At Royal Bank of Scotland, business intelligence users require sophisticated security permissions both at object level and data (row) level in order to comply with data security, audit, and regulatory requirements. When we rolled out SAS^® Visual Analytics to our two main stakeholder groups, this was identified as a key requirement as data is no longer restricted to the desktop but is increasingly available on mobile devices such as tablets and smart phones. Implementing row-level security (RLS) controls, in addition to standard security measures such as authentication, is a most effective final layer in your data authorization process. RLS procedures in leading relational database management systems (RDBMSs) and business intelligence (BI) software are fairly commonplace, but with the emergence of big data and in-memory visualization tools such as SAS^® Visual Analytics, those RLS procedures now need to be extended to the memory interface. Identity-driven row-level security is a specific RLS technique that enables the same report query to retrieve different sets of data in accordance with the varying security privileges afforded to the respective users. This paper discusses an automated framework approach for applying identity-driven RLS controls on SAS^® Visual Analytics and our plans to implement a generic end-to-end RLS framework extended to the Teradata data warehouse.

SAS^® solutions that run in Hadoop provide you with the best tools to transform data in Hadoop. They also provide insights to help you make the right decisions for your business. It is possible to incorporate SAS products and solutions into your shared Hadoop cluster in a cooperative fashion with YARN to manage the resources. Best practices and customer examples are provided to show how to build and manage a shared cluster with SAS applications and products.

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Architects do not see a single architectural solution, such as SAS^® Grid Manager, satisfying the varied needs of users across the enterprise. Multi-tenant environments need to support data movement (such as ETL), analytic processing (such as forecasting and predictive modeling), and reporting, which can include everything from visual data discovery to standardized reporting. SAS^® users have a myriad of choices that might seem at odds with one another, such as in-database versus in-memory, or data warehouses (tightly structured schemes) versus data lakes and event stream processing. Whether fit for purpose or for potential, these choices force us as architects to modernize our thinking about the appropriateness of architecture, configuration, monitoring, and management. This paper discusses how SAS^® Grid Manager can accommodate the myriad use cases and the best practices used in large-scale, multi-tenant SAS environments.

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SAS^® Grid Manager, as well as other grid computing technologies, have a set of great capabilities that we, IT professionals, love to have in our systems. This technology increases high availability, allows parallel processing, facilitates increasing demand by scale out, and offers other features that make life better for those managing and using these environments. However, even when business users take advantage of these features, they are more concerned about the business part of the problem. Most of the time business groups hold the budgets and are key stakeholders for any SAS Grid Manager project. Therefore, it is crucial to demonstrate to business users how they will benefit from the new technologies, how the features will improve their daily operations, help them be more efficient and productive, and help them achieve better results. This paper guides you through a process to create a strong and persuasive business plan that translates the technology features from SAS Grid Manager to business benefits.

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Now that you have deployed SAS^®, what should you do to ensure it continues to meet your SAS users' performance expectations? This paper discusses how to proactively monitor your SAS infrastructure, with tools that should be used on a regular basis to keep tabs on infrastructure performance and housekeeping. Basic SAS administration concepts are discussed, with references for blogs, communities, and the new visual learning sites.

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Today's SAS^® environment has large numbers of concurrent SAS processes that have to process ever-growing data volumes. To help SAS users remain productive, SAS administrators must ensure that SAS applications have sufficient computer resources that are properly configured and monitored often. Understanding how all of the components of SAS work and how they are used by your users is the first step. The guidance offered in this paper helps SAS administrators evaluate hardware, operating system, and infrastructure options for a SAS environment that will keep their SAS applications running at optimal performance and keep their user community happy.

Companies looking for an optimal solution to run their SAS^® Analytics need a seamless way to manage their data between many different systems, including commodity Hadoop storage and the more traditional data warehouse. This presentation outlines a simple path for building a single platform that integrates SAS^®, Hadoop, and the data warehouse into a single, pre-configured solution, as well as strategies for querying data within multiple existing systems and combing the results to produce even more powerful decision-making possibilities.

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Organizations view Hadoop as a lower cost means to assemble their data in one location. Increasingly, the Hadoop environment also provides the processing horsepower to analyze the patterns contained in this data to create business value. SAS^® Grid Manager for Hadoop allows you to co-locate your SAS^® Analytics workload directly on your Hadoop cluster via an integration with the YARN and Oozie components of the Hadoop ecosystem. YARN allows Hadoop to become the operating system for your data-it is a tool that manages and mediates access to the shared pool of data, and manages and mediates the resources that are used to manipulate the pool. Learn the capabilities and benefits of SAS Grid Manager for Hadoop as well as some configuration tips. In addition, sample SAS Grid jobs are provided to illustrate different ways to access and analyze your Hadoop data with your SAS Grid jobs.

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Considering the fact that SAS^® Grid Manager is becoming more and more popular, it is important to fulfill the user's need for a successful migration to a SAS^® Grid environment. This paper focuses on key requirements and common issues for new SAS Grid users, especially if they are coming from a traditional environment. This paper describes a few common requirements like the need for a current working directory, the change of file system navigation in SAS^® Enterprise Guide^® with user-given location, getting job execution summary email, and so on. The GRIDWORK directory has been introduced in SAS Grid Manager, which is a bit different from the traditional SAS WORK location. This paper explains how you can use the GRIDWORK location in a more user-friendly way. Sometimes users experience data set size differences during grid migration. A few important reasons for data set size difference are demonstrated. We also demonstrate how to create new custom scripts as per business needs and how to incorporate them with SAS Grid Manager engine.

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We spend so much time talking about GRID environments, distributed jobs, and huge data volumes that we ignore the thousands of relatively tiny programs scheduled to run every night, which produce only a few small data sets, but make all the difference to the users who depend on them. Individually, these jobs might place a negligible load on the system, but by their sheer number they can often account for a substantial share of overall resources, sometimes even impacting the performance of the bigger, more important jobs. SAS^® programs, by their varied nature, use available resources in a varied way. Depending on whether a SAS^® procedure is CPU-, disk- or memory-intensive, chunks of a memory or CPU can sometimes remain unused for quite a while. Bigger jobs leave bigger chunks, and this is where being small and able to effectively exploit those leftovers can be a great advantage. We call our main optimization technique the Fast Lane, which is a queue configuration dedicated to jobs with consistently small SASWORK directories, that, when available, lets them use unused RAM in place of their SASWORK disk. The approach improves overall CPU saturation considerably while taking loads off the I/O subsystem, and without failure results in improved runtimes for big jobs and small jobs alike, without requiring any changes to deployed code. This paper explores the practical aspects of implementing the Fast Lane on your environment. The time-series profiling of overnight workload finds available resource gaps, identifies candidate jobs to fill those gaps, schedules and queues triggers, changes application server configuration, and monitors and controls techniques that keep everything running smoothly. And, of course, are the very real, tangible gains in performance that Fast Lane has delivered in real-world scenarios.

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At Royal Bank of Scotland, one of our key organizational design principles is to 'share everything we can share.' In essence, this promotes the cross-departmental sharing of platform services. Historically, this was never enforced on our Business Intelligence platforms like SAS^®, resulting in a diverse technology estate, which presents challenges to our platform team for maintaining software currency, software versions, and overall quality of service. Currently, we have SAS^® 8.2 and SAS^® 9.1.3 on the mainframe, SAS^® 9.2, SAS^® 9.3, and SAS^® 9.4 across our Windows and Linux servers, and SAS^® 9.1.3 and SAS^® 9.4 on PC across the bank. One of the benefits to running a multi-tenant SAS environment is removing the need to procure, install, and configure a new environment when a new department wants to use SAS. However, the process of configuring a secure multi-tenant environment, using the default tools and procedures, can still be very labor intensive. This paper explains how we analyzed the benefits of creating a shared Enterprise Business Intelligence platform in SAS alongside the risks and organizational barriers to the approach. Several considerations are presented as well as some insight into how we managed to convince our key stakeholders with the approach. We also look at the 'custom' processes and tools that RBS has implemented. Through this paper, we encourage other organizations to think about the various considerations we present to decide if sharing is right for their context to maximize the return on investment in SAS.

This session is an in-depth review of SAS^® Grid performance on IBM Hardware. This review spans our environment's growth over the last four years and includes the latest upgrade to our environment from the first maintenance release of SAS^® 9.3 to the third maintenance release of SAS^® 9.4 (and doing a hardware refresh in the process).

SAS^® Web Application Server goes down and the user is presented with an error message. The error messages in the SAS^® 9.4 middle tier are the default ones that are shipped with the underlying VMware vFabric Web Server and are seen by many users as too technical and uninformative. This paper describes an application called 'Errors' that was developed at the Royal Bank of Scotland that has been implemented across its 9.4 estate to provide a much better user experience for when things go wrong. In addition, regardless of communications, users always try to access an application if it is available. This paper goes into detail about a feature of the Errors application that RBS uses to prevent this. This feature is used to control access to the web applications during scheduled outage windows and it provides capability for IP and location-based access as well as others. This paper also documents features and capabilities that RBS would like to introduce to the application.

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