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Generate BigQuery tables, load and extract data, based on JSON Table Schema descriptors.

Project description


Generate and load BigQuery tables based on JSON Table Schema descriptors.

Version v0.3 contains breaking changes:
  • renamed Storage.tables to Storage.buckets
  • changed to read into memory
  • added Storage.iter to yield row by row

Getting Started


pip install jsontableschema-bigquery


Package implements Tabular Storage interface.

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

We can get storage this way:

import io
import os
import json
from apiclient.discovery import build
from oauth2client.client import GoogleCredentials
from jsontableschema_bigquery import Storage

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

Then we could interact with storage:

storage.create('bucket', descriptor)
storage.describe('bucket') # return descriptor
storage.iter('bucket') # yields rows'bucket') # return rows
storage.write('bucket', rows)


schema.json -> bigquery table schema
data.csv -> bigquery talbe data


Default Google BigQuery client is used - docs.


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