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

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781789532371

Length 494 pages

Edition 1st Edition

Tools

SAS

Concepts

Application Development

Author (1):

Monika Wahi

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Table of Contents (18) Chapters

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

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Needs of data warehouse users

Tim Mitchell, a business intelligence architect who writes on many data science topics, published an article titled Why Data Warehouse Projects Fail (a link to the article is available in the Further reading section). He noted that many data warehouse projects fail—some have estimated the rate of failure to be as high as 50%. Since Mitchell has experience of rescuing data warehouse projects, he listed several reasons why he thinks data warehouse projects fail:

The data warehouse does not have an objective: In Chapter 7, Designing and Developing ETL Code in SAS, we described a hypothetical data warehouse with the objective of studying the quality of life (QoL) of United States veterans after they leave the service. However, Mitchell points out that in a surprising number of cases, leadership failed to answer the question of why the organization was building a data warehouse. Some would say they assumed their organization needed one simply...