There are many components that make up the middle tier and server tier of a SAS^® 9.4 deployment. There is also a variety of technologies that can be used to provide high availability of these components. This paper focuses on a small set of best practices recommended by SAS for a consistent high-availability strategy across the entire SAS 9.4 platform. We focus on two technologies: clustering, as well as the high-availability features of SAS^® Grid Manager. For the clustering, we detail newly introduced clustering capabilities in SAS 9.4 such as the middle-tier SAS^® Web Application Server and the server-tier SAS^® metadata clusters. We also introduce the small, medium, and large deployment scenarios or profiles, which make use of each of these technologies. These deployment scenarios reflect the typical customer's environment and address their high availability, performance, and scalability requirements.

With the growth in size and complexity of organizations investing in SAS^® platform technologies, the size and complexity of ETL subsystems and data integration (DI) jobs is growing at a rapid rate. Developers are pushed to come up with new and innovative ways to improve process efficiency in their DI jobs to meet increasingly demanding service level agreements (SLAs). The ability to conditionally execute or switch paths in a DI job is an extremely useful technique for improving process efficiency. How can a SAS^® Data Integration developer design a job to best suit conditional execution? This paper discusses a technique for providing a parameterized dynamic execution custom transformation that can be easily incorporated into SAS^® Data Integration Studio jobs to provide process path switching capabilities. The aim of any data integration task is to ensure that all sources of business data are integrated as efficiently as possible. It is concerned with the repurposing of data via transformation, should be a value-adding process, and also should be the product of collaboration. Modularization of common or repeatable processes is a fundamental part of the collaboration process in DI design and development. Switch path a custom transformation built to conditionally execute branches or nodes in SAS Data Integration Studio provides a reusable module for solving the conditional execution limitations of standard SAS Data Integration Studio transformations and jobs. Switch Path logic in SAS Data Integration Studio can serve many purposes in day-to-day business needs for a SAS data integration developer as it is completely reusable

The Washington D.C. aqueduct was completed in 1863, carrying desperately needed clean water to its many residents. Just as the aqueduct was vital and important to its residents, a lifeline if you will, so too is the supply of data to the business. Without the flow of vital information, many businesses would not be able to make important decisions. The task of building my company s first dashboard was brought before us by our CIO; the business had not asked for it. In this poster, I discuss how we were able to bring fresh ideas and data to our business units by converting the data they saw on a daily basis in reports to dashboards. The road to success was long with plenty of struggles from creating our own business requirements to building data marts, synching SQL to SAS^®, using information maps and SAS^® Enterprise Guide^® projects to move data around, all while dealing with technology and other I.T. team roadblocks. Then on to designing what would become our real-time dashboards, fighting for SharePoint single sign-on, and, oh yeah, user adoption. My story of how dashboards revitalized the business is a refreshing tale for all levels.

Your company s chronically overloaded SAS^® environment, adversely impacted user community, and the resultant lackluster productivity have finally convinced your upper management that it is time to upgrade to a SAS^® grid to eliminate all the resource problems once and for all. But after the contract is signed and implementation begins, you as the SAS administrator suddenly realize that your company-wide standard mode of SAS operations, that is, using the traditional SAS^® Display Manager on a server machine, runs counter to the expectation of the SAS grid your users are now supposed to switch to SAS^® Enterprise Guide^® on a PC. This is utterly unacceptable to the user community because almost everything has to change in a big way. If you like to play a hero in your little world, this is your opportunity. There are a number of things you can do to make the transition to the SAS grid as smooth and painless as possible, and your users get to keep their favorite SAS Display Manager.

Pipeline parallelism, an extension of MP Connect, is an effective way to speed processing. Piping allows the typical programming sequence of DATA step followed by PROC to execute in parallel. Piping uses TCP ports to pass records directly from the DATA step to the PROC immediately as each individual record is processed. The DATA step in effect becomes a data transformation filter for the PROC , running in parallel and incurring no additional disk storage or related I/O lag. Establishing a pipe with MP Connect typically requires specifying a physical TCP port to be used by the writing and by the reading processes. Coding in this style opens the possibility for users to generate systems conflicts by inadvertently requesting ports that are in use. SAS^® Metadata Server allows one to allocate ports dynamically; that is, users can use a symbolic name for the port with the server dynamically determining an unused port to temporarily assign to the SAS^® job. While this capability is attractive, implementing SAS Metadata Server on a system which does not use any of the other SAS BI technology can be inefficient from a cost perspective. To enable dynamic port allocation without the added cost, we created a UNIX script which can be called from within SAS to ascertain which ports are available at runtime. The script returns a list of available ports which is captured in a SAS macro variable and subsequently used in establishing pipeline parallelism.

Business Intelligence (BI) dashboards serve as an invaluable, high-level, visual reference tool for decision-making processes in many business industries. A request was made to our department to develop some BI dashboards that could be incorporated in an academic setting. These dashboards would aim to serve various undergraduate executive and administrative staff at the university. While most business data may lend itself to work very well and easily in the development of dashboards, academic data is typically modeled differently and, therefore, faces unique challenges. In this paper, the authors detail and share the design and development process of creating dashboards for decision making in an academic environment utilizing SAS^® BI Dashboard 4.3 and other SAS^® Enterprise Business Intelligence 9.2 tools. The authors also provide lessons learned as well as recommendations for future implementations of BI dashboards utilizing academic data.

Traditionally, web applications interact with back-end databases by means of JDBC/ODBC connections to retrieve and update data. With the growing need for real-time charting and complex analysis types of data representation on these web applications, SAS computing power can be put to use by adding a SAS web service layer between the application and the database. With the experience that we have with integrating these applications to SAS^® BI Web Services, this is our attempt to point out five things to do when using SAS BI Web Services. 1) Input Data Sources: always enable Allow rewinding stream while creating the stored process. 2) Use LIBNAME statements to define XML filerefs for the Input and Output Streams (Data Sources). 3) Define input prompts and output parameters as global macro variables in the stored process if the stored process calls macros that use these parameters. 4) Make sure that all of the output parameters values are set correctly as defined (data type) before the end of the stored process. 5) The Input Streams (if any) should have a consistent data type; essentially, every instance of the stream should have the same structure. This paper consist of examples and illustrations of errors and warnings associated with the previously mentioned cases.

For almost two decades, Western Kentucky University's Office of Institutional Research (WKU-IR) has used SAS^® to help shape the future of the institution by providing faculty and administrators with information they can use to make a difference in the lives of their students. This presentation provides specific examples of how WKU-IR has shaped the policies and practices of our institution and discusses how WKU-IR moved from a support unit to a key strategic partner. In addition, the presentation covers the following topics: How the WKU Office of Institutional Research developed over time; Why WKU abandoned reactive reporting for a more accurate, convenient system using SAS^® Enterprise Intelligence Suite for Education; How WKU shifted from investigating what happened to predicting outcomes using SAS^® Enterprise Miner^™ and SAS^® Text Miner; How the office keeps the system relevant and utilized by key decision makers; What the office has accomplished and key plans for the future.

Based on selection criteria, the SAS^® Data Integration Studio loop or splitter transformations can be used to generate multiple output files. The ETL developer or SAS^® administrator can decide which transformation is better suited for the design, priorities, and SAS configuration at their site. Factors to consider are the setup, maintenance, and performance of the ETL job. The loop transformation requires an understanding of macros and a control table. The splitter transformation is more straightforward and self documenting. If time allows, creating and running a job with each transformation can provide benchmarking to measure performance. For a comparison of these two options, this paper shows an example of the same job using the loop or splitter transformation. For added testing metrics, one can adapt the LOGPARSE SAS macro to parse the job logs.

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're getting oriented and will provide short, straightforward answers. And we can share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure you are ready to begin your transition to SAS^® 9.4.

SAS^® 9.4 has improved clustering capabilities that allow for scalability and failover for middle-tier servers and the metadata server. In this presentation, we share our experiences with high-availability and failover testing done prior to SAS 9.4 availability. We discuss what we tested and lessons learned (good and bad) while doing the testing.

In this session, you learn how Kaiser Permanente has taken a centralized production support approach to using SAS^® Enterprise Guide^® 4.3 in the healthcare industry. Kaiser Permanente Northwest (KPNW) has designed standardized processes and procedures that have allowed KPNW to streamline the support of production content, which enabled KPNW analytical resources to focus more on new content development rather than on maintenance and support of steady state programs and processes. We started with over 200 individual SAS^® processes across four different SAS platforms, SAS Enterprise Guide, Mainframe SAS^®, PC SAS^® and SAS^® Data Integration Studio, in oder to standardize our development approach on SAS Enterprise Guide and build efficient and scalable processes within our department and across the region. We walk through the need for change, how the team was set up, provide an overview of the UNIX SAS platform, walk through the standard production requirements (developer pack), and review lessons learned.

Can you juggle? Maybe. Can you shuffle a deck of cards? Probably. Can you do both at the same time? Welcome to the world of SAS^® and LSF! Very few SAS Administrators start out learning LSF at the same time they learn SAS; most already know SAS, possibly starting out as a programmer or analyst, but now have to step up to an enterprise platform with shared resources. The biggest challenge on an enterprise platform? How to share! How to maximum the utilization of a SAS platform, yet still ensure everyone gets their fair share? This presentation will boil down the 2000+ pages of LSF documentation to provide an introduction into various LSF concepts: * Host * Clusters * Nodes * Queues * First-Come-First-Serve * Fairshare * and various configuration settings: UJOB_LIMIT, PJOB_LIMIT, etc. Plus some insight on where to configure all these settings which are set up by the installation process, and which can be configured by the SAS or LSF administrator. This session is definitely NOT for experts. It is for those about to step into an enterprise deployment of SAS, and want to understand how the SAS server sessions they know so well can run on a shared platform.

The Department of Market Monitoring (DMM) at California ISO is responsible for promoting a robust, competitive, and nondiscriminatory electric power market in California by keeping a close watch on the efficiency and effectiveness of the ancillary service, congestion management, and real-time spot markets. We monitor the potential of market participants to exercise undue market power, the behavior of market participants that is consistent with attempts to exercise market power and the market performance that results from the interaction of market structure with participant behavior. In order to perform monitoring activities effectively, DMM collects available data, designs, and implement reporting dashboards that track key market metrics. We are using various SAS^® BI tools to develop and employ metrics and analytic tools applicable to market structure, participant behavior, and market performance. This paper provides details about the effective use of various SAS BI tools to implement an automated real time market monitoring functionality.

SAS^® Visual Analytics delivers the power of approachable in-memory analytics in an intuitive web interface. The scalable technology behind SAS Visual Analytics should not benefit just the analyst or data scientist in your organization but indeed everyone regardless of their analytical background. This paper outlines a framework for the creation of a cloud deployment of SAS Visual Analytics using the SAS^® 9.4 platform. Based on proven best practices and existing customer implementations, the paper focuses on architecture, processes, and design for reliability and scalable multi-tenancy. The framework enables your organization to move away from the departmental view of the world and to offer analytical capabilities for consumerization and collaboration across the enterprise.

Traditionally, Java web applications interact with back-end databases by means of JDBC/ODBC connections to retrieve and update data. With the growing need for real-time charting and complex analysis types of data representation on these types of web applications, SAS^® computing power can be put to use by adding a SAS web service layer between the application and the database. This paper shows how a SAS web service layer can be used to render data to a JAVA application in a summarized form using SAS^® Stored Processes. This paper also demonstrates how inputs can be passed to a SAS Stored Process based on which computations/summarizations are made before output parameter and/or output data streams are returned to the Java application. SAS Stored Processes are then deployed as SAS^® BI Web Services using SAS^® Management Console, which are available to the JAVA application as a URL. We use the SOAP method to interact with the web services. XML data representation is used as a communication medium. We then illustrate how RESTful web services can be used with JSON objects being the communication medium between the JAVA application and SAS in SAS^® 9.3. Once this pipeline communication between the application, SAS engine, and database is set up, any complex manipulation or analysis as supported by SAS can be incorporated into the SAS Stored Process. We then illustrate how graphs and charts can be passed as outputs to the application.

SAS^® OLAP technology is used to organize and present summarized data for business intelligence applications. It features flexible options for creating and storing aggregations to improve performance and brings a powerful multi-dimensional approach to querying data. This paper focuses on managing security features available to OLAP cubes through the combination of SAS metadata and MDX logic.

The Purchasing Department is considering contracting with your team for a new SAS^® Enterprise BI application. He's already met with SAS^® and seen the sales pitch, and he is very interested. But the manager is a tightwad and not sure about spending the money. Also, he wants his team to be the primary developers for this new application. Before investing his money on training, programming, and support, he would like a proof-of-concept. This paper will walk you through the seven steps to create a SAS Enterprise BI POC project: Develop a kick-off meeting including a full demo of the SAS Enterprise BI tools. Set up your UNIX file systems and security. Set up your SAS metadata ACTs, users, groups, folders, and libraries. Make sure the necessary SAS client tools are installed on the developers machines. Hold a SAS Enterprise BI workshop to introduce them to the basics, including SAS^® Enterprise Guide^®, SAS^® Stored Processes, SAS^® Information Maps, SAS^® Web Report Studio, SAS^® Information Delivery Portal, and SAS^® Add-In for Microsoft Office, along with supporting documentation. Work with them to develop a simple project, one that highlights the benefits of SAS Enterprise BI and shows several methods for achieving the desired results. Last but not least, follow up! Remember, your goal is not to launch a full-blown application. Instead, we ll strive toward helping them see the potential in your organization for applying this methodology.

SAS^® platform installations are large, complex, growing, and ever-changing enterprise systems that support many diverse groups of users and content. A reliable metadata security implementation is critical for providing access to business resources in a methodical, organized, partitioned, and protected manner. With natural changes to users, groups, and folders from an organization s day-to-day activities, deviations from an original metadata security plan are very likely and can put protected resources at risk. Regular security testing can ensure compliance, but, given existing administrator commitments and the time consuming nature of manual testing procedures, it doesn't tend to happen. This paper discusses concepts and outlines several example test specifications from an automated metadata security testing framework being developed by Metacoda. With regularly scheduled, automated testing, using a well-defined set of test rules, administrators can focus on their other work, and let alerts notify them of any deviations from a metadata security test specification.

SAS^® Management Console was designed to control and monitor virtually all of the parts and features of the SAS^® Intelligence Platform. However, administering even a small SAS^® Business Intelligence system can be a daunting task. This paper presents a few techniques that will help you simplify your administrative tasks and enable you and your user community to get the most out of your system. The SAS^® Metadata Server stores most of the information required to maintain and run the SAS Intelligence Platform, which is obviously the heart of SAS BI. It stores information about libraries, users, database logons, passwords, stored processes, reports, OLAP cubes, and a myriad of other information. Organization of this metadata is an essential part of an optimally performing system. This paper discusses ways of organizing the metadata to serve your organization well. It also discusses some of the key features of SAS Management Console and best practices that will assist the administrator in defining roles, promoting, archiving, backing up, securing, and simply just organizing the data so that it can be found and accessed easily by administrators and users alike.

Have you found OS file permissions to be insufficient to tailor access controls to meet your SAS^® data security requirements? Have you found metadata permissions on tables useful for restricting access to SAS data, but then discovered that SAS programmers can avoid the permissions by issuing LIBNAME statements that do not use the metadata? Would you like to ensure that users have access to only particular rows or columns in SAS data sets, no matter how they access the SAS data sets? Metadata-bound libraries provide the ability to authorize access to SAS data by authenticated Metadata User and Group identities that cannot be bypassed by SAS programmers who attempt to avoid the metadata with direct LIBNAME statements. They also provide the ability to limit the rows and columns in SAS data sets that an authenticated user is allowed to see. The authorization decision is made in the bowels of the SAS^® I/O system, where it cannot be avoided when data is accessed. Metadata-bound libraries were first implemented in the second maintenance release of SAS^® 9.3 and were enhanced in SAS^® 9.4. This paper overviews the feature and discusses best practices for administering libraries bound to metadata and user experiences with bound data. It also discusses enhancements included in the first maintenance release of SAS 9.4.