SAS Viya Data Management

Governs and monitors data pipelines to ensure AI models are trained and deployed on high-quality, transparent data

Modular architecture allowing teams to extend, adapt and innovate without being locked into closed systems

Intelligent data fabric that integrates data across hybrid and multicloud environments with unified approach making data instantly available for analytics and AI

Automates and orchestrates data workflows to eliminate manual, fragmented processes, speeding pipeline delivery and reducing errors

Connects to virtually any data source – cloud, on-premises or hybrid – without complex integration

Create realistic, privacy-compliant synthetic data for testing, training and trustworthy AI

Unifies data across clouds, systems and formats without unnecessary movement, which reduces duplication, accelerates insights and lowers infrastructure costs

Transform, manage and prepare data for analytics and AI with a modern, code-friendly and visual interface

Continuously monitors, scores and improves data for accuracy, completeness and consistency with automated processes that identify and correct errors at scale

High-performance, cloud-native database designed for analytics at scale

Built-in governance policies, data lineage tracking and embedded compliance to reduce regulatory risk and enable secure collaboration

Track and visualize data lineage to ensure transparency and compliance across data pipelines

Ensures trusted, well-governed data with policies, lineage and controls built into your stack

Integrates data across cloud, hybrid and on-premises environments, optimizing access and reducing costs

Integrates seamlessly with modern data lakehouse platforms like Databricks, Snowflake and Azure OneLake to simplify large-scale data management

Uses open standards to integrate seamlessly with modern ecosystems like Snowflake, Databricks, Python and Postgres without vendor lock-in

Automated tools and expert guidance to simplify transition from SAS 9 to SAS Viya

Ongoing data observability to build confidence with transparency and trust in data quality

Boosts performance by processing data where it resides, reducing unnecessary movement with optimized ingestion, preparation and model execution

Push SAS analytics directly inside databases to speed performance and reduce costs

Handle growing data volumes with elastic infrastructure