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

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

Connecting to eBay Analytics Data

Connecting to eBay Analytics 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.

You can authenticate to eBay Analytics only via the OAuth 2 authentication method. The eBay Analytics API requires an access token created with the authorization code grant flow to authorize the requests.

You can follow the guide in the Help documentation for a step by step guide on how to authenticate using the OAuth 2 protocol.

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

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

cnxn = mod.connect("OAuthClientId=MyAppID;OAuthClientSecret=MyCertID;RuName=MyRuName;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query eBay Analytics

Use SQL to create a statement for querying eBay Analytics. In this article, we read data from the TrafficReportByListing entity.

sql = "SELECT ListingName, ClickThroughRate FROM TrafficReportByListing WHERE ListingId = '201284405428'"

Extract, Transform, and Load the eBay Analytics Data

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

Loading eBay Analytics Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("OAuthClientId=MyAppID;OAuthClientSecret=MyCertID;RuName=MyRuName;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT ListingName, ClickThroughRate FROM TrafficReportByListing WHERE ListingId = '201284405428'"

table1 = etl.fromdb(cnxn,sql)

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

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