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

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

Connecting to Greenhouse Data

Connecting to Greenhouse 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.

You need an API key to connect to Greenhouse. To create an API key, follow the steps below:

1. Click the Configure icon in the navigation bar and locate Dev Center on the left.
2. Select API Credential Management.
3. Click Create New API Key.
• Set "API Type" to Harvest.
• Set "Partner" to custom.
• Optionally, provide a description.
4. Proceed to Manage permissions and select the appropriate permissions based on the resources you want to access through the driver.
5. Copy the created key and set APIKey to that value.

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

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

cnxn = mod.connect("APIKey=YourAPIKey;")

Create a SQL Statement to Query Greenhouse

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

sql = "SELECT Id, CandidateId FROM Applications WHERE Status = 'Active'"

Extract, Transform, and Load the Greenhouse Data

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

Loading Greenhouse Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("APIKey=YourAPIKey;")

sql = "SELECT Id, CandidateId FROM Applications WHERE Status = 'Active'"

table1 = etl.fromdb(cnxn,sql)

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

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