Typical challenges with on-premises SAS applications

DB Best completed several modernization and data management projects with customers using SAS-based systems. Our team established a proven optimization practice and identified several common challenges that we faced in these projects. They include relatively high licensing costs, tons of legacy code and the following issues:

Overwhelming features

The roots of SAS (previously Statistical Analysis System) go all the way back to the analysis of agricultural data to improve crop yields. Since then much water has passed under the bridge, and the SAS Platform developed impressively. Currently, SAS includes a large set of tools with multiple BI and ETL features focused on advanced analytics (statistical, predictive, data mining), as well as business intelligence and data management.

However, users often do not utilize most of these capabilities. Experts notice that SAS programs provide an extraordinary range of data analysis and data management tasks but remain difficult to use and learn. That’s why many customers are looking for help from subject-matter experts or even for a proven optimization path.


SAS Platform can mine, alter, manage and retrieve data from a variety of sources and as well as perform statistical analysis on such data. However, all SAS programs consist of DATA steps and PROC steps. DATA steps retrieve and manipulate data, while PROC steps analyze it. So, you need to address both steps by using different approaches.

When working with older SAS code, this can be rather limiting. Customers often need a fresh new look for modernizing their existing solutions with IoT, artificial intelligence, machine learning and other modern technologies. You can rely on third-party service providers or use the following proprietary SAS software:

You can see that SAS-based technologies can address these modernization opportunities as well as many other big data challenges in full.

Data storage

Another important point is data storage. You can use SAS tables (called data sets) for data storage. This is a default format for storing data in SAS. However, you can also store data in a wide range of databases including transactional, hierarchical, data warehouses, and multidimensional cubes. These include Microsoft SQL Server, Oracle Database, MySQL, Sybase, IBM DB2, Teradata, Apache Hadoop, and others. And of course, SAS supports major cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

So, when you hit the boundaries of internal SAS storage, be sure to take advantage of modern data platforms to make your BI solutions shine again. And DB Best data experts can help you reimagine your data storage and discover the best option in terms of performance and cost.

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Cloud opportunities for your SAS solutions

Lift and shift SAS Solutions to SAS Cloud services

For understanding the indisputable advantages of cloud solutions, SAS designed cloud-specific offerings. For example, SAS Viya, a cloud-enabled, in-memory analytics engine, is fully compatible with Amazon Web Services, Microsoft Azure, and Google Cloud Platform. The good news is that you can run your SAS-based workloads in the cloud. However, before adopting cloud technology, you still need to overcome all typical migration or version upgrade challenges.

The DB Best team can help you leverage these native SAS cloud applications as well as design the future-state architecture for your new cloud data infrastructure. You may also want to consider using SAS for Containers to run your analytic solutions in Docker or Kubernetes. This approach provides you with a flexible and efficient way to execute your SAS workloads at an uncompromisingly high level of performance.

Typically, the benefits of cloud adoption include agility, innovation, and high performance at a reasonable cost. What’s important is that we at DB Best know how to obtain all these benefits while maximizing your ROI.

Integrating SAS Solutions with modern cloud services

Until recently, many organizations did not want to consider cloud platforms for their business intelligence and analytics. We at DB Best know the proven cloud models that ensure security, performance, and functionality of your mission-critical business intelligence and advanced analytics solutions. Our team has experience with moving business applications to the cloud and we can help you maximize the performance and value of analytics in the cloud.

Modernizing your SAS-based analytic reports and data storing or processing system is only the first step of a long journey. Think bigger and consider taking full advantage of the latest and greatest that modern technology has to offer. Particularly, we can help move your entire data infrastructure to a secure, automatically scalable and highly available cloud platform. And then we use native cloud services to manage your data and build reporting or analytic systems from scratch to meet your critical business demands. Be sure to check our specific cloud migration offerings for the following destinations: Microsoft Azure, Amazon Web Services, and Google Cloud Platform.

Take advantage of the DB Best cloud experience

As a partner of Microsoft, AWS, and Google Cloud, we have specific offerings to modernize your SAS solutions.

Microsoft Azure

Deploy SAS Viya into its own virtual network using a generic license. The deployment includes three products all running on Linux: SAS Visual Analytics, SAS Visual Statistics, and SAS Visual Data Mining and Machine Learning. We deploy SAS on Azure using the modern microservices architecture, so you can easily upgrade your applications in future. Additionally, we can help design your cloud data storage on one of the following platforms: Azure SQL Database Managed Instances, Azure SQL Database Hyperscale, or Azure Cosmos DB.

Amazon Web Services

Create a new virtual private cloud (VPC) with default parameters in a symmetric multiprocessing (SMP) environment to deploy SAS Viya. The deployment includes 4 products running on Linux: SAS Visual Analytics, SAS Visual Statistics, and SAS Visual Data Mining and Machine Learning, and SAS Data Preparation. The deployment best practices include “We deploy SAS” on Azure using Elastic Load Balancer, managed network address translation (NAT) gateways, as well as security groups for the virtual machines and the Ansible controller. Here’s what the general architecture diagram looks like:

SAS Viya on AWS

Finally, we move your data to Amazon Aurora or Amazon RDS and lift and shift your workloads to Amazon EC2.

Google Cloud

With no license agreement between Google and SAS, we can deploy SAS Viya in the GCP cloud as a stand-alone on-premises installation. Once again, the deployment best practices include creating a new virtual private cloud (VPC), enabling Transport Layer Security (TLS), and using Google Cloud native load balancer together with Ansible Controller or Bastion.

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