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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Chapter 7: Designing and Developing ETL Code in SAS

This chapter demonstrates how to design and develop extract, transform, and load (ETL) code in SAS. The SAS data warehouse serves users, and the main services it provides has to do with presenting data in a manner that is easy to understand, analyze, and visualize. Therefore, successful SAS data warehouses will have ETL protocols that are organized, transparent, and evidence-based, because these practices lead to providing valid, accurate, and easy-to-use data to users.

This chapter will cover these main topics:

How to conduct preliminary research on categorical and continuous variables to inform the development of ETL code
How to split up the tasks of planning coding from the actual coding, and how this can lead to better planning and oversight of the data warehouse
How to develop transformation code that matches the coding plans developed, and check transformed variables for accuracy