vestapol 0.0.20

Python package that loads data from the web and deploys a corresponding external table definition, so that the data can be queried using standard SQL.

vestapol

vestapol is a Python package that loads data from the web and deploys a corresponding external table definition, so that the data can be queried using standard SQL.

"Vestapol" is an open D Major tuning for the guitar. It is named after a 19th-century composition distributed in some of the earliest instructional guides for guitar.

Usage

from vestapol.web_resources.csv_resource import CSVResource
from vestapol.destinations.gcp_destination import GoogleCloudPlatform


nyt_covid_data_2022 = CSVResource(
    name="nyt_covid19_us_counties_2022",
    base_url="https://raw.githubusercontent.com/",
    endpoint="nytimes/covid-19-data/master/rolling-averages/us-counties-2022.csv",
    version="v0",
    skip_leading_rows=1,
)

destination = GoogleCloudPlatform()

nyt_covid_data_2022.load(destination)
tablename = destination.create_table(nyt_covid_data_2022)


from google.cloud import bigquery

client = bigquery.Client()
query = f"""
    select date, state, county, cases_avg_per_100k
    from `{tablename}`
    where requested_at = '{nyt_covid_data_2022.requested_at}'
    limit 5
"""
query_job = client.query(query)
for row in query_job.result():
    print(row)

Prerequisites

Installation of this project requires Poetry 1.2+ and Python version 3.9+.

Older version of poetry can be updated by running:

poetry self update
poetry --version

Installation

Install vestapol and its dependencies by running:

poetry install

Testing

Run tests with the following command:

poetry run pytest

Environment Variables

• GCS_BUCKET_NAME: the Google Cloud Storage bucket where data is loaded (e.g. inq-warehouse-waligob)
• GCS_ROOT_PREFIX: the GCS prefix where data is loaded (e.g. data_catalog)
• GBQ_PROJECT_ID: the BigQuery project identifier (e.g. inq-warehouse)
• GBQ_DATASET_ID: the BigQuery dataset where external tables will be created (e.g. data_catalog_waligob)
• GBQ_DATASET_LOCATION: the BigQuery dataset location (e.g. US)
• GOOGLE_APPLICATION_CREDENTIALS=: location of the GCS service account keyfile (e.g. ~/inq-warehouse-f0962a57089e-inf.json)

Publishing to PyPI

Instructions for pushing new versions of vestapol to PyPI:

1. Update CHANGELOG.md. Include Additions, Fixes, and Changes.

2. Update project version using either a valid PEP 440 string or a valid bump rule following Semantic Versioning.

    poetry version <version string or bump rule>

3. Create a release and check the CD Pipeline action to ensure that the project was built and published to PyPI successfully.