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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

Chapter 2: Reading Big Data into SAS

This chapter will introduce SAS data warehouse developers to the issues and strategies surrounding reading big data into SAS. SAS has native data formats *.SAS7bdat and XPT, but also reads in non-native formats such as *.csv and *.txt. There are advantages and disadvantages to storing data in any of these formats, and special considerations need to be made when preparing transfers of big data in these formats. SAS warehouse developers are tasked with reading data from multiple different source systems into SAS, and this can be done using infile statements, PROC IMPORT, or a strategy that combines both techniques. Because SAS has proficiency in handling big data, SAS data warehouses often need to read in large extracts from legacy systems, many of which provide fixed-width extracts. These can be particularly challenging to read into SAS, and so this chapter also describes approaches to tackling these challenges.

This chapter takes a deep dive...

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