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

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

Connecting to Harvest Data

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

Harvest API Profile Settings

To authenticate to Harvest, you can use either Token authentication or the OAuth standard. Use Basic authentication to connect to your own data. Use OAuth to allow other users to connect to their data.

Using Token Authentication

To use Token Authentication, set the APIKey to your Harvest Personal Access Token in the ProfileSettings connection property. In addition to APIKey, set your AccountId in ProfileSettings to connect.

Using OAuth Authentication

First, register an OAuth2 application with Harvest. The application can be created from the "Developers" section of Harvest ID.

After setting the following connection properties, you are ready to connect:

• ProfileSettings: Set your AccountId in ProfileSettings.
• AuthScheme: Set this to OAuth.
• OAuthClientId: Set this to the client ID that you specified in your app settings.
• OAuthClientSecret: Set this to the client secret that you specified in your app settings.
• CallbackURL: Set this to the Redirect URI that you specified in your app settings.
• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage how the driver obtains and refreshes the OAuthAccessToken.

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

cnxn = mod.connect("Profile=C:\profiles\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';")

Create a SQL Statement to Query Harvest

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

sql = "SELECT Id, ClientName FROM Invoices WHERE State = 'open'"

Extract, Transform, and Load the Harvest Data

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

Loading Harvest Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

With the CData API Driver for Python, you can work with Harvest 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 Harvest 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\Harvest.apip;ProfileSettings='APIKey=my_personal_key;AccountId=_your_account_id';")

sql = "SELECT Id, ClientName FROM Invoices WHERE State = 'open'"

table1 = etl.fromdb(cnxn,sql)

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

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