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

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

Connecting to Amazon Marketplace Data

Connecting to Amazon Marketplace 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 the Amazon Marketplace Webservice (MWS), AWSAccessKeyId, MWSAuthToken, AWSSecretKey and SellerId are required. You can optionally set the Marketplace property. For more information on obtaining values for these properties, refer to the Help documentation.

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

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

cnxn = mod.connect("AWS Access Key Id=myAWSAccessKeyId;AWS Secret Key=myAWSSecretKey;MWS Auth Token=myMWSAuthToken;Seller Id=mySellerId;Marketplace=United States;")

Create a SQL Statement to Query Amazon Marketplace

Use SQL to create a statement for querying Amazon Marketplace. In this article, we read data from the Orders entity.

sql = "SELECT AmazonOrderId, OrderStatus FROM Orders WHERE IsReplacementOrder = 'True'"

Extract, Transform, and Load the Amazon Marketplace Data

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

Loading Amazon Marketplace Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("AWS Access Key Id=myAWSAccessKeyId;AWS Secret Key=myAWSSecretKey;MWS Auth Token=myMWSAuthToken;Seller Id=mySellerId;Marketplace=United States;")

sql = "SELECT AmazonOrderId, OrderStatus FROM Orders WHERE IsReplacementOrder = 'True'"

table1 = etl.fromdb(cnxn,sql)

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

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