A Data Location is a database schema into which successive Model Editions will be deployed.
Data Location Types
There are two types of data locations. The type is selected when the data location is created and cannot be changed afterwards:
The data location types are:
Development Data Location: A data location of this type supports deploying open or closed model editions. This type of data location is suitable for testing models in development and quality assurance environments.
Production Data Location: A data location of this type supports deploying only closed model editions. This type of data location is suitable for deploying data hubs in production environments.
|Be cautious when choosing the data location type, as it will determine the type of deployment operations that can be done. It is recommended to use only Production Data locations for Production and User Acceptance Test environments.|
Data Location Contents
A Data Location contains the hub data, stored in the schema accessed using the data location’s datasource. This schema contains database tables and other objects generated from the model edition.
The data location also refers three type of jobs (stored in the repository):
Installation Jobs: The jobs for creating or modifying, in a non-destructive way, the data structures in the schema.
Integration Jobs: The jobs for certifying data in these data structures, according to the model job definitions.
Purge Jobs: The jobs for purging the logs and data history according to the retention policies.
In addition of the deployed model editions, the jobs and their execution logs, the data locations also contains the configuration of:
The Continuous Loads, used by integration specialists to push data into the data location in a continuous way.
The Job Notifications Policies, sent under certain conditions when an integration job completes for administration, monitoring, or integration automation purposes.
The Data Notifications, used to propagate data from the data hub to downstream systems.
The Data Purge Schedule, to reduce the data location storage volume by pruning the history of data changes and job logs.