1.2 KiB
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
- Familiarity with pandas data-frames
- Connectivity drivers are included
- Mining data from various sources
- Useful for data migrations or ETL
Installation
Within the virtual environment perform the following :
pip install git+https://github.com/lnyemba/data-transport.git
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