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

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

Connecting to Pipeline CRM Data

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

Pipeline CRM API Profile Settings

Retrieve your API Key via Account Settings > Pipeline API > Enable API Access > New API Key, and generate an APP Key at https://app.pipelinecrm.com/admin/modern/api by creating a new Integration.

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

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

Create a SQL Statement to Query Pipeline CRM

Use SQL to create a statement for querying Pipeline CRM. In this article, we read data from the AccountNotifications entity.

sql = "SELECT Id, AccountId FROM AccountNotifications WHERE Seen = 'true'"

Extract, Transform, and Load the Pipeline CRM Data

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

Loading Pipeline CRM Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, AccountId FROM AccountNotifications WHERE Seen = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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