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SAS Warehouse Technology Exam
This exam will retire on December 31, 2008. A new exam will be offered in this track and will focus on SAS Data Integration Technology. Anticipated exam release date early 2009.Audience
A candidate for the Warehouse Technology exam is knowledgeable of the SAS 9.1 warehouse solution, including SAS ETL Studio, SAS Integration Technologies, SAS SPDE (Scalable Performance Data Engine), SAS SPDS (Scalable Performance Data Server), SAS Application Servers (e.g. Metadata, Workspace, OLAP, Stored Process, CONNECT, SHARE), SMC (SAS Management Console), and related plug-ins such as SAS OLAP Administrator. In addition, the candidate should be able to perform standard transformations including resolving data quality issues using the SAS Data Quality Solution. The candidate should also be able to extract and load data using SAS/ACCESS interfaces, SAS Data Surveyors, and other SAS data access technologies.The candidate should have current warehousing experience (e.g. designing, managing, deploying) using SAS technologies. In addition, the candidate should have a strong working knowledge of data management issues (e.g. cleansing, transformation, file structures) and should be aware of data modeling techniques.
Test Content
Setting up the Warehouse EnvironmentPrepare the infrastructure for the ETL processes to support the construction and management of the warehouse by using SAS Management Console as a centralized point of control for performing administrative tasks such as defining/managing/monitoring servers, setting up authorizations, authentication, managing metadata and libraries, creating/configuring repositories, and scheduling.
Configure, administer, and use the integrated change management system for large scale implementations to allow multi-user concurrent development.
Building the Warehouse
Define source metadata structures and verify source data definitions in order to load data into the warehouse.
Import and integrate metadata from external systems to a central repository to facilitate the sharing of information for tasks such as ETL processes.
Define the target metadata structures (e.g. tables, databases, datamarts, flatfiles, reports, OLAP structures) in order to move metadata from the source to target data structures by importing a model, referencing an existing data target or developing a new data target.
Define the data extract, transform, and load processes that are consistent with the business rules and environmental/technical constraints in order to perform the necessary restructuring and make the data available for exploitation.
Cleanse data by checking for inconsistent/erroneous/duplicate data and correcting the issues in order to ensure data quality in terms of accuracy, integrity, consistency, and validity.
Deploying and Maintaining the Warehouse
Promote and replicate the warehouse from the development to the test to the production environment in order to manage the lifecycle of the warehouse.
Deploy the warehouse in order to complete the development cycle by conducting tasks such as creating/automating/maintaining job flows and scheduled processes, defining alerts, optimizing performance of the batch load and queries, optimizing resource usage, and publishing metadata.
Construct and validate warehouse maintenance and management processes (e.g. aging, versioning, archiving, backup and restore, and scheduling) according to the design specifications in order to ensure the sustainability and usability of the warehouse.