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

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

Connecting to BambooHR Data

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

BambooHR API Profile Settings

In order to authenticate to BambooHR, you'll need to provide your API Key. To generate an API key, log in and click your name in the upper right-hand corner of any page to get to the user context menu. If you have sufficient permissions, there will be an "API Keys" option in that menu to go to the page, where you can create a new API Key. Additionally, you will need to set the Domain, found in the domain name of your BambooHR account. For example if your BambooHR account is acmeinc.bamboohr.com, then the Domain should be 'acmeinc'. Set both the API Key and Domain in the ProfileSettings property to connect.

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

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

Create a SQL Statement to Query BambooHR

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

sql = "SELECT Id, DisplayName FROM Employees WHERE Department = 'Sales'"

Extract, Transform, and Load the BambooHR Data

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

Loading BambooHR Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

sql = "SELECT Id, DisplayName FROM Employees WHERE Department = 'Sales'"

table1 = etl.fromdb(cnxn,sql)

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

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