Persistence of Tables Created in Vantage by Teradata Python Package - Persistence of Tables Created by the teradataml Package in Vantage - Teradata Package for Python

Teradata® Package for Python User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
March 2024
Language
English (United States)
Last Update
2024-04-09
dita:mapPath
nvi1706202040305.ditamap
dita:ditavalPath
plt1683835213376.ditaval
dita:id
rkb1531260709148
Product Category
Teradata Vantage

When users run a ML Engine analytic function, results are stored as tables in the database that is specified in the Vantage connection.

However, not all of these resulting tables may be persistent (in permanent storage) in the connection database. Specifically, tables that store models produced by analytic functions are non-persistent work tables (temporary tables).

The difference is that tables in permanent storage persist across different sessions, whereas temporary tables are automatically dropped at the end of a session.

Therefore, if the user establishes a Vantage connection in Python and calls an analytic function that creates an analytic model table in the database, when the user eliminates the connection, the database session will be terminated and the model table will be automatically dropped from the database.

To preserve a non-persistent model table created by teradataml, use the copy_to function with the model as a table object input to the function, before disconnecting from the session where the model table was created.