Databases Reference
In-Depth Information
When the data in the staging area is valid, SAS Data Integration Studio jobs load
that data into the central data warehouse.
Step 3: Create Data Marts or Dimensional Data
After the data has been loaded into the data warehouse, SAS Data Integration
Studio jobs extract data from the warehouse into smaller data marts, OLAP structures,
or star schemas that are dedicated to specific business dimensions, such as products,
customers, suppliers, financials, and employees. From these smaller structures,
additional SAS Data Integration Studio jobs generate, format, and publish reports
throughout the enterprise.
Planning a Data Warehouse
The following steps outline one way of implementing a data warehouse.
1
Determine your initial needs:
a
Generate a list of business questions that you would like to answer.
b
Specify data collections (data marts or dimensional data) that will provide
answers to your business questions.
c
Determine how and when you would like to receive information. Information
can be delivered based on events, such as supply shortages, on time, such as
monthly reports, or simply on demand.
2
Map the data in your enterprise:
Locate existing storage locations for data that can be used to populate your
data collections.
Determine storage format, data columns, and operating environments.
3
Create a data model for your central data warehouse:
Combine selected enterprise data sources into a denormalized database that
is optimized for efficient data extraction and ad hoc queries. SAS Data
Integration Studio resolves issues surrounding the extraction and
combination of source data.
Consider a generalized collection of data that might extend beyond your
initial scope to account for unanticipated business requirements.
4
Estimate and order hardware and software:
Include storage, servers, backup systems, and disaster recovery.
Include the staging area, the central data warehouse, and the data marts or
dimensional data model.
5
Based on the data model, develop a plan for extracting data from enterprise
sources into a staging area. Then specify a series of SAS Data Integration Studio
jobs that put the extraction plan into action:
Consider the frequency of data collection based on business needs.
Consider the times of data extraction based on system performance
requirements and data entry times.
Note that all data needs to be cleansed and validated in the staging area to
avoid corruption of the data warehouse.
Consider validation steps in the extraction jobs to ensure accuracy.
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