BASE SAS v/s CASL: A Simple Comparison

This article helps you understand the differences between BASE SAS and CASL (Cloud Analytic Services Language), so you can choose the best tool for your specific analytical needs and infrastructure.

It highlights the strengths and capabilities of both, guiding you to the best solution for various analytical challenges and business goals.

Why CASL is so important in SAS Viya?

BASE SAS vs. CASL: A Simple Comparison

Language Structure:

• BASE SAS: Combines the DATA step with SAS Procedures, which are used for data manipulation and analysis.
• CASL: A statement-based scripting language that is case insensitive and runs CAS actions. It is designed to interact seamlessly with the SAS Viya environment.

Processing Engine:

• BASE SAS: Runs on a traditional SAS server, which processes tasks sequentially on a single machine.
• CASL: Interacts with SAS Cloud Analytic Services (CAS), enabling distributed computing across multiple nodes, which enhances processing speed and efficiency for large datasets.

Data Handling:

• BASE SAS: Processes data sequentially, which can be slower for large volumes of data.
• CASL: Uses in-memory processing, allowing for much faster handling of big data as all operations are performed in memory across distributed nodes.

Conditional Logic:

• BASE SAS: Requires SAS/MACRO for implementing complex conditional logic, adding an extra layer of programming.
• CASL: Has built-in conditional logic capabilities similar to having MACRO functionality integrated directly into the language.

Procedure Execution:

• BASE SAS: Uses PROC statements to execute procedures directly.
• CASL: Uses CAS actions instead of traditional procedures, though many CAS actions correspond to CAS-enabled PROC statements, providing similar functionality in a distributed environment.

Data Access:

• BASE SAS: Primarily works with local data and SAS datasets stored on the server, making it well-suited for traditional data processing and analysis tasks.
• CASL: Designed to access and manipulate data in CAS tables, which allows for distributed data processing and better performance with large and complex datasets.

Scalability:

• BASE SAS: Limited by the capacity of a single machine, making it less scalable for very large data sets and high-performance needs.
• CASL: Designed for scalable, distributed computing environments, making it capable of handling very large data sets efficiently by distributing the load across multiple servers.

Language Integration:

• BASE SAS: Primarily SAS-centric and focuses on SAS procedures and macros.
• CASL: Can be accessed via multiple interfaces, including SAS, Python, R, Java, and REST APIs, providing greater flexibility and integration with other programming environments.

Analytics Lifecycle Support:

• BASE SAS: Supports traditional analytics workflows, including data preparation, analysis, and reporting.
• CASL: Designed to support the entire analytical lifecycle, including data management, analytics, and scoring, within a modern cloud-based infrastructure.

Performance:

• BASE SAS: Efficient for traditional data processing tasks on small to moderately large datasets.
• CASL: Optimized for high-performance analytics, especially with very large datasets, due to its distributed, in-memory processing capabilities.

Code Generation:

• BASE SAS: Often requires macro programming for dynamic code generation, which can be complex and less flexible.
• CASL: Offers more flexible options for dynamic code generation and execution, making it easier to automate and manage complex analytical tasks.