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

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

Connecting to Pinterest Data

Connecting to Pinterest 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.

Pinterest authentication is based on the standard OAuth flow. To authenticate, you must initially create an app via the Pinterest developer platform where you can obtain an OAuthClientId, OAuthClientSecret, and CallbackURL.

Set InitiateOAuth to GETANDREFRESH and set OAuthClientId, OAuthClientSecret, and CallbackURL based on the property values for the app you created.

See the Help documentation for other OAuth authentication flows.

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

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='https://localhost:33333';InitiateOAuth=GETANDREFRESH;")

Create a SQL Statement to Query Pinterest

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

sql = "SELECT Id, Username FROM Users WHERE FirstName = 'Jane'"

Extract, Transform, and Load the Pinterest Data

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

Loading Pinterest Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;CallbackURL='https://localhost:33333';InitiateOAuth=GETANDREFRESH;")

sql = "SELECT Id, Username FROM Users WHERE FirstName = 'Jane'"

table1 = etl.fromdb(cnxn,sql)

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

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