Book Image

Mastering SAS Programming for Data Warehousing

By : Monika Wahi
Book Image

Mastering SAS Programming for Data Warehousing

By: Monika Wahi

Overview of this book

SAS is used for various functions in the development and maintenance of data warehouses, thanks to its reputation of being able to handle ’big data’. This book will help you learn the pros and cons of storing data in SAS. As you progress, you’ll understand how to document and design extract-transform-load (ETL) protocols for SAS processes. Later, you’ll focus on how the use of SAS arrays and macros can help standardize ETL. The book will also help you examine approaches for serving up data using SAS and explore how connecting SAS to other systems can enhance the data warehouse user’s experience. By the end of this data management book, you will have a fundamental understanding of the roles SAS can play in a warehouse environment, and be able to choose wisely when designing your data warehousing processes involving SAS.
Table of Contents (18 chapters)
1
Section 1: Managing Data in a SAS Data Warehouse
7
Section 2: Using SAS for Extract-Transform-Load (ETL) Protocols in a Data Warehouse
12
Section 3: Using SAS When Serving Warehouse Data to Users

Chapter 11

  1. In de-identification, identifiers need to be placed on a server. If they are placed on a server not connected to the internet, there is no chance for a hacker to steal the identifiers by connecting to the server through the internet. Therefore, if real identifiers are placed on a server not connected to the internet, and while on that server, they are replaced with identifiers that are consistent but cannot be decoded to actual people, then the resulting data is safe. Even if they are placed on an internet server and stolen, the identifiers cannot be decoded by whoever stole the data. If no other identifying data is in the stolen data, then the privacy and confidentiality of the people represented in the database records are preserved.

  2. As described throughout this book, data warehouse and data lake projects are enormously expensive and effort-intensive, so it is necessary to make them valuable so that analyst users use them. When data systems are set up such that...