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Mastering SAS Programming for Data Warehousing

You're reading from   Mastering SAS Programming for Data Warehousing An advanced programming guide to designing and managing Data Warehouses using SAS

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781789532371
Length 494 pages
Edition 1st Edition
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Author (1):
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Monika Wahi Monika Wahi
Author Profile Icon Monika Wahi
Monika Wahi
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Managing Data in a SAS Data Warehouse
2. Chapter 1: Using SAS in a Data Mart, Data Lake, or Data Warehouse FREE CHAPTER 3. Chapter 2: Reading Big Data into SAS 4. Chapter 3: Helpful PROCs for Managing Data 5. Chapter 4: Managing ETL in SAS 6. Chapter 5: Managing Data Reporting in SAS 7. Section 2: Using SAS for Extract-Transform-Load (ETL) Protocols in a Data Warehouse
8. Chapter 6: Standardizing Coding Using SAS Arrays 9. Chapter 7: Designing and Developing ETL Code in SAS 10. Chapter 8: Using Macros to Automate ETL in SAS 11. Chapter 9: Debugging and Troubleshooting in SAS 12. Section 3: Using SAS When Serving Warehouse Data to Users
13. Chapter 10: Considering the User Needs of SAS Data Warehouses 14. Chapter 11: Connecting the SAS Data Warehouse to Other Systems 15. Chapter 12: Using the ODS for Visualization in SAS 16. Assessments 17. Other Books You May Enjoy

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...

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