Extract, Transform, and Load GraphQL Data in Python

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GraphQL Python Connector

Python Connector Libraries for GraphQL Data Connectivity. Integrate GraphQL with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for GraphQL enables you to create ETL applications and pipelines for GraphQL data in Python with petl.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for GraphQL and the petl framework, you can build GraphQL-connected applications and pipelines for extracting, transforming, and loading GraphQL data. This article shows how to connect to GraphQL with the CData Python Connector and use petl and pandas to extract, transform, and load GraphQL data.

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

Connecting to GraphQL Data

Connecting to GraphQL 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 must specify the URL of the GraphQL service. The driver supports two types of authentication:

  • Basic: Set AuthScheme to Basic. You must specify the User and Password of the GraphQL service.
  • OAuth 1.0 & 2.0: Take a look at the OAuth section in the Help documentation for detailed instructions.

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

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

cnxn = mod.connect("AuthScheme=Basic;User=username;Password=password;URL=https://mysite.com;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Create a SQL Statement to Query GraphQL

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

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

Extract, Transform, and Load the GraphQL Data

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

Loading GraphQL Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

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

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

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

cnxn = mod.connect("AuthScheme=Basic;User=username;Password=password;URL=https://mysite.com;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

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

table1 = etl.fromdb(cnxn,sql)

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

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