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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
Tools
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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

Summary

This chapter provided an overview of planning the ETL approach and creating transformation code. First, we selected native variables to include in our SAS data warehouse, then designed transformed variables to derive during ETL. We used PROC FREQ and PROC UNIVARIATE to study our native variables and make good design decisions about our transformed variables. We documented our decisions in a data dictionary, which we then used as a guide when we created our transformation code. We used SAS data steps to create grouping variables and two-state flags and recoded continuous variables. We checked the variables for accuracy in recoding as we created them, and then we exported a final analytic dataset.

These skills are important to know when running a data warehouse. Making a data dictionary is a great skill to have for planning variables for transformation, as well as for keeping documentation about both native and transformed variables. It is helpful to become adept at using...

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