In this model, SAS ETL Studio jobs are used to perform the following tasks :

1. Extract enterprise data into a staging area.

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 ETL 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 ETL Studio

SAS ETL 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 24).

• Cleanse extracted data and populate a central data warehouse (see "Step 2: Cleanse , Validate, and Load" on page 24).

• Create dimensional data that reflects important business needs (see "Step 3: Create Data Marts or Dimensional Data" on page 25).

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 ETL 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 ETL 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.