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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

Questions

  1. Why must raw datasets direct from the data provider be stored in a highly secure area in the warehouse environment?

  2. Why is it better to maintain many modular code files instead of one big long code file?

  3. In a dataset that contains 10 different cost variables, what is the main advantage and disadvantage of naming the variables COST1, COST2, and so on up to COST10?

  4. How is declaring formats in SAS different to declaring arrays?

  5. Why is it logical to assign programmers who already have access to raw data from the data provider used in ETL to also maintain SAS label and format code?

  6. Why is it helpful to have consistent naming conventions throughout the data warehouse?

  7. How is it helpful when senior programmers serve as SMEs for particular datasets?

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