Skip to main content

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

Project description

# jsontableschema-bigquery-py

[![Travis](https://img.shields.io/travis/frictionlessdata/jsontableschema-bigquery-py.svg)](https://travis-ci.org/frictionlessdata/jsontableschema-bigquery-py)
[![Coveralls](http://img.shields.io/coveralls/frictionlessdata/jsontableschema-bigquery-py.svg?branch=master)](https://coveralls.io/r/frictionlessdata/jsontableschema-bigquery-py?branch=master)
[![PyPi](https://img.shields.io/pypi/v/jsontableschema-bigquery.svg)](https://pypi-hypernode.com/pypi/jsontableschema-bigquery)
[![Gitter](https://img.shields.io/gitter/room/frictionlessdata/chat.svg)](https://gitter.im/frictionlessdata/chat)

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

## Tabular Storage

Package implements [Tabular Storage](https://github.com/okfn/datapackage-storage-py#tabular-storage) interface.

To start using Google BigQuery service:
- Create a new project - [link](https://console.developers.google.com/home/dashboard)
- Create a service key - [link](https://console.developers.google.com/apis/credentials)
- Download json credentials and set `GOOGLE_APPLICATION_CREDENTIALS` environment variable

We can get storage this way:

```python
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(io.open('.credentials.json', encoding='utf-8'))['project_id']
storage = Storage(service, project, 'dataset', prefix='prefix')
```

Then we could interact with storage:

```python
storage.tables
storage.check('table_name') # check existence
storage.create('table_name', schema)
storage.delete('table_name')
storage.describe('table_name') # return schema
storage.read('table_name') # return data
storage.write('table_name', data)
```

## Mappings

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

## Drivers

Default Google BigQuery client is used - [docs](https://developers.google.com/resources/api-libraries/documentation/bigquery/v2/python/latest/).

## Documentation

API documentation is presented as docstings:
- [Storage](https://github.com/frictionlessdata/jsontableschema-bigquery-py/blob/master/jsontableschema_bigquery/storage.py)

## Contributing

Please read the contribution guideline:

[How to Contribute](CONTRIBUTING.md)

Thanks!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jsontableschema-bigquery-0.1.2.tar.gz (7.9 kB view details)

Uploaded Source

File details

Details for the file jsontableschema-bigquery-0.1.2.tar.gz.

File metadata

File hashes

Hashes for jsontableschema-bigquery-0.1.2.tar.gz
Algorithm Hash digest
SHA256 8e92fb9f886dd5832781e9d54708118ff5f079571df30dce6fbcaec140a7a21c
MD5 619a31ec6e985b42f780c77bd841083b
BLAKE2b-256 8f785df299f34c61ffadf3a5057bef6279db072c3fb74d8872cb689583651ea3

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page