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SAS Data Integration Studio 3.3- P10:This manual is a companion to the online Help for SAS Data Integration Studio. The Help describes all windows in SAS Data Integration Studio, and it summarizes the main tasks that you can perform with the software. The Help includes examples for all source designer wizards, all target designer wizards, and all transformation templates in the Process Library tree. | 40 Data Warehousing with SAS Data Integration Studio A Chapter 4 2 Cleanse and validate data and load a central data warehouse. 3 Populate a data mart or dimensional model that provides collections of data from across the enterprise. Each step of the enterprise data model is implemented by multiple jobs in SAS Data Integration Studio. Each job in each step can be scheduled to run at the time or event that best fits your business needs and network performance requirements. Data Warehousing with SAS Data Integration Studio Developing an Enterprise Model SAS Data Integration Studio helps you build dimensional data from across your enterprise in three steps Extract source data into a staging area see Step 1 Extract and Denormalize Source Data on page 40 . Cleanse extracted data and populate a central data warehouse see Step 2 Cleanse Validate and Load Data on page 40 . Create dimensional data that reflects important business needs see Step 3 Create Data Marts or Dimensional Data on page 41 . The three-step enterprise model represents best practices for large enterprises. Smaller models can be developed from the enterprise model. For example you can easily create one job in SAS Data Integration Studio that extracts transforms and loads data for a specific purpose. Step 1 Extract and Denormalize Source Data The extraction step consists of a series of SAS Data Integration Studio jobs that capture data from across your enterprise for storage in a staging area. SAS data access capabilities in the jobs enable you to extract data without changing your existing systems. The extraction jobs denormalize enterprise data for central storage. Normalized data many tables few connections is efficient for data collection. Denormalized data few tables more connections is more efficient for a central data warehouse where efficiency is needed for the population of data marts. Step 2 Cleanse Validate and Load Data After loading the staging area a second set of SAS Data Integration Studio
