Data in JSON or CSV Format | Native Object Store | Teradata Python Package - Data in JSON or CSV Format - Teradata Vantage

Foreign table created in JSON or comma-separated values (CSV) format typically contains two columns:

• Location
• Payload

You can create a teradataml DataFrame on a foreign table using "DataFrame()" or "DataFrame.from_table()", the same way to create a teradataml DataFrame on a regular table. With the created DataFrame, you can easily access the data in these columns and process the data using teradataml DataFrame API or other Python packages.

• Example: Create teradataml DataFrame on NOS foreign table in JSON data format
• Example: Create teradataml DataFrame on NOS foreign table in CSV data format

Though teradataml provides direct access to the data in foreign tables, actual data that resides in 'Payload' column and path variables for the foreign table can be accessed in one of the following ways:

• Accessing Columns and Path Variables using DataFrame.from_query()
• Accessing Columns and Path Variables by Creating a View on Foreign Table
• Accessing Columns and Path Variables by Creating a Table from Foreign Table