ingenii-data-engineering 0.2.1

Facilitate data engineering on the Ingenii Data Platform

Ingenii Data Engineering Package

Details

• Current Version: 0.2.1

Overview

This package provides utilities for data engineering on Ingenii's Azure Data Platform. This can be both used for local development, and is used in the Ingenii Databricks Runtime.

Usage

Import the package to use the functions within.

import ingenii_data_engineering

dbt

See details of how we validate dbt schemas in the dbt README file

Pre-processing

See details of working with pre-processing functions in the pre-processing README file

Development

Prerequisites

1. A working knowledge of git SCM
2. Installation of Python 3.7.3

Set up

1. Complete the 'Getting Started > Prerequisites' section
2. For Windows only:
3. Run make setup: to copy the .env into place (.env-dist > .env)

Getting started

1. Complete the 'Getting Started > Set up' section

2. From the root of the repository, in a terminal (preferably in your IDE) run the following commands to set up a virtual environment:

   python -m venv venv
   . venv/bin/activate
   pip install -r requirements-dev.txt
   pre-commit install
   

   or for Windows:

   python -m venv venv
   . venv/Scripts/activate
   pip install -r requirements-dev.txt
   pre-commit install
   

3. Note: if you get a permission denied error when executing the pre-commit install command you'll need to run chmod -R 775 venv/bin/ to recursively update permissions in the venv/bin/ dir

4. The following checks are run as part of pre-commit hooks: flake8(note unit tests are not run as a hook)

Building

1. Complete the 'Getting Started > Set up' section
2. Run make build to create the package in ./dist
3. Run make clean to remove dist files

Testing

1. Complete the 'Getting Started > Set up' and 'Development' sections
2. Run make test to run the unit tests using pytest
3. Run flake8 to run lint checks using flake8
4. Run make qa to run the unit tests and linting in a single command
5. Run make qa to remove pytest files

Version History

• 0.2.1: Handle JSON read UTF-8 BOM
• 0.2.0: Pre-processing happens all in the 'archive' container
• 0.1.5: Better functionality for column names in .csv files
• 0.1.4: Handle JSON files
• 0.1.3: Adding pre-processing utilities
• 0.1.2: Rearrangement and better split of work with the Databricks Runtime. Better validation
• 0.1.1: Minor bug fixes
• 0.1.0: dbt schema validation, pre-processing class