How to Build an ETL App for Reply.io Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build Reply.io-connected applications and pipelines for extracting, transforming, and loading Reply.io data. This article shows how to connect to Reply.io with the CData Python Connector and use petl and pandas to extract, transform, and load Reply.io data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Reply.io data in Python. When you issue complex SQL queries from Reply.io, the driver pushes supported SQL operations, like filters and aggregations, directly to Reply.io and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Reply.io Data

Connecting to Reply.io data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The Reply.io API uses API Key authentication via the x-api-key request header.

Using API Key Authentication

Your Reply.io API key is required to create a connection. To obtain your API key:

1. Log into your Reply.io account.
2. Click your profile icon and select Settings.
3. Navigate to the API section.
4. Copy your API Key.

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Reply.io API key.
• UserEmail (optional): Set this to the email address of the Reply.io user on whose behalf requests are made.

Example connection string:

Profile=C:\profiles\ReplyIO.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';

After installing the CData Reply.io Connector, follow the procedure below to install the other required modules and start accessing Reply.io through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Reply.io Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.api as mod

You can now connect with a connection string. Use the connect function for the CData Reply.io Connector to create a connection for working with Reply.io data.

cnxn = mod.connect("Profile=C:\profiles\ReplyIO.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';")

Create a SQL Statement to Query Reply.io

Use SQL to create a statement for querying Reply.io. In this article, we read data from the BillingInfo entity.

sql = "SELECT ,  FROM BillingInfo WHERE  = ''"

Extract, Transform, and Load the Reply.io Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Reply.io data. In this example, we extract Reply.io data, sort the data by the column, and load the data into a CSV file.

Loading Reply.io Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

etl.tocsv(table2,'billinginfo_data.csv')

With the CData API Driver for Python, you can work with Reply.io data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Reply.io data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\ReplyIO.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';")

sql = "SELECT ,  FROM BillingInfo WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

etl.tocsv(table2,'billinginfo_data.csv')