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

Once installed data-transport can be used as a library in code or a command line interface (CLI)

Data Transport as a Library (in code)

---

The data-transport can be used within code as a library

• Read/Write against mongodb
• Read/Write against tranditional RDBMS
• Read/Write against bigquery

The read/write functions make data-transport a great candidate for data-science; data-engineering or all things pertaining to data. It enables operations across multiple data-stores(relational or not)

Command Line Interface (CLI)

---

The CLI program is called transport and it requires a configuratio file

[
    {
    "id":"logs",
    "source":{
        "provider":"postgresql","context":"read","database":"mydb",
        "cmd":{"sql":"SELECT * FROM logs limit 10"}
        },
    "target":{
        "provider":"bigquery","private_key":"/bgqdrive/account/bq-service-account-key.json",
        "dataset":"mydataset"
        }    
    },
    
]

Assuming the above content is stored in a file called etl-config.json, we would perform the following in a terminal window:

[steve@data-transport]$ transport --config ./etl-config.json [--index <value>]

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>)