This repo contains a variety of presentations, workshops and blog posts on how to use the Python SWAT package in SAS Viya to use Cloud Analytic Services (CAS server) for massively parallel processing.
- Python SWAT package documentation
- Python SWAT GitHub page
- CAS Action documentation. All CAS actions can be used as methods in the SWAT package. Check out my blog series CAS Action! - a series on fundamentals to learn more about using CAS actions. This blog series using the CAS language to execute CAS actions, but you can execute these in the Python SWAT package with some slight modification to the syntax.
This is an Ask the Expert presentation I did. You can sign up to watch the onDemand recording.
Join this webinar to learn how to process, analyze and visualize data using the Python SWAT package in SAS Viya. The webinar will introduce the fundamentals of SAS Viya and Cloud Analytic Services, or the CAS Server. We will explain why you would want to use the CAS Server and Python together. We’ll also cover how SAS, using the SWAT package, blends the world of Pandas and SAS Viya to help you derive insights and solve big analytical questions.
You will learn:
- Why you should use SAS Viya and Python.
- How to access data in Viya using Python and the SWAT package.
- How to process and analyze data using the Pandas API in the SWAT package.
- How to visualize data in Viya using Python.
SAS Viya provides many integration points with open source for data preparation, model development and model deployment for users in different roles along the analytics life cycle.
We will show you how, through a use case starting with raw data, you can use Viya and open source together to create and deploy superior models to drive your organizational goals.
You will learn about:
- How Viya was designed to integrate with open source languages and packages to enable development and deployment of superior models for your organization.
- The key integration points between Viya and open source.
- How open source developers can take advantage of the massively parallel processing Cloud Analytics Services in-memory engine of Viya to speed model development against large data.
Getting started with the Python SWAT package in SAS Viya. A quick introduction into accessing, exploring, preparing and analyzing data in the Cloud Analytic Services (CAS Server) massively parallel processing environment.
YouTube video release coming shortly.
(in progress). Will be presenting at the Buffalo State data science seminar series. This presentation is titled The Best of Both Worlds: Analytics with SAS(R) Viya(R) and Python. By the end of the presentation you will:
- Understand the massively parallel processing capabilities in SAS Viya
- Integrate Python and SAS Viya to process data throughout the analytics life cycle
- Create a dashboard using SAS Visual Analytics
Python SWAT package workshop.
Python SWAT package workshop.
You can find all of my notebooks for my Getting Started with Python Integration to SAS® Viya® - Index here.
The posts show how to use the Python SWAT package to process data in SAS Viya's massively parallel processing environment, Cloud Analytics Services (CAS Server).
My presentation for SAS Explore. You an view the presentation on YouTube.
The presentations shows how to unite the Python language with the analytic power of SAS. In this presentation, you learn how to use the SWAT (SAS wrapper for analytics transfer) package to take advantage of the SAS Cloud Analytic Services (CAS) engine in SAS Viya for massively parallel processing (MPP). We begin with a quick overview of SAS Viya and the CAS engine and then discuss how to leverage the strengths of the CAS engine and your local Python client, how to connect Python to CAS, and how to access and load data into CAS's MPP environment. Then we dive into exploring, preparing, and analyzing data using the CAS server, taking advantage of the CAS server's distributed processing power using familiar pandas API and CAS actions from the SWAT package. Lastly, we learn how to unite traditional python packages such as Pandas and matplotlib with the summarized results from the CAS server for additional processing and visualization.