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

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

Connecting to Vimeo Data

Connecting to Vimeo 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.

Vimeo is a professional video hosting platform. The Vimeo API uses personal access tokens (bearer tokens) to enable secure access to video metadata, user information, channels, groups, categories, and related resources.

Using API Key Authentication

To authenticate to the Vimeo API, you will need to provide a personal access token. To obtain your access token:

1. Log in to your Vimeo account at https://vimeo.com
2. Navigate to https://developer.vimeo.com/apps
3. Create a new app or select an existing app
4. Under "Personal Access Tokens", click "Generate" to create a new token
5. Select the required scopes: public and private for read access
6. Copy the generated token

After obtaining your access token, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Vimeo personal access token.

Example connection string

Profile=C:\profiles\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';

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

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

cnxn = mod.connect("Profile=C:\profiles\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';")

Create a SQL Statement to Query Vimeo

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

sql = "SELECT ,  FROM Videos WHERE UserUri = '/users/12345678'"

Extract, Transform, and Load the Vimeo Data

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

Loading Vimeo Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Vimeo 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 API Driver for Python to start building Python apps and scripts with connectivity to Vimeo 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.api as mod

cnxn = mod.connect("Profile=C:\profiles\Vimeo.apip;ProfileSettings='APIKey=your_personal_access_token';")

sql = "SELECT ,  FROM Videos WHERE UserUri = '/users/12345678'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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