Getting Started With The Ibm Smart Analytics System 9600

The IBM® Smart Analytics System 9600 is a single, end-to-end business analytics solution to accelerate data warehousing and business intelligence initiatives. It provides integrated hardware, software, and services that enable enterprise customers to quickly and cost-effectively deploy business-changing analytics across their organizations. As a workload-optimized system for business analytics, it leverages the strengths of the System z® platform to drive: Significant savings in hardware, software, operating, and people costs to deliver a complete range of data warehouse and BI capabilities Faster time to value with a reduction in the time and speed associated with deploying Business Intelligence Industry-leading scalability, reliability, availability, and security Simplified and faster access to the data on System z

The IBM® Smart Analytics System 9600 is a single, end-to-end business analytics solution to accelerate data warehousing and business intelligence initiatives. It provides integrated hardware, software, and services that enable enterprise customers to quickly and cost-effectively deploy business-changing analytics across their organizations. As a workload-optimized system for business analytics, it leverages the strengths of the System z® platform to drive: Significant savings in hardware, software, operating, and people costs to deliver a complete range of data warehouse and BI capabilities Faster time to value with a reduction in the time and speed associated with deploying Business Intelligence Industry-leading scalability, reliability, availability, and security Simplified and faster access to the data on System z.

The IBM® Smart Analytics System is a fully-integrated and scalable data warehouse solution that combines software, server, and storage resources to offer optimal business intelligence and information management performance for enterprises. This IBM Redbooks® publication introduces the architecture and components of the IBM Smart Analytics System family. We describe the installation and configuration of the IBM Smart Analytics System and show how to manage the systems effectively to deliver an enterprise class service. This book explains the importance of integrating the IBM Smart Analytics System with the existing IT environment, as well as how to leverage investments in security, monitoring, and backup infrastructure. We discuss the monitoring tools for both operating systems and DB2®. Advance configuration, performance troubleshooting, and tuning techniques are also discussed. This book is targeted at the architects and specialists who need to know the concepts and the detailed instructions for a successful Smart Analytics System implementation and operation.

As business cycles speed up, many customers gain significant competitive advantage from quicker and more accurate business decision-making by using real data. For many customers, choosing the path to co-locate their transactional and analytical workloads on System z® better leverages their existing investment in hardware, software, and skills. We created a project to address a number of best practice questions on how to manage these newer, analytical type workloads, especially when co-located with traditional transactional workloads. The goal of this IBM® Redbooks® publication is to provide technical guidance and performance trade-offs associated with resource management and potentially DB2® data-sharing in a variety of mixed transactional / data warehouse System z topologies. The term co-location used here and in the rest of the book is specifically defined as the practice of housing both transactional (OLTP) and data warehouse (analytical) workloads within the same System z configuration. We also assumed that key portions of the transactional and data warehouse databases would reside on DB2 for z/OS®. The databases may or may not reside in a DB2 data-sharing environment; we discuss those pros and cons in this book. The intended audience includes DB2 data warehouse architects and practitioners who are facing choices in resource management and system topologies in the data warehouse arena. This specifically includes Business Intelligence (BI) administrators, DB2 database administrators (DBAs) and z/OS performance administrators / systems programmers. In addition, decision makers and architects can utilize this book to assist in making platform and database topology decisions. The book is divided into four parts. Part I, "Introducing the co-location project" covers the System z value proposition and why one should consider System z as the central platform for their data warehousing / business analytics needs. Some topics are risk avoidance via data consolidation, continuous availability, simplified disaster recovery, IBM Smart Analytics Optimizer, reduced network bandwidth requirements, and the unique virtualization and resource management capabilities of System z LPAR, z/VM® and WLM. Part I also provides some of the common System z co-location topologies along with an explanation of the general pros and cons of each. This would be useful input for an architect to understand where a customer is today and where they might consider moving to. Part II, "Project environment" covers the environment, products, workloads, workload drivers, and data models implemented for this study. The environment consisted of a logically partitioned z10TM 32way, running z/VM, Linux®, and z/OS operating system instances. On those instances we ran products such as z/OS DB2 V9, IBM Cognos® Business Intelligence Version 8.4 for Linux on System z, InfoSphereTM Warehouse for System z, InfoSphere Change Data Capture, z/OS WebSphere® V7, Tivoli® Omegamon for DB2 Performance expert. Utilizing these products we created transactional (OLTP), data warehouse query, and data warehouse refresh workloads. All the workloads were based on an existing web-based transactional Bookstore workload, that's currently utilized for internal testing within the System p® and z labs. While some IBM Cognos BI and ISWz product usage and experiences information is covered in this book, we do not go into the depth typically found in IBM Redbooks publications, since there's another book focused specifically on that

Many companies have built data warehouses (DWs) and have embraced business intelligence (BI) and analytics solutions. Even as companies have accumulated huge amounts of data, however, it remains difficult to provide trusted information at the right time and in the right place. The amount of data collected and available throughout the enterprise continues to grow even as the complexity and urgency of receiving meaningful information continues to increase. Producing meaningful and trusted information when it is needed can only be achieved by having a proper information architecture in place and a powerful underlying infrastructure. The amounts of data to mine, cleanse, and integrate are becoming so large that increasingly the infrastructure is becoming the bottleneck. This results in low refresh rates of the data in the data warehouse and in not having the information available in time where it is needed. And even before information can become available in a BI dashboard or a report, many preceding steps must take place: the collection of raw data; integration of data from multiple data stores, business units or geographies; transformation of data from one format to another; cubing data into data cubes; and finally, loading changes to data in the data warehouse. Combining the complexity of the information requirements, the growing amounts of data, and multiple layers of the information architecture requires an extremely powerful infrastructure. This IBM® RedguideTM publication explains how you can use IBM System z® as the foundation for your information management architecture. The System z value proposition for information management is fueled by the traditional strengths of the IBM mainframe, the specific strengths of DB2® for z/OS®, and the broad functionality of the IBM information management software portfolio. For decades, System z has proven its ability to manage vast amounts of mission-critical data for many companies throughout the world; your data is safe on System z. The available information management functionality on System z has grown from database management systems to a full stack of solutions including solutions for content management, master data management, information integration, data warehousing, and business intelligence and analytics. The availability of Linux® on System z provides an excellent opportunity to place certain components in an easy-to-manage and scalable virtualized Linux server, while benefitting from the System z hardware strengths. DB2 on z/OS can remain the operational data store and the underlying database for the data warehouse. The next generation of System z is growing into a heterogeneous architecture with which you can take advantage of System z-managed "accelerators" running on IBM System x® or IBM Power Blades. The first of these accelerators is the IBM Smart Analytics Optimizer for DB2 for z/OS V1.1, an "all-in-one" solution in which System z, z/OS, DB2 on z/OS, an IBM BladeCenter®, and IBM storage work together to accelerate certain queries by one to two orders of magnitude. With the IBM Smart Analytics Optimizer, slices of data are periodically offloaded from DB2 on z/OS to the BladeCenter. After a query is launched against that data, it will automatically run against the data kept on the BladeCenter. The BladeCenter will process the query an order of magnitude faster than DB2 on z/OS, because all data is cached in internal memory on the BladeCenter and special compression techniques are used to keep the data footprint small and efficient. As a solid information management architecture ready for the future, System z has it all.

This IBM® Redbooks® publication is the first of two volumes explaining the approach and implementation of a new hybrid workload integrated in the existing IBM Smarter Banking® showcase, which is hosted at IBM in Montpellier, France. The volumes contain the following information. Volume 1 Assessment describes how to evaluate the requirements of a new IBM Smarter AnalyticsTM workload. It addresses the user, system resources, and data processing profiles to identify the optimal configuration using IBM methodologies, such as Fit for Purpose (F4P). Because the existing showcase is based on IBM zEnterprise®, the deployment options include IBM z/OS®, Linux on IBM System z®, IBM AIX® running on POWER7® blades within the zEnterprise BladeCenter® Extension (zBX), and Microsoft Windows 2008 Server running on Intel blades also within zBX. Volume 2 Implementation describes the steps involved in deploying the Smarter Analytics workload in the showcase. With multiple components, including IBM Cognos® 10.1 Business Intelligence, IBM Cognos TM1®, Cognos Metrics Studio, IBM SPSS®, IBM DB2® for z/OS, and many application design tools, the workload spans multiple operating environments. Application clustering, setting up performance policies using Unified Resource Manager, and simulation test execution results are included. This book is intended for an audience of professionals in an infrastructure architecture role. However, architects with a focus on business intelligence and analytics, as well as IT managers, will find value in reading this book. If you want to see this solution in action, contact your IBM representative or send an email directly to [email protected].

MQ Telemetry Transport (MQTT) is a messaging protocol that is lightweight enough to be supported by the smallest devices, yet robust enough to ensure that important messages get to their destinations every time. With MQTT devices such as smart energy meters, cars, trains, satellite receivers, and personal health care devices can communicate with each other and with other systems or applications. This IBM® Redbooks® publication introduces MQTT and takes a scenario-based approach to demonstrate its capabilities. It provides a quick guide to getting started and then shows how to grow to an enterprise scale MQTT server using IBM WebSphere® MQ Telemetry. Scenarios demonstrate how to integrate MQTT with other IBM products, including WebSphere Message Broker. This book also provides typical usage patterns and guidance on scaling a solution. The intended audience for this book ranges from new users of MQTT and telemetry to those readers who are looking for in-depth knowledge and advanced topics.

To make better informed business decisions, better serve clients, and increase operational efficiencies, you must be aware of changes to key data as they occur. In addition, you must enable the immediate delivery of this information to the people and processes that need to act upon it. This ability to sense and respond to data changes is fundamental to dynamic warehousing, master data management, and many other key initiatives. A major challenge in providing this type of environment is determining how to tie all the independent systems together and process the immense data flow requirements. IBM® InfoSphere® Change Data Capture (InfoSphere CDC) can respond to that challenge, providing programming-free data integration, and eliminating redundant data transfer, to minimize the impact on production systems. In this IBM Redbooks® publication, we show you examples of how InfoSphere CDC can be used to implement integrated systems, to keep those systems updated immediately as changes occur, and to use your existing infrastructure and scale up as your workload grows. InfoSphere CDC can also enhance your investment in other software, such as IBM DataStage® and IBM QualityStage®, IBM InfoSphere Warehouse, and IBM InfoSphere Master Data Management Server, enabling real-time and event-driven processes. Enable the integration of your critical data and make it immediately available as your business needs it.

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