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data-transport/README.md

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# 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](https://healthcareio.the-phi.com/data-transport)