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

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

Connecting to Freshteam Data

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

Freshteam API Profile Settings

Find your API Key in the top-right corner of your Freshteam account under the API Key section. Your AccountName is the subdomain of your Freshteam URL.

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

cnxn = mod.connect("Profile=C:\profiles\Freshteam.apip;ProfileSettings='APIKey=your_api_key;AccountName=your_account_name';")

Create a SQL Statement to Query Freshteam

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

sql = "SELECT Id, Name FROM Branches WHERE MainOffice = 'true'"

Extract, Transform, and Load the Freshteam Data

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

Loading Freshteam Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Freshteam 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 Freshteam 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\Freshteam.apip;ProfileSettings='APIKey=your_api_key;AccountName=your_account_name';")

sql = "SELECT Id, Name FROM Branches WHERE MainOffice = 'true'"

table1 = etl.fromdb(cnxn,sql)

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

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