely formatted commit history.


Initial driver implementation.