Migration to a SAS^® Grid Computing environment provides many advantages. However, such migration might not be free from challenges especially considering users' pre-migration routines and programming practices. While SAS^® provides good graphical user interface solutions (for example, SAS^® Enterprise Guide^®) to develop and submit the SAS code to SAS Grid Computing, some situations might need command-line batch submission of a group of related SAS programs in a particular sequence. Saving individual log and output files for each program might also be a favorite routine in many organizations. SAS has provided the SAS Grid Manager Client Utility and SASGSUB commands to enable command-line submission of SAS programs to the grid. However, submitting a sequence of SAS programs in a conventional batch program style and getting individual logs and outputs for them needs a customized approach. This paper presents such an approach. In addition, an HTML and JavaScript tool developed in-house is introduced. This tool automates the generation of a SAS program that almost emulates a conventional scenario of submitting a batch program in a command-line shell using SASGSUB commands.

Would you agree that the value of SAS^® for your organization comes from transforming data into actionable information, using well-prepared human resources? This paper presents seven areas where this potential SAS value can be lost by inefficient data access, limited reporting and visualization, poor data cleansing, obsolete predictive analytics, incomplete SAS solutions, limited hardware use, and lack of governance. This paper also suggests what to do to overcome these issues.

Many organizations are using SAS^® Visual Analytics for their daily reporting. But as more users gain access to the visual tool, it is easy to lose track of what data is being used, what reports are being accessed, and what elements of the system are classified as critical. With SAS Visual Analytics comes a governance exercise that all organizations should provision for, as otherwise it jeopardizes its maintenance and performance. This paper explores the three different auditing areas that can be configured with SAS Visual Analytics and the different metrics that are associated with them. It presents how to configure the auditing, the data sources that are being populated on the background, and how to exploit them to expand your reports beyond the pre-created audit reports. Consideration is also given to the IT and infrastructure side of enabling auditing mechanisms, with data volumes and archiving practices being at the heart of the discussion.

Connecting database schemas to libraries in the SAS^® metadata is a very important part of setting up a functional and useful environment for business users. This task can be quite difficult for the untrained administrator. This paper addresses the key configuration items that often go unnoticed but that can make a big difference. Using the wrong options can lead to poor database performance or even to a total lockdown, depending on the number of connections to the database.

When new technologies, workflows, or processes are implemented, an organization and its employees must embrace changes in order to ensure long-term success. This paper provides guidelines and best practices in change management that the SAS Advanced Analytics Division uses with customers when it implements prescriptive analytics solutions (provided by SAS/OR^® software). Highlights include engaging technical leaders in defining project scope and providing functional design documents. The paper also highlights SAS' approach in engaging business leaders on business scope, garnering executive-level project involvement, establishing steering committees, defining use cases, developing an effective communication strategy, training, and implementing of SAS/OR solutions.

SAS^® is often deployed in a client/server architecture in which SAS^® Foundation is installed on a server and is accessed from each user's workstation. Many system administrators prefer that users not log on directly to the server to run SAS, nor do they want to set up a complex Citrix environment. SAS client applications are an attractive alternative for this type of architecture. But with the advent of multiple SAS^® Studio editions and ongoing enhancements to SAS^® Enterprise Guide^®, choosing the most suitable client application presents a challenge for many system administrators. To help guide you in this choice, this paper compares the administration of three SAS Foundation client applications that can be used in a client/server architecture: SAS Enterprise Guide, SAS^® Studio Basic, and SAS^® Studio Mid-Tier. The usage differences between SAS Studio and SAS Enterprise Guide have been addressed elsewhere. In this paper, we focus on differences that pertain specifically to system administration, including deployment, maintenance, and authentication. The information presented here will help system administrators determine which application best fits the needs of their users and their environment.

Today it is vital for an organization to manage, distribute, and secure content for its employees. In most cases, different groups of employees are interested in different content, and some content should not be available to everyone. It is the SAS^® administrator's job to design a metadata group structure that makes managing content easier. SAS enables you to create any metadata group organizational structure imaginable, and it is common to define a metadata group structure that mimics the organization's hierarchy. Circular group memberships are frequently the cause of unexpected issues with SAS web applications. A circular group relationship can be as simple as two groups being members of one another. You might not be aware that you have defined this type of recursive association between groups. The paper identifies some problems that are caused by recursive group memberships and provides tools to investigate your metadata group structure that help identify recursive metadata group relationships. We explain the process of extracting group associations from the SAS^® Metadata Server, and we show how to organize this data to investigate group relationships. We use a stored process to generate a report and SAS^® Visual Analytics to generate a network diagram that provides a graphical representation of an organization's group relationship structure, to easily identify circular group structures.

Learn how SAS^® works with analytics, big data, and the cloud all in one product: SAS^® Analytics for Containers. This session describes the architecture of containers running in the public, private, or hybrid cloud. The reference architecture also shows how SAS leverages the distributed compute of Hadoop. Topics include how SAS products such as Base SAS^®, SAS/STAT^® software, and SAS/GRAPH^® software can all run in a container in the cloud. This paper discusses how to work with a SAS container running in a variety of Infrastructure as a Service (IaaS) models, including Amazon Web Services and OpenStack cloud. Additional topics include provisioning web-browser-based clients via Jupyter Notebooks and SAS^® Studio to provide data scientists with the tool of their choice. A customer use case is discussed that describes how SAS Analytics for Containers enables an IT department to meet the ad hoc, compute-intensive, and scaling demands of the organization. An exciting differentiator for the data scientist is the ability to send some or all of the analytic workload to run inside their Hadoop cluster by using the SAS accelerators for Hadoop. Doing so enables data scientists to dive inside the data lake and harness the power of all the data.

This paper presents considerations for deploying SAS^® Foundation across software-defined storage (SDS) infrastructures, and within virtualized storage environments. There are many new offerings on the market that offer easy, point-and-click creation of storage entities, with simplified management. Internal storage area network (SAN) virtualization also removes much of the hands-on management for defining storage device pools. Automated tier software further attempts to optimize data placement across performance tiers without manual intervention. Virtual storage provisioning and automated tier placement have many time-saving and management benefits. In some cases, they have also caused serious unintended performance issues with heavy large-block workloads, such as those found in SAS Foundation. You must follow best practices to get the benefit of these new technologies while still maintaining performance. For SDS infrastructures, this paper offers specific considerations for the performance of applications in SAS Foundation, workload management and segregation, replication, high availability, and disaster recovery. Architecture and performance ramifications and advice are offered for virtualized and tiered storage systems. General virtual storage pros and cons are also discussed in detail.

This paper describes specific actions to be taken to increase the usability, data consistency, and performance of an advanced SAS^® Customer Intelligence solution for marketing and analytic purposes. In addition, the paper focuses on the establishment of a data governance program to support the processes that take place within this environment. This paper presents our experiences developing a data governance light program for the enterprise data warehouse and its sources as well as for the data marts created downstream to address analytic and campaign management purposes. The challenge was to design a data governance program for this system in 90 days.

Are you prepared if a disaster happens? If your company relies on SAS^® applications to stay in business, you should have a Disaster Recovery Plan (DRP) in place. By a DRP, we mean documentation of the process to recover and protect your SAS infrastructure (SAS binaries, the operating system that is tuned to run your SAS applications, and all the pertinent data that the SAS applications require) in the event of a disaster. This paper discusses what needs to be in this plan to ensure that your SAS infrastructure not only works after it is recovered, but is able to be maintained on the recovery hardware infrastructure.

Sometimes it might be beneficial to share a BI environment with multiple tenants within an enterprise, but at the same time this might also introduce additional complexity with regard to the administration of data access. In this breakout session, one possible setup is shown by sharing a high-level overview of such an environment within the ING bank in the Netherlands for the Risk Services organization.

SAS^® Environment Manager is the predominant tool for managing your SAS^® environment. Its popularity is increasing quickly as evidenced by the increased technical support requests from our customers. This paper identifies the most frequently asked questions from customers by reviewing the support work completed by the development and technical support teams over the last few years. The questions range across topics such as web interface usage; alerts, controls, and resource discovery; Agent issues; and security issues. Questions discussed in the paper include: What resources need to be configured after we install SAS Environment Manager? What Control Actions are available, what is their purpose, and when do I use them? Why does SAS Environment Manager show all resources as (!) (Down)? What is the best way to enable an alert for a resource? How do I configure HTTPs? Can we configure the Agents with certificates other than the default? What is the combination of roles needed to see the Resources Tab? This paper presents detailed answers to the questions and also points out where you can find more information. We believe that by understanding these answers, SAS^® administrators will be more knowledgeable about SAS Environment Manager, and can better implement and manage their SAS environment.

In the quest for valuable analytics, access to business data through message queues provides near real-time access to the entire data life cycle. This in turn enables our analytical models to perform accurately. What does the item a user temporarily put in the shopping basket indicate, and what can be done to motivate the user? How do you recover the user who has now unsubscribed, given that the user had previously unsubscribed and re-subscribed quickly? User behavior can be captured completely and efficiently using a message queue, which causes minimal load on production systems and allows for distributed environments. There are some technical issues encountered when attempting to populate a data warehouse using events from a message queue. The presentation outlines a solution to the following issues: the message queue connection, how to ensure that messages aren't lost in transit, and how to efficiently process messages with SAS^®; message definition and metadata, and how to react to changes in message structure; data architecture and which data architecture is appropriate for storing message data and other business data; late arrival of messages and how late arriving data can be loaded into slowly changing dimensions; and analytical processing and how transactional message data can be reformatted for analytical modeling. Ultimately, populating a data warehouse with message queue data can require less development than accessing source databases; however a robust architecture

SAS^® has been installed at your organization now what? How do you approach configuring groups, roles, folders, and permissions in your environment? This presentation is built on best practices used within the U.S. SAS^® Professional Services and Delivery division and aims to equip new and seasoned SAS administrators with the knowledge and tools necessary to design and implement a SAS metadata and file system security model. We start by covering the basic building blocks of the SAS^® Intelligence Platform metadata and security framework. We discuss the SAS metadata architecture, and highlight the differences between groups and roles, permissions and capabilities, access control entries and access control templates, and what content can be stored within metadata folders versus in file system folders. We review the various authorization layers in a SAS deployment that must work together to create a secure environment, including the metadata layer, the file system, and the data layer. Then, we present a 10-step best practice approach for how to design your SAS metadata security model. We provide an introduction to basic metadata security design and file system security design templates that have been used extensively by SAS Professional Services and Delivery in helping customers secure their SAS environments.

SAS^® 9.4 provides three ways to upgrade: upgrade in place, automated migration with the SAS^® Migration Utility, and partial promotion. This session focuses primarily on the different techniques and best practices for each. We also discuss the pros and cons of using the SAS Migration Utility and what is required for migrating users' content like projects, data, and code.

Because many SAS^® users either work for or own companies that house big data, the threat that malicious software poses becomes even more extreme. Malicious software, often abbreviated as malware, includes many different classifications, ways of infection, and methods of attack. This E-Poster highlights the types of malware, detection strategies, and removal methods. It provides guidelines to secure essential assets and prevent future malware breaches.

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Capacity management is concerned with managing, controlling, and optimizing the hardware resources on a technology platform. Its primary goal is to ensure that IT resources are right-sized to meet current and future business requirements in a cost-effective manner. In other words, keeping those hardware vendors at bay! A SAS^® LASR Analytic Server, with its dependence on in-memory resources, necessitate a revisit to the traditional IT server capacity management practices. A major UK-based financial services institution operates a multi-tenanted Enterprise SAS^® platform. The tenants share platform resources and as such, require quotas enforced with system limits and costs for their resource utilization, aligned to the business outcomes and agreed-upon service level agreements (SLAs). This paper discusses the implementation of system, operational, and development polices applicable in a multi-tenanted SAS platform, in order to optimize an investment in the analytic platform provided by SAS LASR Analytic Server and to be in control as to when capacity uplifts are required.

How do you enable strong authentication across different parts of your organization in a safe and secure way? We know that Kerberos provides us with a safe and secure strong authentication mechanism, but how does it work across different domains or realms? In this paper, we examine how Kerberos cross-realm authentication works and the different parts that you need ready in order to use Kerberos effectively. Understanding the principals and applying the ideas we present will make you successful at improving the security of your authentication system.

Developing software using agile methodologies has become the common practice in many organizations. We use the SCRUM methodology to prepare, plan, and implement changes in our analytics environment. Preparing for the deployment of a new release usually took two days of creating packages, promoting them, deploying jobs, creating migration scripts, and correcting errors made in the first attempt. A sprint that originally took 10 working days (two weeks) was effectively reduced to barely seven. By automating this process, we were able to reduce the time needed to prepare our deployment to less than half a day, increasing the time we can spend developing by 25%. In this paper, we present the process and system prerequisites for automating the deployment process. We also describe the process, code, and scripts required for automating metadata promotion and physical table comparison and update.

You re in the business of performing complex analyses on large amounts of data. This data changes quickly and often, so you ve invested in a powerful high-performance analytics engine with the speed to respond to a real-time data stream. However, you realize an immediate problem upon the implementation of your software solution: your analytics engine wants to process many records of data at once, but your streaming engine wants to send individual records. How do you store this streaming data? How do you tell the analytics engine about the updates? This paper explains how to manage real-time streaming data in a batch-processing analytics engine. The problem of managing streaming data in analytics engines comes up in many industries: energy, finance, health care, and marketing to name a few. The solution described in this paper can be applied in any industry, using features common to most analytics engines. You learn how to store and manage streaming data in such a way as to guarantee that the analytics engine has only current information, limit interruptions to data access, avoid duplication of data, and maintain a historical record of events.

How many environments does your organization have-three (Dev/Test/Prod), five (Dev/SIT/UAT/Pre-Prod/Prod), or maybe only one? Once you've built your SAS^® process-an ETL job, a model, an exploration, or a report-how should you promote it across these environments? If you have only one environment, is a development life cycle still possible? (Yes, it is.) Historically, the traditional systems development life cycle (SDLC) spans multiple environments (for example, Dev/Test/Prod). This approach has benefits-primarily to ensure that change in one environment does not adversely impact others, but costs and release time-frames mean this is not always practicable. Some sites now adopt a two-platform approach: Non-Production and Production. Non-Prod exists for technology change, such as new software, hot fixes, database connections, and so on. At these sites, the business runs wholly within the Production environment, yet still requires a business-specific life-cycle management within the Production environment. And, of course, all promotion must include thorough testing. Other questions to consider are: 1) Can this promotion process be automated? 2) Can this process extend beyond business content to include configuration settings? This presentation investigates the SAS tools available to promote content between environments or between functional areas of a single environment, and how to automate and test the promotion process. Just imagine: a weekly automated and tested promotion process? Let's see

SAS^® migrations are the number one reason why SAS architects and administrators are fired. Even though this bold statement is not universally true, it has been at the epicenter of many management and technical discussions at UnitedHealth Group. The competing business forces between the desire to innovate and to provide platform stability drive difficult discussions between business leaders and IT partners that tend to result in a frustrated user-base, flustered IT professionals, and a stale SAS environment. Migrations are the antagonist of any IT professional because of the disruption, long hours, and stress that typically ensues. This paper addresses the lessons learned from a SAS migration from the first maintenance release of SAS^® 9.4 to the third maintenance release of SAS^® 9.4 on a technically sophisticated enterprise SAS platform including clustered metadata servers, clustered middle-tier, SSL, an IBM Platform Load Sharing Facility (LSF) grid, and SAS^® Visual Analytics.

Today, companies are increasingly using analytics to discover new revenue 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 is also being used in risk management, fraud detection, life sciences, sports, and many more emerging markets. However, to maximize the value to the business, analytics solutions need to be deployed quickly and cost-effectively, while also providing the ability to readily scale without degrading performance. Of course, in today's demanding environments, where budgets are still shrinking and mandates to reduce carbon footprints are growing, the solution must deliver excellent hardware utilization, power efficiency, and return on investment. To help solve 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 covers Red Hat Enterprise Linux 6 and 7. Areas researched include the I/O subsystem, file system selection, and kernel tuning, both in bare metal and virtualized (KVM) environments. Additionally, we now include grid-based configurations running with Red Hat Resilient Storage Add-On (Global File System 2 [GFS2] clusters).

Moving a workforce in a new direction takes a lot of energy. Your planning should include four pillars: culture, technology, process, and people. These pillars assist small and large SAS^® rollouts with a successful implementation and an eye toward future proofing. Boston Scientific is a large multi-national corporation that recently grew SAS from a couple of desktops to a global implementation. Boston Scientific's real world experiences reflect on each pillar, both in what worked and in lessons learned.

Installation and configuration of a SAS^® Enterprise BI platform in the requirements of the today's world requires knowledge on a wide variety of subjects. Security requirements are growing, the number of involved components is growing, time to delivery should be shorter, and the quality must be increased. The expectations of the customers are based on a cloud experience where automated deployments with ready-to-use applications are state of the art. This paper describes an approach to address the challenges to deploy SAS^® 9.4 on Linux to meet today's customer expectations.

For customers providing SAS^® reporting to the public, the ability to use a Social login opens up a number of possibilities to provide richer services. Instead of everybody using generic Guest access and being limited to a common subset of reports or other functionality, previously unknown users can seamlessly log in and access SAS web content while SAS administrators can continue to apply best-practice security. This paper focuses on integrating Google Sign-In, Microsoft Account Sign-In, and Facebook Sign-In as alternative methods to log in from the SAS Logon Manager, as well as registering any new users SAS metadata automatically.

Getting speedy results from your SAS^® programs when you re working with bulky data sets is more than elegant coding techniques. There are several approaches to improving performance when working with biggish data. Although you can upgrade your hardware, this just helps you to run inefficient code and bloated tables quicker. So, you should also consider the results that tuning your database and adjusting your SAS platform can bring. In this paper, we review the various options available to give you some ideas about things you can do better.

SAS^® Management Console has been a key tool to interact with SAS^® Metadata Server. But sometimes users need much more than what SAS Management Console can do. This paper contains a couple of SAS^® macros that can be used in SAS^® Enterprise Guide^® and PC SAS to read SAS metadata. These macros read users, roles, and groups registered in metadata. This paper explains how these macros can be executed in SAS Enterprise Guide and how to change these macros to meet other business requirements. There might be some tools available in the market that can be used to read SAS metadata, but this paper helps in achieving most of them within a SAS client like PC SAS and SAS Enterprise Guide, without requiring any additional plug-ins.

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SAS^® job flows created by Windows services have a problem. Currently, they can execute only jobs in a series (one at a time). This can slow down job processing, and it limits the utility of the flows. This paper shows how you can alter the flow of Windows services after they have been generated to enable jobs to run in parallel (side by side). A high-level overview of PROC GROOVY, which automates these changes, is provided, as well as a summary of the positives and negatives of running jobs in parallel.

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The purpose of this paper is to provide an overview of SAS^® metadata security for new or inexperienced SAS administrators. The focus of the discussion is on identifying the most common metadata security objects such as access control entries (ACEs), access control templates (ACTs), metadata folders, authentication domains, and so on, and on describing how these objects work together to secure the SAS environment. Based on a standard SAS^® Enterprise Office Analytics for Midsize Business installation in a Windows environment, this paper walks through a simple example of securing a metadata environment, which demonstrates how security is prioritized, the impact of each security layer, and how conflicts are resolved.

You have got your SAS^® environments installed, configured, and running smoothly. Time to relax and put your feet up, right? Not so fast! There is still one more leg to go on your security journey. After the deployment of your initial security plan, the security audit process provides active and regular monitoring and ensures that your environment remains secure. There are many reasons to carry out security audits: to ensure regulatory compliance, to maintain business confidence, and to keep your SAS platform as per the design specifications. This paper looks at some of the available ways to regularly review your environment to ensure that protected resources are not at risk, to comply with security auditing requirements, and to quickly and easily answer the question 'Who has access to what?' through efficient SAS metadata security management using Metacoda software.

Not only does the new SAS^® Viya platform bring exciting advancements in high-performance analytics, it also takes a revolutionary step forward in the area of administration. The new SAS^® Cloud Analytic Services is accompanied by new platform management tools and techniques that are designed to ease the administrative burden while leveraging the open programming and visual interfaces that are standard among SAS Viya applications. Learn about the completely rewritten SAS^® Environment Manager 3.2, which supports the SAS Viya platform. It includes a cleaner HTML5-based user interface, more flexible and intuitive authorization windows, and user and group management that is integrated with your corporate Lightweight Directory Access Protocol (LDAP). Understand how authentication works in SAS Viya without metadata identities. Discover the key differences between SAS^®9 and SAS Viya deployments, including installation and automated update-in-place strategies orchestrated by Ansible for hot fixes, maintenance, and new product versions alike. See how the new microservices and stateful servers are managed and monitored. In general, gain a better understanding of the components of the SAS Viya architecture, and how they can be collectively managed to keep your environment available, secure, and performant for the users and processes you support.

As a SAS^® administrator, have you ever wanted to look at the data in SAS^® Environment Manager spanning a longer length of time? Has your manager asked for access to the data so that they can use it to spot trends and make predictions? This paper shows you how to share that wealth of information found in the SAS Environment Manager log data. It explains how to save and store the data for use in SAS^® Visual Analytics. You will find tips on structuring the data for easy analysis and examples of using the data to make business decisions.

If you are planning to deploy SAS^® Grid Manager or SAS^® Enterprise BI (or other distributed SAS^® Foundation applications) with load-balanced servers on multiple operating systems instances, a shared file system is required. In order to determine the best shared file system choice for a given deployment, it is important to understand how the file system is used, the SAS^® I/O workload characteristics performed on it, and the stressors that SAS Foundation applications produce on the file system. For the purposes of this paper, we use the term shared file system to mean both a clustered file system and shared file system, even though shared can denote a network file system and a distributed file system not clustered. This paper examines the shared file systems that are most commonly used with SAS and reviews their strengths and weaknesses.

SAS^® Cloud Analytic Services (CAS) is the cloud-based run-time environment for data management and analytics in SAS^®. By run-time environment, we refer to the combination of hardware and software where data management and analytics take place. In a sense, CAS is just another SAS platform to do things. CAS is a platform for high-performance analytics and distributed computing. The CAS server provides data management and an analytics framework that can run in the cloud, that can act as a cloud, and that provides the best-in-class analytics that SAS is known for. This new architecture functions as a public API, allowing access from many different clients such as Lua, Python, Java, REST, and yes, even SAS. The CAS server is designed to provide user-level sessions, to share data between sessions, and to provide fault tolerance, which allows a worker node to crash without losing data and allows the user action to continue running to completion. The isolation provided to each session allows one session to crash without affecting other sessions. The concept of 'always in memory' in CAS means that an action is not aware of what the server does to allow the action to access the data. The entire file might be in memory or just pieces of the file might be mapped into memory, just in time for the action to access the data. This allows CAS tables to be loaded that are larger than the memory available across the grid. Hadoop can be used to provide data redundancy. The server is elastic and can add or remove nodes as needed. Users can specify how many nodes they want their session to use, so that the session fits their needs.

SAS^® environments are evolving in multiple directions. Modern web interfaces such as SAS^® Studio are replacing the traditional SAS^® Display Manager system. At the same time, distributed analytic computing, centrally managed by SAS^® Grid Manager, is becoming the standard topology for many enterprises. SAS administrators are faced with the task of providing business users properly configured, tuned, and monitored applications. The tips included in this paper provide SAS administrators with best practices to centrally manage SAS Studio options and repositories, proper grid tuning, effective monitoring of user sessions, high-availability considerations and more.

Knowing which SAS^® products are being used in your organization, by whom, and how often helps you decide whether you have the right mix and quantity licensed. These questions are not easy to answer. We present an innovative technique using three SAS utilities to answer these questions. This paper includes example code written for Linux that can easily be modified for Windows and other operating systems.

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Transport Layer Security (TLS) configuration for SAS^® components is essential to protect data in motion. All necessary encryption arrangement is established through a TLS handshake between the client and the server side. Many SAS^® 9.4 and SAS^® Viya components can be a client side, a server side, or both. SAS documentation primarily provides how-to steps for the configuration. This paper examines the X.509 certificate and the TLS handshake protocol, which are the basic building blocks of the secure communication. The paper focuses on the logic behind the setup and how various types of certificates are used in the configuration. Many unique client and server combinations of SAS components are illustrated and explained with the best-practice suggestions.

We are always looking for ways to improve the performance, efficiency, and availability of our investment in SAS^® solutions. To address those needs, SAS offers the ability to cluster many of its constituent software components. A cluster is a set of systems that work together with the goal of providing a single service. This session identifies 12 different technologies to create clusters of SAS software components and describes how they are designed to boost the capabilities of SAS to function in the enterprise.

Within a SOX-compliant environment, a batch job is run. During the process, an FTP server needs to be accessed. The batch user password is not known and the FTP credentials are not known either. How safely and securely can we achieve this? The approach is to have an authentication domain within the SAS metadata created that has the FTP credentials. Create an internal SAS user within the SAS metadata. This user exists only within the SAS metadata, so it does not pose any risk. Create an FTP server within the SAS metadata. Add and link everything together within the SAS metadata. Within the SAS batch job, the SAS internal user will be used (with the use of the hashed password) to connect to the metadata to get the FTP credentials stored within the authentication domain and retrieve or upload the data.

Your SAS^® Visual Analytics users begin to create and share reports. As an administrator, you want to track performance of the reports over time, analyzing timing metrics for key tasks such as data query and rendering, relative to total user workload for the system. Logging levels can be set for the SAS Visual Analytics reporting services that provide timing metrics for each report execution. The log files can then be mined to create a data source for a time series plot in SAS Visual Analytics. You see report performance over time with peak workloads and how this impacts the user experience. Isolation on key metrics can identify performance bottlenecks for improvement. First we look at how logging levels are modified for the reporting services and focus on tracking a single user viewing a report. Next, we extract data from a long running log file to create a report performance data source. Using SAS Visual Analytics, we analyze the data with a time series plot, looking at times of peak work load and how the user experience changes.

Using shared accounts to access third-party database servers is a common architecture in SAS^® environments. SAS software can support seamless user access to shared accounts in databases such as Oracle and MySQL, via group definitions and outbound authentication domains in metadata. However, the configurations necessary to leverage shared accounts in Kerberized Hadoop clusters are more complicated. Kerberos tickets must often be generated and maintained in order to simply access the Hadoop environment, and those tickets must allow access as the shared account instead of as an individual user's account. In all cases, key prerequisites and configurations must be put into place in order for seamless Hadoop access to function with the shared account. Methods for implementing these arrangements in SAS environments can be non-intuitive. This paper starts by outlining general architectures of shared accounts in third-party database environments. It then presents several methods of managing remote access to shared accounts in Kerberized Hadoop environments using SAS, including specific implementation details, code samples, and security implications.

Over the years, the use of SAS^® has grown immensely within Royal Bank of Scotland (RBS), making platform support and maintenance overly complicated and time consuming. At RBS, we realized that we have been living 'war and peace' every day for many years and that the time has come to re-think how we support SAS platforms. With our approach to rationalize and consolidate the ways our organization uses SAS came the need to review and improve the processes and procedures we have in place. This paper explains why we did it, what we've changed or reinvented, and how all these have changed our way of operation by bringing us closer to DevOps and helping us to improve our relationship with our customers as well as building trust in the service we deliver.

Whether you are a new SAS^® administrator or you are switching to a Linux environment, you have a complex mission. This job becomes even more formidable when you are working with a system like SAS^® Visual Analytics that requires multiple users loading data daily. Eventually a user has data issues or creates a disruption that causes the system to malfunction. When that happens, what do you do next? In this paper, we go through the basics of a SAS Visual Analytics Linux environment and how to troubleshoot the system when issues arise.