The presentation illustrates techniques and technology to manage complex and large-scale ETL workloads using SAS^® and IBM Platform LSF to provide greater control of flow triggering and more complex triggering logic based on a rules-engine approach that parses pending workloads and current activity. Key techniques, configuration steps, and design and development patterns are demonstrated. Pros and cons of the techniques that are used are discussed. Benefits include increased throughput, reduced support effort, and the ability to support more sophisticated inter-flow dependencies and conflicts.

With the popularity of network-attached storage for shared systems, IT shops are increasingly turning to them for SAS^® Grid implementations. Given the high I/O demand of SAS large block processing, how do you ensure good performance between network-attached SAS Grid nodes and storage? This paper discusses different types of network-attached implementations, what works, and what does not. It provides advice for bandwidth planning, types of network technology to use, and what has been practically successful in the field.

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This paper demonstrates the deployment of SAS^® Visual Analytics 7.2 with a distributed SAS^® LASR™ Analytic Server and an internet-facing web tier. The key factor in this deployment is to establish the secure web server connection using a third-party certificate to perform the client and server authentication. The deployment process involves the following steps: 1) Establish the analytics cluster, which consists of SAS^® High-Performance Deployment of Hadoop and the deployment of high-performance analytics environment master and data nodes. 2) Get the third-party signed certificate and the key files. 3) Deploy the SAS Visual Analytics server tier and middle tier. 4) Deploy the standalone web tier with HTTP protocol configured using secure sockets. 5) Deploy the SAS^® Web Infrastructure Platform. 6) Perform post-installation validation and configuration to handle the certificate between the servers.

The power of SAS^®9 applications allows information and knowledge creation from very large amounts of data. Analysis that used to consist of 10s to 100s of gigabytes (GBs) of supporting data has rapidly grown into the 10s to 100s of terabytes (TBs). This data expansion has resulted in more and larger SAS^® data stores. Setting up file systems to support these large volumes of data with adequate performance, as well as ensuring adequate storage space for the SAS temporary files, can be very challenging. Technology advancements in storage and system virtualization, flash storage, and hybrid storage management require continual updating of best practices to configure IO subsystems. This paper presents updated best practices for configuring the IO subsystem for your SAS^®9 applications, ensuring adequate capacity, bandwidth, and performance for your SAS^®9 workloads. We have found that very few storage systems work ideally with SAS with their out-of-the-box settings, so it is important to convey these general guidelines.

Whether you are deploying a new capability with SAS^® or modernizing the tool set that people already use in your organization, change management is a valuable practice. Sharing the news of a change with employees can be a daunting task and is often put off until the last possible second. Organizations frequently underestimate the impact of the change, and the results of that miscalculation can be disastrous. Too often, employees find out about a change just before mandatory training and are expected to embrace it. But change management is far more than training. It is early and frequent communication; an inclusive discussion; encouraging and enabling the development of an individual; and facilitating learning before, during, and long after the change. This paper not only showcases the importance of change management but also identifies key objectives for a purposeful strategy. We outline our experiences with both successful and not so successful organizational changes. We present best practices for implementing change management strategies and highlighting common gaps. For example, developing and engaging Change Champions from the beginning alleviates many headaches and avoids disruptions. Finally, we discuss how the overall company culture can either support or hinder the positive experience change management should be and how to engender support for formal change management in your organization.

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If your organization already deploys one or more software solutions via Amazon Web Services (AWS), you know the value of the public cloud. AWS provides a scalable public cloud with a global footprint, allowing users access to enterprise software solutions anywhere at any time. Although SAS^® began long before AWS was even imagined, many loyal organizations driven by SAS are moving their local SAS analytics into the public AWS cloud, alongside other software hosted by AWS. SAS^® Solutions OnDemand has assisted organizations in this transition. In this paper, we describe how we extended our enterprise hosting business to AWS. We describe the open source automation framework from which SAS Soultions onDemand built our automation stack, which simplified the process of migrating a SAS implementation. We'll provide the technical details of our automation and network footprint, a discussion of the technologies we chose along the way, and a list of lessons learned.

A group tasked with testing SAS^® software from the customer perspective has gathered a number of helpful hints for SAS^® 9.4 that will smooth the transition to its new features and products. These hints will help with the 'huh?' moments that crop up when you are getting oriented and will provide short, straightforward answers. We also share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure that you are ready to begin your transition to SAS 9.4 and guidance for after you are at SAS 9.4. The target audience for this paper is primarily system administrators who will be installing, configuring, or administering the SAS 9.4 environment. This paper was first published in 2012; it has been revised each year since with new information.

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Report automation and scheduling are very hot topics in many corporate industries. Automating reports has many advantages, including reducing workload, eliminating repetitive tasks, generating accurate results, and offering better performance. In recent years SAS^® launched more powerful tools to present and share business analytics data. This paper illustrates the stepwise process of how to deploy and schedule reports on a server using SAS^® Management Console 9.4. Many of us know that the scheduling jobs can be done using Windows (as well as scheduling jobs at the server level) and it is important to note that the server-side scheduling has more advantages than scheduling jobs on Windows. The Windows scheduler invokes SAS programs on a local PC and these are more often subject to system crashes. The main advantage of scheduling on the server is that most jobs that are scheduled run using nighttime facilities when there is faster record retrieval and less load burden on database servers. Other advantages of scheduling on the server side are that all scheduled jobs are at one location and it is also easy to maintain and keep track of log files if any scheduled jobs fail to run. This paper includes an overview of the schedule manager in SAS Management Console 9.4, a description of system tools and their options, instructions for converting SAS^® Enterprise Guide^® point-and-click programs into a consolidated SAS program for deployment, and several other related topics.

You've heard all the talk about SAS^® Visual Analytics--but maybe you are still confused about how the product would work in your SAS^® environment. Many customers have the same points of confusion about what they need to do with their data, how to get data into the product, how SAS Visual Analytics would benefit them, and even should they be considering Hadoop or the cloud. In this paper, we cover the questions we are asked most often about implementation, administration, and usage of SAS Visual Analytics.

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Today, companies are increasingly using analytics to discover new revenue-increasing and cost-saving opportunities. Many business professionals turn to SAS, a leader in business analytics software and service, to help them improve performance and make better decisions faster. Analytics are also being used in risk management, fraud detection, life sciences, sports, and many more emerging markets. To maximize their value to a business, analytics solutions need to be deployed quickly and cost-effectively, while also providing the ability to scale readily without degrading performance. Of course, in today's demanding environments, where budgets are shrinking and the number of mandates to reduce carbon footprints are growing, the solution must deliver excellent hardware utilization, power efficiency, and return on investment. To address some of these challenges, Red Hat and SAS have collaborated to recommend the best practices for configuring SAS^® 9 running on Red Hat Enterprise Linux. The scope of this document includes Red Hat Enterprise Linux 6 and 7. Researched areas include the I/O subsystem, file system selection, and kernel tuning, in both bare-metal and kernel-based virtual machine (KVM) environments. In addition, we include grid configurations that run with the Red Hat Resilient Storage Add-On, which includes Global File System 2 (GFS2) clusters.

SAS^® 9.4 allows for extensive customization of configuration settings. These settings are changed as new products are added into a deployment and upgrades to existing products are deployed into the SAS^® infrastructure. The ability to track which configuration settings change during the addition of a specific product or the installation of a particular platform maintenance release can be very useful. Often, customers run a SAS deployment step and wonder what exactly changed and in which files. The use of version control systems is becoming increasingly popular for tracking configuration settings. This paper demonstrates how to use Git, a hugely popular, open-source version control system to manage, track, audit, and revert changes to your SAS configuration infrastructure. Using Git, you can quickly list which files were changed by the addition of a product or maintenance package and inspect the differences. You can then revert to the previous settings if that becomes desirable.

At the University of Central Florida (UCF), we recently invested in SAS^® Visual Analytics, along with the updated SAS^® Business Intelligence platform (from 9.2 to 9.4), a project that took over a year to be completed. This project was undertaken to give our users the best and most updated tools available. This paper introduces the SAS Visual Analytics environment at UCF and includes projects created using this product. It answers why we selected SAS Visual Analytics for development over other SAS^® applications. It explains the technical environment for our non-distributed SAS Visual Analytics: RAM, servers, benchmarking, sizing, and scaling. It discusses why we chose the non-distributed mode versus distributed mode. Challenges in the design, implementation, usage, and performance are also presented, including the reasons why Hadoop was not adopted.

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For many organizations, the answer to whether to manage their data and analytics in a public or private cloud is going to be both. Both can be the answer for many different reasons: common sense logic not to replace a system that already works just to incorporate something new; legal or corporate regulations that require some data, but not all data, to remain in place; and even a desire to provide local employees with a traditional data center experience while providing remote or international employees with cloud-based analytics easily managed through software deployed via Amazon Web Services (AWS). In this paper, we discuss some of the unique technical challenges of managing a hybrid environment, including how to monitor system performance simultaneously for two different systems that might not share the same infrastructure or even provide comparable system monitoring tools; how to manage authorization when access and permissions might be driven by two different security technologies that make implementation of a singular protocol problematic; and how to ensure overall automation of two platforms that might be independently automated, but not originally designed to work together. In this paper, we share lessons learned from a decade of experience implementing hybrid cloud environments.