Once the required modules and frameworks are installed, we are ready to build our ETL app. Pip install pandas Build an ETL App for GraphQL Data in Python Use the pip utility to install the required modules and frameworks: pip install petl OAuth 1.0 & 2.0: Take a look at the OAuth section in the Help documentation for detailed instructions.Īfter installing the CData GraphQL Connector, follow the procedure below to install the other required modules and start accessing GraphQL through Python objects.You must specify the User and Password of the GraphQL service. The driver supports two types of authentication:

You must specify the URL of the GraphQL service. For this article, you will pass the connection string as a parameter to the create_engine function. Create a connection string using the required connection properties. 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).Ĭonnecting to GraphQL data looks just like connecting to any relational data source. With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live GraphQL data in Python.

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 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. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.

Graphql python mongodb