How to Build an ETL App for Rebilly 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 Rebilly-connected applications and pipelines for extracting, transforming, and loading Rebilly data. This article shows how to connect to Rebilly with the CData Python Connector and use petl and pandas to extract, transform, and load Rebilly data.

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

Connecting to Rebilly Data

Connecting to Rebilly 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.

Start by setting the Profile connection property to the location of the Rebilly Profile on disk (e.g. C:\profiles\Rebilly.apip). Next, set the ProfileSettings connection property to the connection string for Rebilly (see below).

Rebilly API Profile Settings

Generate an API Key in Rebilly by navigating to Automations > Integrations > Custom Integrations > API Keys and selecting Secret as the type.

After installing the CData Rebilly Connector, follow the procedure below to install the other required modules and start accessing Rebilly 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 Rebilly 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 Rebilly Connector to create a connection for working with Rebilly data.

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

Create a SQL Statement to Query Rebilly

Use SQL to create a statement for querying Rebilly. In this article, we read data from the Attachments entity.

sql = "SELECT Id, Name FROM Attachments WHERE RelatedType = 'customer'"

Extract, Transform, and Load the Rebilly Data

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

Loading Rebilly Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

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

With the CData API Driver for Python, you can work with Rebilly 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 Rebilly 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\Rebilly.apip;ProfileSettings='APIKey=your_api_key';")

sql = "SELECT Id, Name FROM Attachments WHERE RelatedType = 'customer'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'Name')

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