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

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

Connecting to Square Data

Connecting to Square 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.

Square uses the OAuth authentication standard. To authenticate using OAuth, you will need to register an app with Square to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

Additionally, you must specify the LocationId. You can retrieve the Ids for your Locations by querying the Locations table. Alternatively, you can set the LocationId in the search criteria of your query.

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

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

cnxn = mod.connect("OAuthClientId=MyAppId;OAuthClientSecret=MyAppSecret;CallbackURL=http://localhost:33333;LocationId=MyDefaultLocation;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query Square

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

sql = "SELECT Reason, RefundedMoneyAmount FROM Refunds WHERE Type = 'FULL'"

Extract, Transform, and Load the Square Data

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

Loading Square Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

Adding New Rows to Square

table1 = [ ['Reason','RefundedMoneyAmount'], ['NewReason1','NewRefundedMoneyAmount1'], ['NewReason2','NewRefundedMoneyAmount2'], ['NewReason3','NewRefundedMoneyAmount3'] ]

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

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

cnxn = mod.connect("OAuthClientId=MyAppId;OAuthClientSecret=MyAppSecret;CallbackURL=http://localhost:33333;LocationId=MyDefaultLocation;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Reason, RefundedMoneyAmount FROM Refunds WHERE Type = 'FULL'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Reason','RefundedMoneyAmount'], ['NewReason1','NewRefundedMoneyAmount1'], ['NewReason2','NewRefundedMoneyAmount2'], ['NewReason3','NewRefundedMoneyAmount3'] ]

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