Introduction

This project implements an abstraction of objects that can have access to a variety of data stores, implementing read/write with a simple and expressive interface. This abstraction works with NoSQL, SQL and Cloud data stores and leverages pandas.

Why Use Data-Transport ?

Mostly data scientists that don't really care about the underlying database and would like a simple and consistent way to read/write and move data are well served. Additionally we implemented lightweight Extract Transform Loading API and command line (CLI) tool. Finally it is possible to add pre/post processing pipeline functions to read/write

1. Familiarity with pandas data-frames
2. Connectivity drivers are included
3. Reading/Writing data from various sources
4. Useful for data migrations or ETL

Installation

Within the virtual environment perform the following :

pip install git+https://github.com/lnyemba/data-transport.git

Features

- read/write from over a dozen databases
- run ETL jobs seamlessly
- scales and integrates into shared environments like apache zeppelin; jupyterhub; SageMaker; ...

What's new

Unlike older versions 2.0 and under, we focus on collaborative environments like jupyter-x servers; apache zeppelin:

1. Simpler syntax to create reader or writer
2. auth-file registry that can be referenced using a label
3. duckdb support

Learn More

We have available notebooks with sample code to read/write against mongodb, couchdb, Netezza, PostgreSQL, Google Bigquery, Databricks, Microsoft SQL Server, MySQL ... Visit data-transport homepage