This paper illustrates a high-level infrastructure discussion with some explanation of the SAS^® codes used to implement a configurable batch framework for managing and updating the data rows and row-level permissions in SAS^® OLAP Cube Studio. The framework contains a collection of reusable, parameter-driven Base SAS^® macros, Base SAS custom programs, and UNIX or LINUX shell scripts. This collection manages the typical steps and processes used for manipulating SAS files and for executing SAS statements. The Base SAS macro collection contains a group of utility macros that includes: concurrent /parallel processing macros, SAS^® Metadata Repository macros, SAS^® Scalable Performance Data Engine table macros, table lookup macros, table manipulation macros, and other macros. There is also a group of OLAP-related macros that includes OLAP utility macros and OLAP permission table processing macros.

This paper introduces a macro that generates a calendar report in two different formats. The first format displays the entire month in one plot, which is called a month-by-month calendar report. The second format displays the entire month in one row and is called an all-in-one calendar report. To use the macro, you just need to prepare a simple data set that has three columns: one column identifies the ID, one column contains the date, and one column specifies the notes for the dates. On the generated calendar reports, you can include notes and add different styles to certain dates. Also, the macro provides the option for you to decide whether those months in your data set that do not contain data should be shown on the reports.

Modernizing SAS^® assets within an enterprise is key to reducing costs and improving productivity. Modernization implies consolidating multiple SAS environments into a single shared enterprise SAS deployment. While the benefits of modernization are clear, the management of a single-enterprise deployment is sometimes a struggle between business units who once had autonomy and IT that is now responsible for managing this shared infrastructure. The centralized management and control of a SAS deployment is based on SAS metadata. This paper provides a practical approach to the shared management of a centralized SAS deployment using SAS^® Management Console. It takes into consideration the day-to-day needs of the business and IT requirements including centralized security, monitoring, and management. This document defines what resources are contained in SAS metadata, what responsibilities should be centrally controlled, and the pros and cons of distributing the administration of metadata content across the enterprise. This document is intended as a guide for SAS administrators and assumes that you are familiar with the concepts and terminology introduced in SAS^® 9.4 Intelligence Platform: Security Administration Guide.

All SAS^® data sets and variables have standard attributes. These include items such as creation date, engine, compression and sort information for data sets, and format and length information for variables. However, for the first time in SAS 9.4, the developer can add their own customized attributes to both data sets and variables. This paper shows how these extended attributes can be created, modified, and maintained. It suggests the sort of items that might be candidates for use as extended attributes and explains in what circumstances they can be used. It also provides a worked example of how they can be used to inform and aid the SAS programmer in creating SAS applications.

In 2013, the University of North Carolina (UNC) at Chapel Hill initiated enterprise-wide use of SAS^® solutions for reporting and data transformations. Just over one year later, the initial rollout was scheduled to go live to an audience of 5,500 users as part of an adoption of PeopleSoft ERP for Finance, Human Resources, Payroll, and Student systems. SAS^® Visual Analytics was used for primary report delivery as an embedded resource within the UNC Infoporte, an existing portal. UNC made the date. With the SAS solutions, UNC delivered the data warehouse and initial reports on the same day that the ERP systems went live. After the success of the initial launch, UNC continues to develop and evolve the solution with additional technologies, data, and reports. This presentation touches on a few of the elements required for a medium to large size organization to integrate SAS solutions such as SAS Visual Analytics and SAS^® Enterprise Business Intelligence within their infrastructure.

SAS^® Visual Analytics is deployed by many customers. IT departments are tasked with efficiently managing the server resources, achieving maximum usage of resources, optimizing availability, and managing costs. Business users expect the system to be available when needed and to perform to their expectations. Business executives who sponsor business intelligence (BI) and analytical projects like to see that their decision to support and finance the project meets business requirements. Business executives also like to know how different people in the organization are using SAS Visual Analytics. With the release of SAS Visual Analytics 7.1, new functionality is added to support the memory management of the SAS^® LASR™ Analytic Server. Also, new out-of-the-box usage and audit reporting is introduced. This paper covers BI-on-BI for SAS Visual Analytics. Also, all the new functionality introduced for SAS Visual Analytics administration and questions about the resource management, data compression, and out-of-the-box usage reporting of SAS Visual Analytics are also discussed. Key product capabilities are demonstrated.

Over the years, the SAS^® Business Intelligence platform has proved its importance in this big data world with its suite of applications that enable us to efficiently process, analyze, and transform huge amounts of business data. Within the data warehouse universe, 'batch execution' sits in the heart of SAS Data Integration technologies. On a day-to-day basis, batches run, and the current status of the batch is generally sent out to the team or to the client as a 'static' e-mail or as a report. From experience, we know that they don't provide much insight into the real 'bits and bytes' of a batch run. Imagine if the status of the running batch is automatically captured in one central repository and is presented on a beautiful web browser on your computer or on your iPad. All this can be achieved without asking anybody to send reports and with all 'post-batch' queries being answered automatically with a click. This paper aims to answer the same with a framework that is designed specifically to automate the reporting aspects of SAS batches and, yes, it is all about collecting statistics of the batch, and we call it - 'BatchStats.'

As the SAS^® platform becomes increasingly metadata-driven, it becomes increasingly important to get the structures and controls surrounding the metadata repository correct. This presentation aims to point out some of the considerations and potential pitfalls of working with the metadata infrastructure. It also suggests some solutions that have been used with the aim of making this process as simple as possible.

We regularly speak with organizations running established SAS^® 9.1.3 systems that have not yet upgraded to a later version of SAS^®. Often this is because their current SAS 9.1.3 environment is working fine, and no compelling event to upgrade has materialized. Now that SAS 9.1.3 has moved to a lower level of support and some very exciting technologies (Hadoop, cloud, ever-better scalability) are more accessible than ever using SAS^® 9.4, the case for migrating from SAS 9.1.3 is strong. Upgrading a large SAS ecosystem with multiple environments, an active development stream, and a busy production environment can seem daunting. This paper aims to demystify the process, suggesting outline migration approaches for a variety of the most common scenarios in SAS 9.1.3 to SAS 9.4 upgrades, and a scalable template project plan that has been proven at a range of organizations.

SAS^® has been an early leader in big data technology architecture that more easily integrates unstructured files across multi-tier data system platforms. By using SAS^® Data Integration Studio and SAS^® Enterprise Business Intelligence software, you can easily automate big data using SAS^® system accommodations for Hadoop open-source standards. At the same time, another seminal technology has emerged, which involves real-time multi-sensor data integration using Arduino microprocessors. This break-out session demonstrates the use of SAS^® 9.4 coding to define Hadoop clusters and to automate Arduino data acquisition to convert custom unstructured log files into structured tables, which can be analyzed by SAS in near real time. Examples include the use of SAS Data Integration Studio to create and automate stored processes, as well as tips for C language object coding to integrate to SAS data management, with a simple temperature monitoring application for Hadoop to Arduino using SAS.

To bring order to the wild world of big data, EMC and its partners have joined forces to meet customer challenges and deliver a modern analytic architecture. This unified approach encompasses big data management, analytics discovery and deployment via end-to-end solutions that solve your big data problems. They are also designed to free up more time for innovation, deliver faster deployments, and help you find new insights from secure and properly managed data. The EMC Business Data Lake is a fully-engineered, enterprise-grade data lake built on a foundation of core data technologies. It provides pre-configured building blocks that enable self-service, end-to-end integration, management and provisioning of the entire big data environment. Major benefits include the ability to make more timely and informed business decisions and realize the vision of analytics in weeks instead of months.SAS enhances the Federation Business Data Lake by providing superior breadth and depth of analytics to tackle any big data analytics problem an organization might have, whether it's fraud detection, risk management, customer intelligence, predictive assets maintenance and others. SAS and EMC work together to deliver a robust and comprehensive big data solution with reduced risk, automated provisioning and configuration and is purpose-built for big data analytics workloads.

So you are still writing SAS^® DATA steps and SAS macros and running them through a command-line scheduler. When work comes in, there is only one person who knows that code, and they are out--what to do? This paper shows how SAS applies extract, transform, load (ETL) modernization techniques with SAS^® Data Integration Studio to gain resource efficiencies and to break down the ETL black box. We are going to share the fundamentals (metadata foldering and naming standards) that ensure success, along with steps to ease into the pool while iteratively gaining benefits. Benefits include self-documenting code visualization, impact analysis on jobs and tables impacted by change, and being supportable by interchangeable bench resources. We conclude with demonstrating how SAS^® Visual Analytics is being used to monitor service-level agreements and provide actionable insights into job-flow performance and scheduling.

In previous papers I have described how many standard SAS/GRAPH^® plots can be converted easily to ODS Graphics by using simple PROC SGPLOT or SGPANEL code. SAS/GRAPH Annotate code would appear, at first sight, to be much more difficult to convert to ODS Graphics, but by using its layering features, many Annotate plots can be replicated in a more flexible and repeatable way. This paper explains how to convert many of your Annotate plots, so they can be reproduced using Base SAS^®.

The Centers for Disease Control and Prevention (CDC) went through a large migration from a mainframe to a Windows platform. This e-poster will highlight the Data Automated Transfer Utility (DATU) that was developed to migrate historic files between the two file systems using SAS^® macros and SAS/CONNECT^®. We will demonstrate how this program identifies the type of file, transfers the file appropriately, verifies the successful transfer, and provides the details in a Microsoft Excel report. SAS/CONNECT code, special system options, and mainframe code will be shown. In 2009, the CDC made the decision to retire a mainframe that was used for years of primarily SAS work. The replacement platform is a SAS grid system, based on Windows, which is referred to as the Consolidated Statistical Platform (CSP). The change from mainframe to Windows required the migration of over a hundred thousand files totaling approximately 20 terabytes. To minimize countless man hours and human error, an automated solution was developed. DATU was developed for users to migrate their files from the mainframe to the new Windows CSP or other Windows destinations. Approximately 95% of the files on the CDC mainframe were one of three file types: SAS data sets, sequential text files, and partitioned data sets (PDS) libraries. DATU dynamically determines the file type and uses the appropriate method to transfer the file to the assigned Windows destination. Variations of files are detected and handled appropriately. File variations include multiple SAS versions of SAS data sets and sequential files that contain binary values such as packed decimal fields. To mitigate the loss of numeric precision during the migration, SAS numeric variables are identified and promoted to account for architectural differences between mainframe and Windows platforms. To aid users in verifying the accuracy of the file transfer, the program compares file information of the source and destination files. When a SAS file is d ownloaded, PROC CONTENTS is run on both files, and the PROC CONTENTS output is compared. For sequential text files, a checksum is generated for both files and the checksum file is compared. A PDS file transfer creates a list of the members in the PDS and destination Windows folder, and the file lists are compared. The development of this program and the file migration was a daunting task. This paper will share some of our lessons learned along the way and the method of our implementation.

The DATA Step has served SAS^® programmers well over the years, and although it is handy, the new, exciting, and powerful DS2 is a significant alternative to the DATA Step by introducing an object-oriented programming environment. It enables users to effectively manipulate complex data and efficiently manage the programming through additional data types, programming structure elements, user-defined methods, and shareable packages, as well as threaded execution. This tutorial is developed based on our experiences with getting started with DS2 and learning to use it to access, manage, and share data in a scalable and standards-based way. It facilitates SAS users of all levels to easily get started with DS2 and understand its basic functionality by practicing the features of DS2.

For the Research Data Centers (RDCs) of the United States Census Bureau, the demand for disk space substantially increases with each passing year. Efficiently using the SAS^® data view might successfully address the concern about disk space challenges within the RDCs. This paper discusses the usage and benefits of the SAS data view to save disk space and reduce the time and effort required to manage large data sets. The ability and efficiency of the SAS data view to process regular ASCII, compressed ASCII, and other commonly used file formats are analyzed and evaluated in detail. The authors discuss ways in which using SAS data views is more efficient than the traditional methods in processing and deploying the large census and survey data in the RDCs.

Proper management of master data is a critical component of any enterprise information system. However, effective master data management (MDM) requires that both IT and Business understand the life cycle of master data and the fundamental principles of entity resolution (ER). This presentation provides a high-level overview of current practices in data matching, record linking, and entity information life cycle management that are foundational to building an effective strategy to improve data integration and MDM. Particular areas of focus are: 1) The need for ongoing ER analytics--the systematic and quantitative measurement of ER performance; 2) Investing in clerical review and asserted resolution for continuous improvement; and 3) Addressing the large-scale ER challenge through distributed processing.

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Building and maintaining a data warehouse can require a complex series of jobs. Having an ETL flow that is reliable and well integrated is one big challenge. An ETL process might need some pre- and post-processing operations on the database to be well integrated and reliable. Some might handle this via maintenance windows. Others like us might generate custom transformations to be included in SAS^® Data Integration Studio jobs. Custom transformations in SAS Data Integration Studio can be used to speed ETL process flows and reduce the database administrator's intervention after ETL flows are complete. In this paper, we demonstrate the use of custom transformations in SAS Data Integration Studio jobs to handle database-specific tasks for improving process efficiency and reliability in ETL flows.

Companies spend vast amounts of resources developing and enhancing proprietary software to clean their business data. Save time and obtain more accurate results by leveraging the SAS^® Quality Knowledge Base (QKB), formerly a DataFlux^® Data Quality technology. Tap into the existing QKB rules for cleansing contact information or product data, or easily design your own custom rules using the QKB editing tools. The QKB enables data management operations such as parsing, standardization, and fuzzy matching for contact information such as names, organizations, addresses, and phone numbers, or for product data attributes such as materials, colors, and dimensions. The QKB supports data in native character sets in over 38 locales. A single QKB can be shared by multiple SAS^® Data Management installations across your enterprise, ensuring consistent results on workstations, servers, and massive parallel processing systems such as Hadoop. In this breakout, a SAS R&D manager demonstrates the power and flexibility of the QKB, and answers your questions about how to deploy and customize the QKB for your environment.

While there has been tremendous progress in technologies related to data storage, high-performance computing, and advanced analytic techniques, organizations have only recently begun to comprehend the importance of parallel strategies that help manage the cacophony of concerns around access, quality, provenance, data sharing, and use. While data governance is not new, the drumbeat around it, along with master data management and data quality, is approaching a crescendo. Intensified by the increase in consumption of information, expectations about ubiquitous access, and highly dynamic visualizations, these factors are also circumscribed by security and regulatory constraints. In this paper, we provide a summary of what data governance is and its importance. We go beyond the obvious and provide practical guidance on what it takes to build out a data governance capability appropriate to the scale, size, and purpose of the organization and its culture. Moreover, we discuss best practices in the form of requirements that highlight what we think is important to consider as you provide that tactical linkage between people, policies, and processes to the actual data lifecycle. To that end, our focus includes the organization and its culture, people, processes, policies, and technology. Further, we include discussions of organizational models as well as the role of the data steward, and provide guidance on how to formalize data governance into a sustainable set of practices within your organization.

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At the University of North Carolina at Chapel Hill, we had the pleasure of rolling out a strong enterprise-wide SAS^® Visual Analytics environment in 10 months, with strong support from SAS. We encountered many bumps in the road, moments of both mountain highs and worrisome lows, as we learned what we could and could not do, and new ways to accomplish our goals. Our journey started in December of 2013 when a decision was made to try SAS Visual Analytics for all reporting, and incorporate other solutions only if and when we hit an insurmountable obstacle. We are still strongly using SAS Visual Analytics and are augmenting the tools with additional products. Along the way, we learned a number of things about the SAS Visual Analytics environment that are gems, whether one is relatively new to SAS^® or an old hand. Measuring what is happening is paramount to knowing what constraints exist in the system before trying to enhance performance. Targeted improvements help if measurements can be made before and after each alteration. There are a few architectural alterations that can help in general, but we have seen that measuring is the guaranteed way to know what the problems are and whether the cures were effective.

Business managers are seeing the value of incorporating business information and analytics in daily decision-making with real-time information, when and where it is needed during business meetings and customer engagements. Real-time access of customer and business information reduces the latency in decision-making with confidence and accuracy, increasing the overall efficiency of the company. SAS is introducing new product options with HTML5 and adding advanced features in SAS^® Mobile BI in SAS^® Visual Analytics 7.2 to enhance the reach and experience of business managers to SAS^® analytics and dashboards from SAS Visual Analytics. With SAS Mobile BI 7.2, SAS will push the limits of a business user's ability to author and change the content of dashboards and reports on mobile devices. This presentation focuses on both the new HTML5-based product options and the new advancements made with SAS Mobile BI that empower business users. We present in detail the scope and new features that are offered with the HTML5-based viewer and with SAS Mobile BI from SAS Visual Analytics. Since the new HTML5-based viewer and SAS Mobile BI are the viewer options for business users to visualize and consume the content from SAS Visual Analytics, this presentation demonstrates the two products in detail. Key product capabilities are demoed.

As a SAS^® Intelligence Platform Administrator, have your eyes ever glazed over as you performed repetitive tasks in SAS^® Management Console or some other administrative user interface? Perhaps you're setting up metadata for a new department, managing a set of backups, or promoting content between dev, test, and prod environments. Did you know there is a large library of batch utilities to help you automate many of these common administration tasks? This paper explores content reporting and management utilities, such as viewing authorizations or relationships between content, as well as administrative tasks such as analyzing, creating, or deleting metadata repositories or performing a backup of the system. The batch utilities can be incorporated into scripts so that you can run them repeatedly on either an ad hoc or scheduled basis. Give your mouse a rest and save yourself some time.

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. The target audience for this paper is primarily system administrators who will be installing, configuring, or administering the SAS 9.4 environment. (This paper is an updated version of the paper presented at SAS Global Forum 2014 and includes new features and software changes since the original paper was delivered, plus any relevant content that still applies. This paper includes information specific to SAS 9.4 and SAS 9.4 maintenance releases.)

In this session, we discuss the advantages of SAS^® Federation Server and how it makes it easier for business users to access secure data for reports and use analytics to drive accurate decisions. This frees up IT staff to focus on other tasks by giving them a simple method of sharing data using a centralized, governed, security layer. SAS Federation Server is a data server that provides scalable, threaded, multi-user, and standards-based data access technology in order to process and seamlessly integrate data from multiple data repositories. The server acts as a hub that provides clients with data by accessing, managing, and sharing data from multiple relational and non-relational data sources as well as from SAS^® data. Users can view data in big data sources like Hadoop, SAP HANA, Netezza, or Teradata, and blend them with existing database systems like Oracle or DB2. Security and governance features, such as data masking, ensure that the right users have access to the data and reduce the risk of exposure. Finally, data services are exposed via a REST API for simpler access to data from third-party applications.

No matter what type of programming you do in a pharmaceutical environment, there will eventually be a need to combine your data with a lookup table. This lookup table could be a code list for adverse events, a list of names for visits, one of your own summary data sets containing totals that you will be using to calculate percentages, or you might have your favorite way to incorporate it. This paper describes and discusses the reasons for using five different simple ways to merge data sets with lookup tables, so that when you take over the maintenance of a new program, you will be ready for anything!

The first task to accomplish our SAS^® 9.4 installation goal is to create an Amazon Web Services (AWS) secured EC2 (Elastic Compute Cloud 2) instance called a Virtual Private Cloud (VPC). Through a series of wizard-driven dialog boxes, the SAS administrator selects virtual CPUs (vCPUs, which have about a 2:1 ratio to cores ), memory, storage, and network performance considerations via regional availability zones. Then, there is a prompt to create a VPC that will be housed within the EC2 instance, along with a major component called subnets. A step to create a security group is next, which enables the SAS administrator to specify all of the VPC firewall port rules required for the SAS 9.4 application. Next, the EC2 instance is reviewed and a security key pair is either selected or created. Then the EC2 launches. At this point, Internet connectivity to the EC2 instance is granted by attaching an Internet gateway and its route table to the VPC and allocating and associating an elastic IP address along with a public DNS. The second major task involves establishing connectivity to the EC2 instance and a method of download for SAS software. In the case of the Linux Red Hat instance created here, putty is configured to use the EC2's security key pair (.ppk file). In order to transfer files securely to the EC2 instance, a tool such as WinSCP is installed and uses the putty connection for secure FTP. The Linux OS is then updated, and then VNCServer is installed and configured so that the SAS administrator can use a GUI. Finally, a Firefox web browser is installed to download the SAS^® Download Manager. After downloading the SAS Download Manager, a SAS depot directory is created on the Linux file system and the SAS Download Manager is run once we have provided the software order number and SAS installation key. Once the SAS software depot has been loaded, we can verify the success of the SAS software depot's download by running the SAS depot checker. The next pre-installatio n task is to take care of some Linux OS housekeeping. Local users (for example, the SAS installation ID), sas, and other IDs such as sassrv, lsfadmin, lsfuser, and sasdemo are created. Specific directory permissions are set for the installer ID sas. The ulimit setting for open files and max user processes are increased and directories are created for a SAS installation home and configuration directory. Some third-party tools such as python, which are required for SAS 9.4, are installed. Then Korn shell and other required Linux packages are installed. Finally, the SAS Deployment Manager installation wizard is launched and the multiple dialog boxes are filled out, with many defaults accepted and Next clicked. SAS administrators should consider running the SAS Deployment Manager twice, first to solely install the SAS software, and then later to configure. Finally, after SAS Deployment Manager completion, SAS post-installation tasks are completed.

Microsoft SharePoint has been adopted by a number of companies today as their content management tool because of its ability to create and manage documents, records, and web content. It is described as an enterprise collaboration platform with a variety of capabilities, and thus it stands to reason that this platform should also be used to surface content from analytical applications such as SAS^® and the R language. SAS provides various methods for surfacing SAS content through SharePoint. This paper describes one such methodology that is both simple and elegant, requiring only SAS Foundation. It also explains how SAS and R can be used together to form a robust solution for delivering analytical results. The paper outlines the approach for integrating both languages into a single security model that uses Microsoft Active Directory as the primary authentication mechanism for SharePoint. It also describes how to extend the authorization to SAS running on a Linux server where LDAP is used. Users of this system are blissfully ignorant of the back-end technology components, as we offer up a seamless interface where they simply authenticate to the SharePoint site and the rest is, as they say, magic.

Organizations are loading data into Hadoop platforms at an extraordinary rate. However, in order to extract value from these platforms, the data must be prepared for analytic exploit. As the volume of data grows, it becomes increasingly more important to reduce data movement, as well as to leverage the computing power of these distributed systems. This paper provides a cursory overview of SAS^® Data Loader, a product specifically aimed at these challenges. We cover the underlying mechanisms of how SAS Data Loader works, as well as how it's used to profile, cleanse, transform, and ultimately prepare data for analytics in Hadoop.

SAS^® customers benefit greatly when they are using the functionality, performance, and stability available in the latest version of SAS. However, the task of moving all SAS collateral such as programs, data, catalogs, metadata (stored processes, maps, queries, reports, and so on), and content to SAS^® 9.4 can seem daunting. This paper provides an overview of the steps required to move all SAS collateral from systems based on SAS^® 9.2 and SAS^® 9.3 to the current release of SAS^® 9.4.

Across the languages of SAS^® are many golden nuggets--functions, formats, and programming features just waiting to impress your friends and colleagues. Learning SAS over 30+ years, I have collected a few, and I offer them to you in this presentation.

In-database processing refers to the integration of advanced analytics into the data warehouse. With this capability, analytic processing is optimized to run where the data reside, in parallel, without having to copy or move the data for analysis. From a data governance perspective there are many good reasons to embrace in-database processing. Many analytical computing solutions and large databases use this technology because it provides significant performance improvements over more traditional methods. Come learn how Blue Cross Blue Shield of Tennessee (BCBST) uses in-database processing from SAS and Teradata.

SAS^® Environment Manager helps SAS^® administrators and system administrators manage SAS resources and effectively monitor the environment. SAS Environment Manager provides administrators with a centralized location for accessing and monitoring the SAS^® Customer Intelligence environment. This enables administrators to identify problem areas and to maintain an in-depth understanding of the day-to-day activities on the system. It is also an excellent way to predict the usage and growth of the environment for scalability. With SAS Environment Manager, administrators can set up monitoring for CI logs (for example, SASCustIntelCore6.3.log, SASCustIntelStudio6.3.log) and other general logs from the SAS^® Intelligence Platform. This paper contains examples for administrators who support SAS Customer Intelligence to set up this type of monitoring. It provides recommendations for approaches and for how to interpret the results from SAS Environment Manager.

The SAS^® Web Application Server is a lightweight server that provides enterprise-class features for running SAS^® middle-tier web applications. This server can be configured to use the SAS^® Web Infrastructure Platform Data Server for a transactional storage database. You can meet the high-availability data requirement in your business plan by implementing a SAS Web Infrastructure Data Server cluster. This paper focuses on how the SAS Web Infrastructure Data Server on the SAS middle tier can be configured for load balancing, and data replication involving multiple nodes. SAS^® Environment Manager and pgpool-II are used to enable these high-availability strategies, monitor the server status, and initiate failover as needed.

This paper describes how we reduced elapsed time for the third maintenance release for SAS^® 9.4 by as much as 22% by using the High Performance FICON for IBM System z (zHPF) facility to perform I/O for SAS^® files on IBM mainframe systems. The paper details the performance improvements, internal testing to quantify improvements, and the customer actions needed to enable zHPF on their system. The benefits of zHPF are discussed within the larger context of other techniques that a customer can use to accelerate processing of SAS files.

EBI administrators who are new to SAS^® Visual Analytics and used to the logging capability of the SAS^® OLAP Server might be wondering how they can get their SAS^® LASR™ Analytic Server to produce verbose log files. While the SAS LASR Analytic Server logs differ from those produced by the SAS OLAP Server, the SAS LASR Analytic Server log contains information about each request made to LASR tables and can be a great data source for administrators looking to learn more about how their SAS Visual Analytics deployments are being used. This session will discuss how to quickly enable logging for your SAS LASR Analytic Server in SAS Visual Analytics 6.4. You will see what information is available to a SAS administrator in these logs, how they can be parsed into data sets with SAS code, then loaded back into the SAS LASR Analytic Server to create SAS Visual Analytics explorations and reports.

The SAS^® Environment Manager Service Architecture expands on the core monitoring capabilities of SAS^® Environment Manager delivered in SAS^® 9.4. Multiple sources of data available in the SAS^® Environment Manager Data Mart--traditional operational performance metrics, events, and ARM, audit, and access logs--together with built-in and custom reports put powerful capabilities into the hands of IT operations. This paper introduces the concept of service-oriented even identification and discusses how to use the new architecture and tools effectively as well as the wealth of data available in the SAS Environment Manager Data Mart. In addition, extensions for importing new data, writing custom reports, instrumenting batch SAS^® jobs, and leveraging and extending auditing capabilities are explored.

It is a common task to reshape your data from long to wide for the purpose of reporting or analytical modeling and PROC TRANSPOSE provides a convenient way to accomplish this. However, when performing the transpose action on large tables stored in a database management system (DBMS) such as Teradata, the performance of PROC TRANSPOSE can be significantly compromised. In this case, it is more efficient for the DBMS to perform the transpose task. SAS^® provides in-database processing technology in PROC SQL, which allows the SQL explicit pass-through method to push some or all of the work to the DBMS. This technique has facilitated integration between SAS and a wide range of data warehouses and databases, including Teradata, EMC Greenplum, IBM DB2, IBM Netezza, Oracle, and Aster Data. This paper uses the Teradata database as an example DBMS and explains how to transpose a large table that resides in it using the SQL explicit pass-through method. The paper begins with comparing the execution time using PROC TRANSPOSE with the execution time using SQL explicit pass-through. From this comparison, it is clear that SQL explicit pass-through is more efficient than the traditional PROC TRANSPOSE when transposing Teradata tables, especially large tables. The paper explains how to use the SQL explicit pass-through method and discusses the types of data columns that you might need to transpose, such as numeric and character. The paper presents a transpose solution for these types of columns. Finally, the paper provides recommendations on packaging the SQL explicit pass-through method by embedding it in a macro. SAS programmers who are working with data stored in an external DBMS and who would like to efficiently transpose their data will benefit from this paper.

Many companies use geographically dispersed data centers running SAS^® Grid Manager to provide 24/7 SAS^® processing capability with the thought that if a disaster takes out one of the data centers, another data center can take over the SAS processing. To accomplish this, careful planning must take into consideration hardware, software, and communication infrastructure along with the SAS workload. This paper looks into some of the options available, focusing on using SAS Grid Manager to manage the disaster workload shift.

Researchers, patients, clinicians, and other health-care industry participants are forging new models for data-sharing in hopes that the quantity, diversity, and analytic potential of health-related data for research and practice will yield new opportunities for innovation in basic and translational science. Whether we are talking about medical records (for example, EHR, lab, notes), administrative data (claims and billing), social (on-line activity), behavioral (fitness trackers, purchasing patterns), contextual (geographic, environmental), or demographic data (genomics, proteomics), it is clear that as health-care data proliferates, threats to security grow. Beginning with a review of the major health-care data breeches in our recent history, we highlight some of the lessons that can be gleaned from these incidents. In this paper, we talk about the practical implications of data sharing and how to ensure that only the right people have the right access to the right level of data. To that end, we explore not only the definitions of concepts like data privacy, but we discuss, in detail, methods that can be used to protect data--whether inside our organization or beyond its walls. In this discussion, we cover the fundamental differences between encrypted data, 'de-identified', 'anonymous', and 'coded' data, and methods to implement each. We summarize the landscape of maturity models that can be used to benchmark your organization's data privacy and protection of sensitive data.

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Sometimes you need to provide multiple administrators with the ability to manage your software. The rationale can be a need to separate roles and responsibilities (such as installer and configuration manager), changing job responsibilities, or even just covering for the primary administrator while on vacation. To meet that need, it's tempting to share the logon credentials of your SAS^® installer account, but doing so can potentially compromise your security and cause a corporate audit to fail. This paper focuses on standard IT practices and utilities, explaining how to diligently manage the administration of your SAS software to help you properly ensure that access is secured and that auditability is maintained.

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SAS^® Visual Analytics provides numerous capabilities to analyze data lightning fast and make key business decisions that are critical for day-to-day operations. Depending on your organization, be it Human Resources, Sales, or Finance, the data can be easily mined by decision makers, providing information that empowers the user to make key business decisions. The right data preparation during report development is the key to success. SAS Visual Analytics provides the ability to explore the data and to make forecasts using automatic charting capabilities with a simple click-and-choose interface. The ability to load all the historical data into memory enables you to make decisions by analyzing the data patterns. The decision is within reach when the report designer uses SAS^® Visual Analytics Designer functionality like alerts, display rules, ranks, comments, and others. Planning your data preparation task is critical for the success of the report. Identify the category and measure values in the source data, and convert them appropriately, based on your planned usage. SAS Visual Analytics has capabilities that help perform conversion on the fly. Creating meaningful derived variables on the go and hierarchies on the run reduces development time. Alerts notifications are sent to the right decision makers by e-mail when the report objects contain data that meets certain criteria. The system monitors the data, and the report developer can specify how frequently the system checks are made and the frequency at which the notifications are sent. Display rules help in highlighting the right metrics to leadership, which helps focus the decision makers on the right metric in the data maze. For example, color coding the metrics quickly tells the report user which business problems require action. Ranking the metrics, such as top 10 or bottom 10, can help the decision makers focus on a success or on problem areas. They can drill into more details about why they stand out or fall b ehind. Discussing a report metric in a particular report can be done using the comments feature. Responding to other comments can lead to the right next steps for the organization. Also, data quality is always monitored when you have actionable reports, which helps to create a responsive and reliable reporting environment.

SAS^® Analytics enables organizations to tackle complex business problems using big data and to provide insights needed to make critical business decisions. A well-architected enterprise storage infrastructure is needed to realize the full potential of SAS Analytics. However, as the need for big data analytics and rapid response times increases, the performance gap between server speeds and traditional hard disk drive (HDD) based storage systems can be a significant concern. The growing performance gap can have detrimental effects, particularly when it comes to critical business applications. As a result, organizations are looking for newer, smarter, faster storage systems to accelerate business insights. IBM FlashSystem Storage systems store the data in flash memory. They are designed for dramatically faster access times and support incredible amounts of input/output operations per second (IOPS) and throughput, with significantly lower latency than HDD-based solutions. Due to their macro-efficiency design, FlashSystem Storage systems consume less power and have significantly lower cooling and space requirements, while allowing server processors to run SAS Analytics more efficiently. Being an all-flash storage system, IBM FlashSystem provides consistent low latency response across IOPS range, as the analytics workload scales. This paper introduces the benefits of IBM FlashSystem Storage for deploying SAS Analytics and highlights some of the deployment scenarios and architectural considerations. This paper also describes best practices and tuning guidelines for deploying SAS Analytics on FlashSystem Storage systems, which would help SAS Analytics customers in architecting solutions with FlashSystem Storage.

Can you create hundreds of great looking Microsoft Excel tables all within SAS^® and make them all Section 508 compliant at the same time? This paper examines how to use the ODS TAGSETS.EXCELXP statement and other Base SAS^® features to create fantastic looking Excel worksheet tables that are all Section 508 compliant. This paper demonstrates that there is no need for any outside intervention or pre- or post-meddling with the Excel files to make them Section 508 compliant. We do it all with simple Base SAS code.

SAS^® Studio (previously known as SAS^® Web Editor) was introduced in the first maintenance release of SAS^® 9.4 as an alternative programming environment to SAS^® Enterprise Guide^® and SAS^® Display Manager. SAS Studio is different in many ways from SAS Enterprise Guide and SAS Display Manager. As a programmer, I currently use SAS Enterprise Guide to help me code, test, maintain, and organize my SAS^® programs. I have SAS Display Manager installed on my PC, but I still prefer to write my programs in SAS Enterprise Guide because I know it saves my log and output whenever I run a program, even if that program crashes and takes the SAS session with it! So should I now be using SAS Studio instead, and should you be using it, too?

Once you have a SAS^® Visual Analytics environment up and running, the next important piece to the puzzle is to keep your users happy by keeping their data loaded and refreshed on a consistent basis. Loading data from the SAS Visual Analytics UI is both a great first start and great for ad hoc data exploring. But automating this data load so that users can focus on exploring the data and creating reports is where to power of SAS Visual Analytics comes into play. By using tried-and-true SAS^® Data Integration Studio techniques (both out of the box and custom transforms), you can easily make this happen. Proven techniques such as sweeping from a source library and stacking similar Hadoop Distributed File System (HDFS) tables into SAS^® LASR™ Analytic Server for consumption by SAS Visual Analytics are presented using SAS Visual Analytics and SAS Data Integration Studio.

Institutional research and effectiveness offices at most institutions are often the primary beneficiaries of the data warehouse (DW) technologies. However, at many institutions, building the data warehouse for growing accountability, decision support, and the institutional effectiveness needs are still unfulfilled, in part due to the growing data volumes as well as the prohibitively expensive data warehousing costs built by UIT departments. In recent years, many institutional research offices in the country are often asked to take a leadership role in building the DW or partner with the campus IT department to improve the efficiency and effectiveness of the DW development. Within this context, the Office of Institutional Research and Effectiveness at a large public research university in the north east was entrusted with the responsibility to build the new campus data warehouse for growing needs such as resource allocation, competitive positioning, new program development in emerging STEM disciplines, and accountability reporting. These requirements necessitated the deployment of state-of-the-art analytical decision support applications, such as SAS^® Visual Analytics (reporting and analysis), SAS^® Visual Statistics (predictive), in a disparate data environment, including PeopleSoft (student), Kuali (finance), Genesys (human resources), and homegrown sponsored funding database. This presentation focuses on the efforts of institutional research and effectiveness offices in developing the decision support applications using the SAS^® Enterprise business intelligence and analytical solutions. With users ranging from nontechnical to advanced analysts, greater efficiency lies in the ability to get faster and more elegant reporting from those huge stores of data and being able to share the resulting discoveries across departments. Most of the reporting applications were developed based on the needs of IPEDS, CUPA, Common Data Set, US News and World Report, g raduation and retention, and faculty activity, and deployed through an online web-based portal. The participants will learn how the University quickly analyzes institutional data through an easy-to-use, drag-and-drop, web-based application. This presentation demonstrates how to use SAS^® Visual Analytics to quickly design reports that are attractive, interactive, and meaningful and then distribute those reports via the web, or through SAS^® Mobile BI on an iPad^® or tablet.

Is your company using or considering using SAP Business Warehouse (BW) powered by SAP HANA? SAS^® provides various levels of integration with SAP BW in an SAP HANA environment. This integration enables you to not only access SAP BW components from SAS, but to also push portions of SAS analysis directly into SAP HANA, accelerating predictive modeling and data mining operations. This paper explains the SAS toolset for different integration scenarios, highlights the newest technologies contributing to integration, and walks you through examples of using SAS with SAP BW on SAP HANA. The paper is targeted at SAS and SAP developers and architects interested in building a productive analytical environment with the help of the latest SAS and SAP collaborative advancements.

One of the challenges in Secure Socket Layer (SSL) configuration for any web configuration is the SSL certificate management for client and server side. The SSL overview covers the structure of the x.509 certificate and SSL handshake process for the client and server components. There are three distinctive SSL client/server combinations within the SAS^® Visual Analytics 7.1 web application configuration. The most common one is the browser accessing the web application. The second one is the internal SAS^® web application accessing another SAS web application. The third one is a SAS Workspace Server executing a PROC or LIBNAME statement that accesses the SAS^® LASR™ Authorization Service web application. Each SSL client/server scenario in the configuration is explained in terms of SSL handshake and certificate arrangement. Server identity certificate generation using Microsoft Active Directory Certificate Services (AD CS) for enterprise level organization is showcased. The certificates, in proper format, need to be supplied to the SAS^® Deployment Wizard during the configuration process. The prerequisites and configuration steps are shown with examples.

The Hadoop ecosystem is vast, and there's a lot of conflicting information available about how to best secure any given implementation. It's also difficult to fix any mistakes made early on once an instance is put into production. In this paper, we demonstrate the currently accepted best practices for securing and Kerberizing Hadoop clusters in a vendor-agnostic way, review some of the not-so-obvious pitfalls one could encounter during the process, and delve into some of the theory behind why things are the way they are.

Your enterprise SAS^® Visual Analytics implementation is on its way to being adopted throughout your organization, unleashing the production of critical business content by business analysts, data scientists, and decision makers from many business units. This content is relied upon to inform decisions and provide insight into the results of those decisions. With the development of SAS Visual Analytics content decentralized into the hands of business users, the use of automated version control is essential to providing protection and recovery in the event of inadvertent changes to that content. Re-creation of complex report objects accidentally modified by a business user is time-consuming and can be eliminated by maintaining a version control repository of report (and other) objects created in SAS Visual Analytics. This paper walks through the steps for implementing an automated process for version control using SAS^®. This process can be applied to all types of metadata objects used in multiple SAS application development and analysis environments, such as reports and explorations from SAS Visual Analytics, and jobs, tables, and libraries from SAS^® Data Integration Studio. Basic concepts for the process, as well as specific techniques used for our implementation are included. So eliminate the risk of content loss for your business users and the burden of manual version control for your applications developers. Your IT shop will enjoy time savings and greater reliability.

Video games used to be child's play. Today, millions of gamers of all ages kill countless in-game monsters and villains every day. Gaming is big business, and the data it generates is even bigger. Massive multi-player online games like World of Warcraft by Blizzard Entertainment not only generate data that Blizzard Entertainment can use to monitor users and their environments, but they can also be set up to log player data and combat logs client-side. Many users spend time analyzing their playing 'rotations' and use the information to adjust their playing style to deal more damage or, more appropriately, to heal themselves and other players. This paper explores World of Warcraft logs by using SAS^® Visual Analytics and applies statistical techniques by using SAS^® Visual Statistics to discover trends.

With cloud service providers such as Amazon commodifying the process to create a server instance based on desirable OS and sizing requirements for a SAS^® implementation, a definite advantage is the speed and simplicity of getting started with a SAS installation. Planning horizons are nonexistent, and initial financial outlay is economized because no server hardware procurement occurs, no data center space reserved, nor any hardware/OS engineers assigned to participate in the initial server instance creation. The cloud infrastructure seems to make the OS irrelevant, an afterthought, and even just an extension of SAS software. In addition, if the initial sizing, memory allocation, or disk space selection results later in some deficiency or errors in SAS processing, the flexibility of the virtual server instance allows the instance image to be saved and restored to a new, larger, or performance-enhanced instance at relatively low cost and minor inconvenience to production users. Once logged on with an authenticated ID, with Internet connectivity established, a SAS installer ID created, and a web browser started, it's just a matter of downloading the SAS^® Download Manager to begin the creation of the SAS software depot. Many Amazon cloud instances have download speeds that tend to be greater and processing time that is shorter to create the depot. Installing SAS via the SAS^® Deployment Wizard is not dissimilar on a cloud instance versus a server instance, and all the same challenges (for example, SSL, authentication and single sign-on, and repository migration) apply. Overall, SAS administrators have an optimal, straightforward, and low-cost opportunity to deploy additional SAS instances running different versions or more complex configurations (for example, SAS^® Grid Computing, resource-based load balancing, and SAS jobs split and run parallel across multiple nodes). While the main advantages of using a cloud instance to deploy a new SAS i mplementation tend to revolve around efficiency, speed, and affordability, its pitfalls have to do with vulnerability to intrusion and external attack. The same easy, low-cost server instance launch also has a negative flip side that includes a possible lack of experienced OS oversight and basic security precaution. At the moment, Linux administrators around the country are patching their physical and virtual systems to prevent the spread of the Shellshock vulnerability for web servers that originated in cloud instances. Cloud instances have also been targeted and credentials compromised which, in some cases, have allowed thousands of new instances to be spun up and billed to an unsuspecting AWS licensed user. Extra steps have to be taken to prevent the aforementioned attacks and fortunately, there are cloud-based methods available. By creating a Virtual Private Cloud (VPC) instance, AWS users can restrict access by originating IP addresses while also requiring additional administration, including creating entries for application ports that require external access. Moreover, with each step toward more secure cloud implementations, there are additional complexities that arise, including making additional changes or compromises with corporate firewall policy and user authentication methods.

For the past two academic school years, our SAS^® Programming 1 class had a classroom discussion about the Charlotte Bobcats. We wondered aloud If the Bobcats changed their team name would the dwindling fan base return? As a class, we created a survey that consisted of 10 questions asking people if they liked the name Bobcats, did they attend basketball games, and if they bought merchandise. Within a one-hour class period, our class surveyed 981 out of 1,733 students at Phillip O. Berry Academy of Technology. After collecting the data, we performed advanced analytics using Base SAS^® and concluded that 75% of students and faculty at Phillip O. Berry would prefer any other name except the Bobcats. In other results, 80% percent of the student body liked basketball, and the most preferred name was the Hornets, followed by the Royals, Flight, Dragons, and finally the Bobcats, The following school year, we conducted another survey to discover if people's opinions had changed since the previous survey and if people were happy with the Bobcats changing their name. During this time period, the Bobcats had recently reported that they were granted the opportunity to change the team name to the Hornets. Once more, we collected and analyzed the data and concluded that 77% percent of people surveyed were thrilled with the name change. In addition, around 50% percent of surveyors were interested in purchasing merchandise. Through the work of this project, SAS^® Analytics was applied in the classroom to a real world scenario. The ability to see how SAS^® could be applied to a question of interest and create change inspired the students in our class. This project is significantly important to show the economic impact that sports can have on a city. This project in particular, focused on the nostalgia that people of the city of Charlotte felt for the name Hornets. The project opened the door for more analysis and questions and continues to spa rk interest. This is the case because when people have a connection to the team and the more the team flourishes, the more Charlotte benefits.

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Many industries are challenged with requirements to protect information and limit its access. In this paper, we will discuss various approaches for row-level access to LASR tables and demonstrate our implementation. Methods discussed in this paper include security joins in data queries, using star schema with security table as one dimension, permission conditions based on metadata stored user information, and user IDs being associated with data as a dedicated column. The paper then identifies shortcomings and strengths of various approaches as well as our iterations to satisfy business needs that led us to our row-level permissions implementation. In addition, the paper offers recommendations and other considerations to keep in mind while working on row-level persmissions with LASR tables.

The SAS^® LASR™ Analytic Server acts as a back-end, in-memory analytics engine for solutions such as SAS^® Visual Analytics and SAS^® Visual Statistics. It is designed to exist in a massively scalable, distributed environment, often alongside Hadoop. This paper guides you through the impacts of the architecture decisions shared by both software applications and what they specifically mean for SAS^®. We then present positive actions you can take to rebound from unexpected outages and resume efficient operations.

This unique culture has access to lots of data, unstructured and structured; is innovative, experimental, groundbreaking, and doesn't follow convention; and has access to powerful new infrastructure technologies and scalable, industry-standard computing power like never seen before. The convergence of data, and innovative spirit, and the means to process it is what makes this a truly unique culture. In response to that, SAS^® proposes The New Analytics Experience. Attend this session to hear more about the New Analytics Experience and the latest Intel technologies that make it possible.

SAS^® Visual Analytics is a product that easily enables the interactive analysis of data. It offers capabilities for analyzing data using a visual approach. This paper discusses architecture options for configuring a SAS Visual Analytics installation that serves multiple customers in parallel. The overall objective is to create an environment that scales with the volume of data and also with the number of customer groups. This paper explains several concepts for serving multiple customers groups and explains the pros and cons of each approach.

SAS^® Visual Analytics is very responsive in analyzing historical data, and it takes advantage of in-memory data. Data query, exploration, and reports form the basis of the tool, which also has other forward-looking techniques such as star schemas and stored processes. A security model is established by defining the permissions through a web-based application that is stored in a database table. That table is brought to the SAS Visual Analytics environment as a LASR table. Typically, security is established based on the departmental access, geographic region, or other business-defined groups. This permission table is joined with the underlying base table. Security is defined by a data filter expression through a conditional grant using SAS^® metadata identities. The in-memory LASR star schema is very similar to a typical star schema. A single fact table that is surrounded by dimension tables is used to create the star schema. The star schema gives you the advantage of loading data quickly on the fly. Each of the dimension tables is joined to the fact table with a dimension key. A SAS application that gives the flexibility and the power of coding is created as a stored process that can be executed as requested by client applications such as SAS Visual Analytics. Input data sources for stored processes can be either LASR tables in the SAS^® LASR™ Analytic Server or any other data that can be reached through the stored process code logic.

The NH Citizens Health Initiative and the University of New Hampshire Institute for Health Policy and Practice, in collaboration with Accountable Care Project (ACP) participants, have developed a set of analytic reports to provide systems undergoing transformation a capacity to compare performance on the measures of quality, utilization, and cost across systems and regions. The purpose of these reports is to provide data and analysis on which our ACP learning collaborative can share knowledge and develop action plans that can be adopted by health-care innovators in New Hampshire. This breakout session showcases the claims-based reports, powered by SAS^® Visual Analytics and driven by the New Hampshire Comprehensive Health Care Information System (CHIS), which includes commercial, Medicaid, and Medicare populations. With the power of SAS Visual Analytics, hundreds of pages of PDF files were distilled down to a manageable, dynamic, web-based portal that allows users to target information most appealing to them. This streamlined approach reduces barriers to obtaining information, offers that information in a digestible medium, and creates a better user experience. For more information about the ACP or to access the public reports, visit http://nhaccountablecare.org/.

SAS^® platform administrators always feel the pinch of not having information about how much storage space is occupied by each user on one specific file system or in the entire environment. Sometimes the platform administrator does not have access to all users' folders, so they have to plan for the worst. There are multiple approaches to tackle this problem. One of the better methods is to initiate an alert mechanism to notify a user when they are in the top 10 file system users on the system.

The latest releases of SAS^® Data Integration Studio and DataFlux^® Data Management Platform provide an integrated environment for managing and transforming your data to meet new and increasingly complex data management challenges. The enhancements help develop efficient processes that can clean, standardize, transform, master, and manage your data. The latest features include capabilities for building complex job processes, new web-based development and job monitoring environments, enhanced ELT transformation capabilities, big data transformation capabilities for Hadoop, integration with the analytic platform provided by SAS^® LASR™ Analytic Server, enhanced features for lineage tracing and impact analysis, and new features for master data and metadata management. This paper provides an overview of the latest features of the products and includes use cases and examples for leveraging product capabilities.

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Working with multiple data sources in SAS^® was not a straight forward thing until PROC FEDSQL was introduced in the SAS^® 9.4 release. Federated Query Language, or FEDSQL, is a vendor-independent language that provides a common SQL syntax to communicate across multiple relational databases without having to worry about vendor-specific SQL syntax. PROC FEDSQL is a SAS implementation of the FEDSQL language. PROC FEDSQL enables us to write federated queries that can be used to perform joins on tables from different databases with a single query, without having to worry about loading the tables into SAS individually and combining them using DATA steps and PROC SQL statements. The objective of this paper is to demonstrate the working of PROC FEDSQL to fetch data from multiple data sources such as Microsoft SQL Server database, MySQL database, and a SAS data set, and run federated queries on all the data sources. Other powerful features of PROC FEDSQL such as transactions and FEDSQL pass-through facility are discussed briefly.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.

How often have you pulled oodles of data out of the corporate data warehouse down into SAS^® for additional processing? This additional processing, sometimes thought to be uniquely SAS, might include FIRST. logic, cumulative totals, lag functionality, specialized summarization, or advanced date manipulation. Using the analytical (or OLAP) and Windowing functionality available in many databases (for example, in Teradata and IBM Netezza ), all of this processing can be performed directly in the database without moving and reprocessing detail data unnecessarily. This presentation illustrates how to increase your coding and execution efficiency by using the database's power through your SAS environment.

As SAS^® products become more web-oriented and sophisticated, SAS administrators face an increased challenge to manage their SAS middle-tier environments. They want to know the answers to important critical questions when planning, installing, configuring, deploying, and administrating their SAS products. They also need to meet the requirements of high performance, high availability, increased security, maintainability, and more. In this paper, we identify the most common and challenging questions that most of our administrators and customers have asked. These questions range across topics such as SAS middle-tier architecture, clustering, performance, security, and administration using SAS^® Environment Manger. These questions come from many sources such as technical support, consultants, and internal customer experience testing teams. The specific questions include: what is new in SAS 9.4 mid-tier infrastructure and why that is better for me; should I use the SAS Web Server or can I use another third party Web Server in my deployment; where can I deploy customer dynamic web applications and static contents; what are the SAS JRE, SAS Web Server, SAS Web Application Server upgrade policy and process; how to architect and configure to achieve High Availability for EBI and VA; how to install, update or add my products for cluster members; how can I tune the mid-tier performance and improve the start-up time of my SAS Web Application Server; what options are available for configuring SSL; what is the security policy, what security patches are available and how to apply them; how can I manage my mid-tier infrastructure and applications and how the user and account are managed in SAS Environment Manager? The paper will present detailed answers for these questions and also point out where you can find more information. We believe that with the answers to these questions, you, SAS administrators, can better implement and manage your SAS environment with a higher confide nce and satisfaction.