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This framework allows read/write and ETL against many SQL, Cloud, NoSQL databases, and other persistent data stores. Additional features include support for user-defined plugin pre/post processing functions
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Steve Nyemba 1eda49b63a
documentation 		7 months ago
bin bug fix: ETL, Mongodb 8 months ago
info refactoring version 2.0 8 months ago
notebooks documentation 7 months ago
transport bug fix: set function mongodb used for updates 7 months ago
.gitignore .. 11 months ago
README.md documentation 7 months ago
requirements.txt S3 Requirments file 7 years ago
setup.py bug fixes 8 months ago

README.md

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 data and have will be well served. Additionally we implemented lightweight Extract Transform Loading API and command line (CLI) tool.

  1. Familiarity with pandas data-frames
  2. Connectivity drivers are included
  3. Mining 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

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