python data transport layer, mongodb, netezza, bigquery, postgresql files
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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 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

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)

ETL

Embedded in Code

It is possible to perform ETL within custom code as follows :

    import transport
    import time
    
    _info = [{source:{'provider':'sqlite','path':'/home/me/foo.csv','table':'me'},target:{provider:'bigquery',private_key='/home/me/key.json','table':'me','dataset':'mydataset'}}, ...]    
    procs = transport.factory.instance(provider='etl',info=_info)
    #
    #
    while procs:
        procs = [pthread for pthread in procs if pthread.is_alive()]
        time.sleep(1)

Command Line Interface (CLI):

The CLI program is called transport and it requires a configuration file. The program is intended to move data from one location to another. Supported data stores are in the above paragraphs.

[
    {
    "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>)