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

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

Connecting to Hugging Face Data

Connecting to Hugging Face 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.

HuggingFace Hub uses token-based authentication to enable access to its API. The API provides access to machine learning models, datasets, spaces, papers, and other resources on the HuggingFace Hub platform.

Using API Key Authentication

To authenticate to HuggingFace Hub, you will need to provide an API Key (Access Token). To obtain your access token:

1. Log in to your HuggingFace account at https://huggingface.co
2. Navigate to Settings > Access Tokens
3. Click "New token" to create a new access token
4. Select the appropriate permissions (read or write)
5. Copy the token value

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

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your HuggingFace access token.

Example connection string

Profile=C:\profiles\HuggingFace.apip;ProfileSettings='APIKey=hf_xxxxxxxxxxxxxxxxxxxx';

After installing the CData Hugging Face Connector, follow the procedure below to install the other required modules and start accessing Hugging Face 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 Hugging Face 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 Hugging Face Connector to create a connection for working with Hugging Face data.

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

Create a SQL Statement to Query Hugging Face

Use SQL to create a statement for querying Hugging Face. In this article, we read data from the Collections entity.

sql = "SELECT ,  FROM Collections WHERE  = ''"

Extract, Transform, and Load the Hugging Face Data

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

Loading Hugging Face Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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

With the CData API Driver for Python, you can work with Hugging Face 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 Hugging Face 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\HuggingFace.apip;ProfileSettings='APIKey=hf_xxxxxxxxxxxxxxxxxxxx';")

sql = "SELECT ,  FROM Collections WHERE  = ''"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'')

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