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

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

Connecting to ShipStation Data

Connecting to ShipStation 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.

Use the BASIC Authentication standard to connect.

1. Login to your ShipStation account
2. Click on the settings icon in the upper right corner. A column menu will show up on the left
3. Click Account -> API Settings
4. On the API Settings page, note the API Key and API Secret.

Authenticating to ShipStation

• APIKey: Set this to the API key from the API settings page.
• APISecret: Set this to the Secret key from the API settings page.

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

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

cnxn = mod.connect("APIKey='YourAPIKey';APISecret='YourAPISecret'")

Create a SQL Statement to Query ShipStation

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

sql = "SELECT Id, Color FROM Tags WHERE CustomerId = '1368175'"

Extract, Transform, and Load the ShipStation Data

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

Loading ShipStation Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("APIKey='YourAPIKey';APISecret='YourAPISecret'")

sql = "SELECT Id, Color FROM Tags WHERE CustomerId = '1368175'"

table1 = etl.fromdb(cnxn,sql)

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

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