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@@ -4,12 +4,11 @@ This project implements an abstraction of objects that can have access to a vari
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# Why Use Data-Transport ?
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-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
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-
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-1. Familiarity with **pandas data-frames**
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-2. Connectivity **drivers** are included
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-3. Reading/Writing data from various sources
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-4. Useful for data migrations or **ETL**
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+Data transport is a simple framework that:
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+- easy to install & modify (open-source)
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+- enables access to multiple database technologies (pandas, SQLAlchemy)
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+- enables notebook sharing without exposing database credential.
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+- supports pre/post processing specifications (pipeline)
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## Installation
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@@ -18,19 +17,16 @@ Within the virtual environment perform the following :
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pip install git+https://github.com/lnyemba/data-transport.git
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-## Features
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+Options to install components in square brackets
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- - read/write from over a dozen databases
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- - run ETL jobs seamlessly
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- - scales and integrates into shared environments like apache zeppelin; jupyterhub; SageMaker; ...
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+ pip install data-transport[nosql,cloud,warehouse,all]@git+https://github.com/lnyemba/data-transport.git
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-## What's new
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-Unlike older versions 2.0 and under, we focus on collaborative environments like jupyter-x servers; apache zeppelin:
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+## Additional features
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- 1. Simpler syntax to create reader or writer
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- 2. auth-file registry that can be referenced using a label
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- 3. duckdb support
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+ - In addition to read/write, there is support for functions for pre/post processing
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+ - CLI interface to add to registry, run ETL
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+ - scales and integrates into shared environments like apache zeppelin; jupyterhub; SageMaker; ...
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## Learn More
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Steve Nyemba