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SAS Data Maker | SAS Viya December 2025 and January 2026 Release In this month’s episode of the SAS Viya Release Highlights Show, discover how SAS Data Maker uses synthetic data to generate safe, high-quality data and accelerate innovation. We also explore the 2025.12 and 2026.01 SAS Viya releases, featuring SAS Viya Copilot for Intelligent Decisioning, Data Step Debugger enhancements, and new SAS ESP APIs with OpenAPI support.
SAS Viya Copilot in Action (Part 1): Intelligent Assistance for Building Model Studio Pipelines Sharad Saxena kicks off a three-part series showing how SAS Viya Copilot provides intelligent, conversational assistance in Model Studio, helping users build, run, and understand machine learning pipelines more efficiently. He walks through core capabilities like submitting prompts, asking modeling questions, adding pipeline nodes, and interpreting results with automated explanations and summaries.
Don't crash this ship! DuckDB is heading straight for Iceberg! Joe Cabral explores how SAS Viya’s SAS/ACCESS Interface to DuckDB now supports Apache Iceberg, enabling efficient access, querying, and updates of data stored in open formats like Parquet across cost‑effective object storage. He explains why combining DuckDB and Iceberg improves performance, flexibility, and data management while keeping data where it lives.
Selective backup and restore of SAS Viya Content Gerry Nelson explains how SAS Viya administrators can selectively back up and restore specific content (such as reports, models, and jobs) without performing a full system backup or causing downtime. He shows how to use the SAS Viya transfer service and CLI to gain more granular, efficient control over content protection while keeping environments running.
Configuring New Publishing Destinations via a User Interface in SAS Environment Manager Sophia Rowland shows how SAS Viya administrators can configure new publishing destinations directly through the user interface, reducing reliance on command-line tools. She walks through setting up and managing destinations more easily, helping teams streamline model and content publishing workflows.
How can SAS improve my city driving experience? Peter Christie explores how SAS analytics and AI can improve everyday city driving, from optimizing traffic flow and parking to enhancing road safety and reducing congestion. He highlights real-world use cases where data-driven insights help cities deliver smoother, safer, and more efficient urban mobility experiences.
How SAS Event Stream Processing and MCP Bring Real‑Time Intelligence to GenAI Michael McCahill explains how integrating SAS Event Stream Processing (ESP) with the Model Context Protocol (MCP) enables GenAI systems to access trusted, real-time operational data instead of relying solely on static or historical information. Together, ESP and MCP allow AI applications to detect events, analyze context, and act immediately, bringing real-time intelligence into enterprise AI workflows.
The Best of Both Worlds: Using SAS and R Together in SAS Viya Workbench Shelby Taylor shows how SAS Viya Workbench lets users combine SAS and R in a single, connected environment, making it easy to move seamlessly between languages without switching tools. Using a practical example, she demonstrates how to prepare data in SAS and analyze it in R, leveraging the strengths of both within one workflow.
Going for Gold: Visualizing Olympic Medal Counts with SAS Viya Greg Treiman walks through an end‑to‑end data science workflow in SAS Viya, showing how to collect Olympic medal data, prepare it, and create compelling visualizations like maps and charts. He demonstrates how SAS Viya combines Python, SAS programming, and visualization tools to turn real‑world data into clear, engaging insights.
Using network analytics to analyze social interactions among needle sharers Hossein Tohidi demonstrates how SAS Viya network analytics can uncover hidden patterns in needle‑sharing networks by identifying highly connected individuals, risk pathways, and community structures. Using real public health data, he shows how network measures like centrality and community detection can support more targeted and effective intervention strategies.
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