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Best Practices in SAS® Programming to Support Ever-Increasing Data Loads
| Content Provider | Semantic Scholar |
|---|---|
| Author | Schmitz, John Robert |
| Copyright Year | 2020 |
| Abstract | In most large organizations, SAS serves a pivotal role in data processing for warehousing, reporting, analytics and more. The SAS language provides multiple tools and options to streamline these data processing needs that may be unfamiliar to many developers. This paper will present some well-known and lesser-known SAS methods for efficient data handling. The focal point of the paper is more geared towards what is available and the use cases for each technique, rather than a detailed how-to on a specific solution. INTRODUCTION SAS often serves a critical data processing role for many large businesses across industries. It offers a rich set of programming features and options combined with extensive data access features provided through an array of SAS/ACCESS engines. These capabilities allow SAS to extract and process data from multiple sources and in some cases directly process data within the system where those data are stored. Learning the capabilities of these various programming features, procedures, and SAS/ACCESS engine tools can be a challenge, especially when there are multiple methods to accomplish the same end. Knowing when to apply each method to efficiently accomplish the desired end is an art that can produce huge savings to the organization in terms of resource utilization and timely solutions. The focus of this paper is to highlight some popular and powerful tools that the SAS developer can leverage for data extraction, transformation and load (ETL) processing. The discussion leans more towards the application of common tools that SAS developer should understand rather technical details regarding how a specific solution would be implemented. The comments here apply primarily to SAS V9 programming. Many of the topics can be applied to SAS Viya as well, but the CAS engine and memory-resident data design of SAS Viya create a unique dynamic that is not considered directly within this topic. CORE BACKGROUND CONSIDERATIONS FOR ANY ETL PROCESS Whenever ETL efforts are required for a SAS project, an initial assessment of key factors can direct the programmer to more efficient extraction methodologies. Choice of programming methods and join alternatives can have a dramatic impact on resource utilization and hence query runtime. Some key considerations for this initial assessment include: • The data volume required for the extract. • The number of distinct data source tables and source locations required for the effort. • A general idea of the ratio of required data to the entire table size for larger tables. • Presence of relevant sort order or indexing on larger source tables. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2020/4929-2020.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
