Getting Started

Stormware provides a unified interface to various external APIs via simple but powerful connectors. It makes data engineering and data science work fast, efficient and enjoyable.

Using the Connectors

Let’s say we need to load the results from our latest Facebook campaign into a Google Sheets spreadsheet. We install Stormware with the appropriate connectors from pypi:

pip install stormware[google,facebook]

We go through the authentication configuration steps for Google and Facebook, and when done, we load the data into a data frame using the Facebook Ads connector:

from stormware.facebook import FacebookAds

facebook = FacebookAds(account_name='Logikal')
report = facebook.report(
    metrics=['spend', 'impressions', 'clicks'],
    dimensions=['campaign_name'],
    parameters={
        'level': 'campaign',
        'time_range': {'since': '2023-01-01', 'until': '2023-01-07'},
    },
)[['campaign_name', 'spend', 'impressions', 'clicks']]

report.head()

	campaign_name	spend	impressions	clicks
0	(y_2023)(p_mindlab)(g_discover)(o_traffic)(c_USA)	3.31	115	1

Finally, we push the data into the desired spreadsheet:

from stormware.google.sheets import Spreadsheet

with Spreadsheet(key='1YKTWQtHk7cBIcWoy3VlHTKQvkhKXUtE8iQDmGK3pIkY') as sheet:
    sheet.set_sheet(name='Facebook Campaign', data=report)

Of course, if we wanted to push the data into Google BigQuery as well, it would be equally easy:

from stormware.google.bigquery import BigQuery

with BigQuery() as bigquery:
    bigquery.set_table(name='test.facebook_campaign', data=report)

That’s it! Connectors are designed to be composable (for example, they will often return a pandas.DataFrame or expect one as an input), which makes common tasks much easier to solve. Additionally, this approach also makes further analysis work with tools like MindLab simple and effortless.