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Mastering SAS Programming for Data Warehousing

Mastering SAS Programming for Data Warehousing

By : Monika Wahi
4.4 (5)
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Mastering SAS Programming for Data Warehousing

Mastering SAS Programming for Data Warehousing

4.4 (5)
By: Monika Wahi

Overview of this book

SAS is used for various functions in the development and maintenance of data warehouses, thanks to its reputation of being able to handle ’big data’. This book will help you learn the pros and cons of storing data in SAS. As you progress, you’ll understand how to document and design extract-transform-load (ETL) protocols for SAS processes. Later, you’ll focus on how the use of SAS arrays and macros can help standardize ETL. The book will also help you examine approaches for serving up data using SAS and explore how connecting SAS to other systems can enhance the data warehouse user’s experience. By the end of this data management book, you will have a fundamental understanding of the roles SAS can play in a warehouse environment, and be able to choose wisely when designing your data warehousing processes involving SAS.
Table of Contents (18 chapters)
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1
Section 1: Managing Data in a SAS Data Warehouse
7
Section 2: Using SAS for Extract-Transform-Load (ETL) Protocols in a Data Warehouse
12
Section 3: Using SAS When Serving Warehouse Data to Users

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

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