- DBAs
- The DBA, typically your Teradata Database Administrator, ensures 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, your 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.
Also, the DBA ensures the proper Teradata Database privileges are given and works with the Teradata Viewpoint Administrator to ensure 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 their team where they manage 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, your power user can be either the lab group owner or the lab owner.
- As the lab group owner, they send and approve requests, can delegate approval authority, and receive lab group email notifications.
- As the lab owner, they 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. They want to easily implement the proofing of data and then migrate new data into production information. Data labs provide proof that test results will be 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 tables in a lab, and uses labs to analyze data.