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.'

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.

During the cementing and pumps-off phase of oil drilling, drilling operations need to know, in real time, about any loss of hydrostatic or mechanical well integrity. This phase involves not only big data, but also high-velocity data. Today's state-of-the-art drilling rigs have tens of thousands of sensors. These sensors and their data output must be correlated and analyzed in real time. This paper shows you how to leverage SAS^® Asset Performance Analytics and SAS^® Enterprise Miner™ to build a model for drilling and well control anomalies, fingerprint key well control measures of the transienct fluid properties, and how to operationalize these analytics on the drilling assets with SAS^® event stream processing. We cover the implementation and results from the Deepwater Horizon case study, demonstrating how SAS analytics enables the rapid differentiation between safe and unsafe modes of operation.

As organizations strive to do more with fewer resources, many modernize their disparate PC operations to centralized server deployments. Administrators and users share many concerns about using SAS^® on a Microsoft Windows server. This paper outlines key guidelines, plus architecture and performance considerations, that are essential to making a successful transition from PC to server. This paper outlines the five key considerations for SAS customers who will change their configuration from PC-based SAS to using SAS on a Windows server: 1) Data and directory references; 2) Interactive and surrounding applications; 3) Usability; 4) Performance; 5) SAS Metadata Server.

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.)

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.

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.

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.

Okay, you've read all the books, manuals, and papers and can produce graphics with SAS/GRAPH^® and Output Delivery System (ODS) Graphics with the best of them. But how do you handle the Final Mile problem--getting your images generated in SAS^® sized just right and positioned just so in Microsoft Excel? This paper presents a method of doing so that employs SAS Integration Technologies and Excel Visual Basic for Applications (VBA) to produce SAS graphics and automatically embed them in Excel worksheets. This technique might be of interest to all skill levels. It uses Base SAS^®, SAS/GRAPH, ODS Graphics, the SAS macro facility, SAS^® Integration Technologies, Microsoft Excel, and VBA.

REST is being used across the industry for designing networked applications to provide lightweight and powerful alternatives to web services such as SOAP and Web Services Description Language (WSDL). Since REST is based entirely around HTTP, SAS^® provides everything you need to make REST calls and process structured and unstructured data alike. Learn how PROC HTTP and other SAS language features provide everything you need to simply and securely make use of REST.

A leading killer in the United States is smoking. Moreover, over 8.6 million Americans live with a serious illness caused by smoking or second-hand smoking. Despite this, over 46.6 million U.S. adults smoke tobacco, cigars, and pipes. The key analytic question in this paper is, How would e-cigarettes affect this public health situation? Can monitoring public opinions of e-cigarettes using SAS^® Text Analytics and SAS^® Visual Analytics help provide insight into the potential dangers of these new products? Are e-cigarettes an example of Big Tobacco up to its old tricks or, in fact, a cessation product? The research in this paper was conducted on thousands of tweets from April to August 2014. It includes API sources beyond Twitter--for example, indicators from the Health Indicators Warehouse (HIW) of the Centers for Disease Control and Prevention (CDC)--that were used to enrich Twitter data in order to implement a surveillance system developed by SAS^® for the CDC. The analysis is especially important to The Office of Smoking and Health (OSH) at the CDC, which is responsible for tobacco control initiatives that help states to promote cessation and prevent initiation in young people. To help the CDC succeed with these initiatives, the surveillance system also: 1) automates the acquisition of data, especially tweets; and 2) applies text analytics to categorize these tweets using a taxonomy that provides the CDC with insights into a variety of relevant subjects. Twitter text data can help the CDC look at the public response to the use of e-cigarettes, and examine general discussions regarding smoking and public health, and potential controversies (involving tobacco exposure to children, increasing government regulations, and so on). SAS^® Content Categorization helps health care analysts review large volumes of unstructured data by categorizing tweets in order to monitor and follow what people are saying and why they are saying it. Ultimatel y, it is a solution intended to help the CDC monitor the public's perception of the dangers of smoking and e-cigarettes, in addition, it can identify areas where OSH can focus its attention in order to fulfill its mission and track the success of CDC health initiatives.

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.

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.