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

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

Connecting to GitHub Data

Connecting to GitHub 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.

GitHub uses the OAuth 2 authentication standard. To authenticate using OAuth, you will need to create an app to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL connection properties. See the Getting Started chapter of the CData help documentation for an authentication guide.

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

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query GitHub

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

sql = "SELECT Name, Email FROM Users WHERE UserLogin = 'mojombo'"

Extract, Transform, and Load the GitHub Data

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

Loading GitHub Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

In the following example, we add new rows to the Users table.

Adding New Rows to GitHub

table1 = [ ['Name','Email'], ['NewName1','NewEmail1'], ['NewName2','NewEmail2'], ['NewName3','NewEmail3'] ]

etl.appenddb(table1, cnxn, 'Users')

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

cnxn = mod.connect("OAuthClientId=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:portNumber;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

sql = "SELECT Name, Email FROM Users WHERE UserLogin = 'mojombo'"

table1 = etl.fromdb(cnxn,sql)

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

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

table3 = [ ['Name','Email'], ['NewName1','NewEmail1'], ['NewName2','NewEmail2'], ['NewName3','NewEmail3'] ]

etl.appenddb(table3, cnxn, 'Users')