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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

Loading transformed data

As described in Chapter 4, Managing ETL in SAS, in a SAS data warehouse, it's not uncommon to receive monthly or annual files that require regular ETL. Imagine receiving the BRFSS files from 2016, 2017, and 2018, and needing to process them. If the datasets are all named according to a particular naming convention, we can use macro code to automatically load the data files and put them through an ETL protocol.

The easiest way to demonstrate this with a simple exercise is to have us generate the multiple files we will later read in. That way, we can concentrate on writing the load macro, and not whether the data will cooperate. For this exercise, we will use the dataset Chap8_2. This dataset has only two variables: _STATE and FMONTH. As with the previous file, the only states included in _STATE are codes 12, 25, and 27, for Florida, Massachusetts, and Minnesota, respectively. FMONTH contains the month the survey was conducted coded as a number, 1 through...

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