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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

Limitations of arrays

Although array processing is usually necessary as part of maintaining a SAS data warehouse, using arrays also introduces limitations, mainly concerning issues surrounding variable naming and renaming, which will be discussed here. Issues associated with troubleshooting array programming will also be covered.

Naming limitations in SAS arrays

To speed up processing, we renamed the input array variables from CM1-CM11, while to handle recoding the grouping variables, we used a condition in our array processing to accommodate the slightly different native coding of DIABETE3 compared to the other co-morbidity variables. It's easy to imagine how this situation of renaming native variables and creating long data steps with conditions can become even more complex.

Imagine that our dataset contains 100 disease variables and that they were coded according to five different systems (one or two dominant ones, and three rarely used ones). Theoretically, it would...

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