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

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

Connecting to FreshBooks Data

Connecting to FreshBooks 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.

To connect to FreshBooks, you can set the CompanyName and Token connection properties. Alternatively, you can use the OAuth authentication standard.

OAuth can be used to enable other users to access their own company data. To authenticate using OAuth, you will need to obtain the OAuthClientId and OAuthClientSecret by registering an app. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

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

cnxn = mod.connect("CompanyName=CData;Token=token;")

Create a SQL Statement to Query FreshBooks

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

sql = "SELECT Username, Credit FROM Clients WHERE Email = 'Captain Hook'"

Extract, Transform, and Load the FreshBooks Data

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

Loading FreshBooks Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to FreshBooks

table1 = [ ['Username','Credit'], ['NewUsername1','NewCredit1'], ['NewUsername2','NewCredit2'], ['NewUsername3','NewCredit3'] ]

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

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

cnxn = mod.connect("CompanyName=CData;Token=token;")

sql = "SELECT Username, Credit FROM Clients WHERE Email = 'Captain Hook'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Username','Credit'], ['NewUsername1','NewCredit1'], ['NewUsername2','NewCredit2'], ['NewUsername3','NewCredit3'] ]

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