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Extract, Transform, and Load HubSpot Data in Python

The CData Python Connector for HubSpot enables you to create ETL applications and pipelines for HubSpot data in Python with petl.

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

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

Connecting to HubSpot Data

Connecting to HubSpot 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.

HubSpot uses the OAuth authentication standard. You can use the embedded OAuthClientId, OAuthClientSecret, and CallbackURL or you can obtain your own by registering an app.

See the Getting Started chapter of the help documentation for a guide to using OAuth.

After installing the CData HubSpot Connector, follow the procedure below to install the other required modules and start accessing HubSpot 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 HubSpot 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.hubspot as mod

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

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query HubSpot

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

sql = "SELECT Slug, PageViews FROM Prospects WHERE Region = 'ONTARIO'"

Extract, Transform, and Load the HubSpot Data

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

Loading HubSpot Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Prospects table.

Adding New Rows to HubSpot

table1 = [ ['Slug','PageViews'], ['NewSlug1','NewPageViews1'], ['NewSlug2','NewPageViews2'], ['NewSlug3','NewPageViews3'] ]

etl.appenddb(table1, cnxn, 'Prospects')

With the CData Python Connector for HubSpot, you can work with HubSpot 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 HubSpot Python Connector to start building Python apps and scripts with connectivity to HubSpot 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.hubspot as mod

cnxn = mod.connect("InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Slug, PageViews FROM Prospects WHERE Region = 'ONTARIO'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Slug','PageViews'], ['NewSlug1','NewPageViews1'], ['NewSlug2','NewPageViews2'], ['NewSlug3','NewPageViews3'] ]

etl.appenddb(table3, cnxn, 'Prospects')