TRUSTED DECISIONS WITH SAS® VIYA® ON GOOGLE CLOUD
Google Cloud and SAS are making it easier than ever to migrate data and analytics to a scalable cloud environment with uncompromised security.
Why choose SAS® on Google Cloud?
Avoid vendor lock-in with Google Cloud’s commitment to open source, multicloud and hybrid cloud, allowing you to use your data and run your analytics applications on any cloud or in any environment. Google Cloud provides consistency between public and private clouds, enabling businesses to modernize and developers to build faster in any environment.
Data where you want it
Get access to all the data in a big data world with SAS Viya on GCP, which enables:
- Centralized management.
- Faster access to data.
- A scalable cloud environment that leverages Kubernetes, containers and other cloud-native capabilities.
Time & cost savings
Easily migrate existing data sources to the cloud:
- Perform easy, cost-effective, homogeneous migrations to managed services.
- Leverage SAS Viya with Google Big Query to enhance business decisions from data across clouds with a flexible, multicloud solution.
- Build on fully managed open source databases designed for mission critical applications.
Protect your data, applications, infrastructure and customers from fraudulent activity, spam and abuse with the same infrastructure and security services Google uses.
- GCP’s networking, data storage and compute services provide data encryption at rest, in transit and in use.
- Advanced security tools support compliance and data confidentiality.
How SAS® on Google Cloud Can Help
SAS Viya has been developed to run natively on Google Cloud. This brings the longest-standing leader in analytics to Google Cloud, which is noted for its broad network, uncompromised security and ability to easily move existing data sources to Google Cloud.
Access powerful analytics via APIs, open source languages and no-code visual tools that enable business analysts and data scientists to collaborate.
Provision only what you need, when you need it – from a single data science workbench to a shared, massively parallel compute engine.
Prepare data and get models into production faster with machine-learning-driven automation, world-class model management and end-to-end governance.