Generate and load BigQuery tables based on Table Schema descriptors.
tableschema.Storage
interfaceThe 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:
GOOGLE_APPLICATION_CREDENTIALS
environment variableimport io
import os
import json
from datapackage import Package
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(io.open('.credentials.json', encoding='utf-8'))['project_id']
# Save package to BigQuery
package = Package('datapackage.json')
package.save(storage='bigquery', service=service, project=project, dataset='dataset')
# Load package from BigQuery
package = Package(storage='bigquery', service=service, project=project, dataset='dataset')
package.resources
Storage
Storage(self, service, project, dataset, prefix='')
BigQuery storage
Package implements Tabular Storage interface (see full documentation on the link):
Only additional API is documented
Arguments
Service
objectThe 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
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.