SAS vs Superset: What are the differences?

1. Data Visualization: SAS is primarily a statistical analysis software, whereas Superset is a modern data exploration and visualization platform. Superset offers interactive, customizable visualizations using various chart types, while SAS focuses more on data analysis and statistical modeling without the same level of visualization capabilities.

2. Open Source vs Proprietary: Another key difference between SAS and Superset is their licensing model. SAS is a proprietary software that requires a license to use, while Superset is an open-source tool that can be freely downloaded and used by anyone.

3. Programming Language: SAS uses its own proprietary programming language for data analysis and manipulation, known as SAS Programming Language. On the other hand, Superset leverages SQL and Python for querying and data manipulation, making it more accessible for users familiar with these languages.

4. Collaboration and Sharing: Superset allows for easy collaboration and sharing of dashboards and visualizations with other users through its web-based interface, facilitating real-time data insights and decision-making. SAS, on the other hand, lacks the same level of collaborative features and might require additional setup for sharing results.

5. Scalability and Performance: When it comes to handling large datasets and high-performance requirements, Superset excels due to its architecture that can be easily scaled horizontally. SAS, while powerful in data analysis, may face limitations in scalability and performance for big data analytics tasks.

6. Community Support and Customization: The Superset community is actively involved in enhancing the platform and providing support to users through forums and documentation. This allows for extensive customization and integration with other tools. In contrast, SAS relies more on official support channels and may have limited flexibility for customization compared to Superset.

In Summary, SAS and Superset differ in their focus on statistical analysis versus data exploration and visualization, licensing models, programming languages used, collaboration features, scalability, and community support.