Effortlessly validate and test your Google BigQuery queries with the power of pandas DataFrames in Python.

Warning

This library is a work in progress!

Breaking changes should be expected until a 1.0 release, so version pinning is recommended.

• Use BQuest in combination with your favorite testing framework (e.g. pytest).
• Create temporary test tables from [JSON](https://cloud.google.com/bigquery/docs/loading-data) or [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).
• Run BQ configurations and plain SQL queries on your test tables and check the result.

Via PyPi (standard):

pip install bquest

Via Github (most recent):

pip install git+https://github.com/ottogroup/bquest

BQuest also requires a dedicated BigQuery dataset for storing test tables, e.g.

resource "google_bigquery_dataset" "bquest" {
  dataset_id    = "bquest"
  friendly_name = "bquest"
  description   = "Source tables for bquest tests"
  location      = "EU"
  default_table_expiration_ms = 3600000
}

We recommend setting an expiration time for tables in the bquest dataset to assure removal of those test tables upon test execution.

TBD