![]() SAS Viya and Azure Synapse empower everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster. When combined with Azure Synapse, it’s easy to rapidly operationalize insights across the entire organization, enabling everyone to be more productive with data. Running natively on Microsoft Azure, SAS Viya scales to fit the scope of all analytics challenges, from experimental to mission critical. SAS Viya addresses the entire scope of analytics requirements, including machine learning, text analytics, computer vision, forecasting, econometrics and optimization. SAS’ integration with Azure Synapse starts with connectivity and extends to native in-engine operationalization of models within the Synapse SQ engine. ![]() It’s designed to deliver better decisions, maximum value and trusted outcomes, regardless of the size or type of data, algorithm used, or how the analtyics are deployed. SAS Viya is a cloud native AI, analytic and data management platform that runs on a modern, scalable architecture. The integrated web studio development environment enables developers to ingest, prepare, manage and serve data for scenarios ranging from descriptive reports to predictive machine learning. It eliminates the silos between databases and data lakes and empowers customers to analyze any data at any scale. ![]() Azure Synapse is a unified platform for analytics, blending big data, data warehousing and data integration into a single cloud native service. The platform combines the power of Azure Synapse and SAS® Viya® to offer a complete data and analytics solution. Fortunately, the new partnership between Microsoft and SAS provides a superior, unified analytical platform that allows data scientists to accelerate the data and analytics life cycle, driving projects swiftly to insights and decisions. It’s a frustrating, cumbersome and time consuming process. This diverse and complex landscape causes data scientists to spend an inordinate amount of time searching for the right data and preparing this information for analytics. ![]() All analytics projects have data as their foundation and this data is usually spread across a variety of databases, storage systems and locations. ![]()
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