![]() Relational: Return individual, related tables from hierarchical data.FlattenedDocuments: Implicitly join nested documents and their parents into a single table.The data provider returns nested elements as aggregates of data. Document (default): Model a top-level, document view of your JSON data.The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations. ![]() ![]() See the Getting Started chapter in the data provider documentation for authentication guides.Īfter setting the URI and providing any authentication values, set DataModel to more closely match the data representation to the structure of your data. The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models JSON APIs as bidirectional database tables and JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). 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 JSON, the driver pushes supported SQL operations, like filters and aggregations, directly to JSON and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).Ĭonnecting to JSON services 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 JSON services in Python. This article shows how to connect to JSON with the CData Python Connector and use petl and pandas to extract, transform, and load JSON services. With the CData Python Connector for JSON and the petl framework, you can build JSON-connected applications and pipelines for extracting, transforming, and loading JSON services. The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively.
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