# 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** and **SQL** data stores and leverages **pandas**

The supported data store providers :

| Provider | Underlying Drivers | Description |
| :---- | :----: | ----: |
| sqlite| Native SQLite|SQLite3|
| postgresql| psycopg2 | PostgreSQL
| redshift| psycopg2 | Amazon Redshift
| s3| boto3 | Amazon Simple Storage Service
| netezza| nzpsql | IBM Neteeza
| Files: CSV, TSV| pandas| pandas data-frame
| Couchdb| cloudant | Couchbase/Couchdb
| mongodb| pymongo | Mongodb
| mysql| mysql| Mysql
| bigquery| google-bigquery| Google BigQuery
| mariadb| mysql| Mariadb
| rabbitmq|pika| RabbitMQ Publish/Subscribe

# Why Use Data-Transport ?

Mostly data scientists that don't really care about the underlying database and would like to manipulate data transparently.

1. Familiarity with **pandas data-frames**
2. Connectivity **drivers** are included
3. Useful for data migrations or ETL

# Usage

## Installation

Within the virtual environment perform the following :

    pip install git+https://dev.the-phi.com/git/steve/data-transport.git



## In code (Embedded)

**Reading/Writing Mongodb**

For this example we assume here we are tunneling through port 27018 and there is not access control:

```
import transport
reader = factory.instance(provider='mongodb',context='read',host='localhost',port='27018',db='example',doc='logs')

df = reader.read() #-- reads the entire collection
print (df.head())
#
#-- Applying mongodb command
PIPELINE = [{"$group":{"_id":None,"count":{"$sum":1}}}]
_command_={"cursor":{},"allowDiskUse":True,"aggregate":"logs","pipeline":PIPLINE}
df = reader.read(mongo=_command)
print (df.head())
reader.close()
```
**Writing to Mongodb**
---
```
import transport
improt pandas as pd
writer = factory.instance(provider='mongodb',context='write',host='localhost',port='27018',db='example',doc='logs')

df = pd.DataFrame({"names":["steve","nico"],"age":[40,30]})
writer.write(df)
writer.close()
```



    #
    # reading from postgresql
    
    pgreader     = factory.instance(type='postgresql',database=<database>,table=<table_name>)
    pg.read()   #-- will read the table by executing a SELECT
    pg.read(sql=<sql query>)
    
    #
    # Reading a document and executing a view
    #
    document    = dreader.read()    
    result      = couchdb.view(id='<design_doc_id>',view_name=<view_name',<key=value|keys=values>)