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Data engineering & Data science Pipeline Framework

Project description


PyGyver is a user-friendly python package for data integration and manipulation.

Named after MacGyver, title character in the TV series MacGyver, and Python, the main language used in the repository.



PyGyver is available on PyPi.

pip install pygyver


Most APIs requires access token files to authentificate and perform tasks such as creating or deleting objects. Those files need to be generated prior to using pygyver and stored in the environment you are executing your code against. The package make use of environment variables, and some of the below might need be supplied in your environment:

# Access token path

# Default values

# Optional


PyGyver is structured around several modules available in the etl folder. Here is a summary table of those modules:

Module name Descrition Documentation
dw Perform task against the Google Cloud BigQuery API
facebook Perform task against the Facebook Marketing API
gooddata Perform task against the GoodData API -
gs Perform task against the Google Sheet API -
lib Store utilities used by other modules -
pipeline Utility to build data pipelines via YAML definition
prep Data transformation - ML pipelines -
storage Perform task against the AWS S3 and Google Cloud Storage API
toolkit Sets of tools for data manipulation -

In order to load BigQueryExecutor from the dw module, you can run:

from pygyver.etl.dw import BigQueryExecutor


To get started...

Step 1

  • 👯 Clone this repo to your local machine using

Step 2

  • HACK AWAY! 🔨🔨🔨

The team follows TDD to develop new features on pygyver. Tests can be found in pygyver/tests.

Step 3

  • 🔃 Create a new pull request and request review from team members. Where applicable, a test should be added with the code change.


  • How to release a new version to PyPi?
    1. Merge your changes to master branch
    2. Create a new release using

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