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

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

Connecting to Drip Data

Connecting to Drip 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 Drip Profile on disk (e.g. C:\profiles\Drip.apip). Next, set the ProfileSettings connection property to the connection string for Drip (see below).

Drip API Profile Settings

To use Token Authentication, specify your APIKey within the ProfileSettings connection property. The APIKey should be set to your Drip personal API Token.

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

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

Create a SQL Statement to Query Drip

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

sql = "SELECT Id, Name FROM Broadcasts WHERE Status = 'scheduled'"

Extract, Transform, and Load the Drip Data

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

Loading Drip Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, Name FROM Broadcasts WHERE Status = 'scheduled'"

table1 = etl.fromdb(cnxn,sql)

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

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