- DBAs
- The DBA, typically the Teradata Database Administrator, makes sure the integrity and health of the production system. They also want to share the workload where it makes sense. The portlet provides integration aspects into Teradata workload management strategies, in particular Teradata Active System Management (TASM), allowing the data lab environment to be managed using wildcard classifications. After workload management is set up and a portion of database space is allocated, data labs management can be given to one or more data lab owners.
Typically, the DBA architects the lab group structure, creates lab groups, assigns lab group owners to manage approvals, manages space, configures workload management, and can approve requests.
The DBA also makes sure the proper Teradata Database privileges are given and works with the Teradata Viewpoint Administrator to make sure the Data Labs portlet permissions are assigned to the Viewpoint role allowing users in that role to create, edit, and delete lab groups.
- Power Users
- The power user, typically owns and manages data labs, is an IT representative for the team, and manages the external repositories for data proofing. They act as the liaison between the DBA and the business analyst and end users. With the Data Lab framework and appropriate Viewpoint portlets, management responsibilities are simplified and are more efficient with more productive results. Typically, the power user can be either the lab group owner or the lab owner.
- Lab group owners send and approve requests, can delegate approval authority, and receive lab group email notifications.
- Lab owners manage labs, can approve requests if given approval authority, receive lab email notifications, and use labs to analyze data.
- End Users
- The end user, typically a business analyst, explores data and tests theories. End users want to easily implement the proofing of data and then migrate new data into production information. Data labs provide proof that test results are consistent when implemented in production. The end user receives fresh data and does not have to wait for periodic data loads from production.
Typically, the end user sends requests, views objects in a lab, and uses labs to analyze data.