Regarding online transaction processing (OLTP) workloads, IBM® z SystemsTM platform, with IBM DB2®, data sharing, Workload Manager (WLM), geoplex, and other high-end features, is the widely acknowledged leader. Most customers now integrate business analytics with OLTP by running, for example, scoring functions from transactional context for real-time analytics or by applying machine-learning algorithms on enterprise data that is kept on the mainframe. As a result, IBM adds investment so clients can keep the complete lifecycle for data analysis, modeling, and scoring on z Systems control in a cost-efficient way, keeping the qualities of services in availability, security, reliability that z Systems solutions offer. Because of the changed architecture and tighter integration, IBM has shown, in a customer proof-of-concept, that a particular client was able to achieve an orders-of-magnitude improvement in performance, allowing that client's data scientist to investigate the data in a more interactive process. Open technologies, such as Predictive Model Markup Language (PMML) can help customers update single components instead of being forced to replace everything at once. As a result, you have the possibility to combine your preferred tool for model generation (such as SAS Enterprise Miner or IBM SPSS® Modeler) with a different technology for model scoring (such as Zementis, a company focused on PMML scoring). IBM SPSS Modeler is a leading data mining workbench that can apply various algorithms in data preparation, cleansing, statistics, visualization, machine learning, and predictive analytics. It has over 20 years of experience and continued development, and is integrated with z Systems. With IBM DB2 Analytics Accelerator 5.1 and SPSS Modeler 17.1, the possibility exists to do the complete predictive model creation including data transformation within DB2 Analytics Accelerator. So, instead of moving the data to a distributed environment, algorithms can be pushed to the data, using cost-efficient DB2 Accelerator for the required resource-intensive operations. This IBM Redbooks® publication explains the overall z Systems architecture, how the components can be installed and customized, how the new IBM DB2 Analytics Accelerator loader can help efficient data loading for z Systems data and external data, how in-database transformation, in-database modeling, and in-transactional real-time scoring can be used, and what other related technologies are available. This book is intended for technical specialists and architects, and data scientists who want to use the technology on the z Systems platform. Most of the technologies described in this book require IBM DB2 for z/OS®. For acceleration of the data investigation, data transformation, and data modeling process, DB2 Analytics Accelerator is required. Most value can be achieved if most of the data already resides on z Systems platforms, although adding external data (like from social sources) poses no problem at all.

In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM® zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, and demonstrates how to leverage certain capabilities and components to enable AI solutions in business-critical use cases, such as fraud detection and credit risk scoring, on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16TM. This publication describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS® V2R5 environment on IBM z16 hardware, and integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS®) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson® Machine Learning for z/OS. The benefits that are derived from the solutions also are described in detail, including how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and enables easy integration to deploy on IBM zSystems for optimal inferencing. Thus, allowing enterprises to uncover insights at the transaction-level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak® for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM CICS, Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC).

There has been a considerable focus on performance improvements as one of the main themes in recent IBM DB2® releases, and DB2 12 for IBM z/OS® is certainly no exception. With the high-value data retained on DB2 for z/OS and the z Systems platform, customers are increasingly attempting to extract value from that data for competitive advantage. Although customers have historically moved data off platform to gain insight, the landscape has changed significantly and allowed z Systems to again converge operational systems with analytics for real-time insight. Business-critical analytics is now requiring the same levels of service as expected for operational systems, and real-time or near real-time currency of data is expected. Hence the resurgence of z Systems. As a precursor to this shift, IDAA brought the data warehouse back to DB2 for z/OS and, with its tight integration with DB2, significantly reduces data latency as compared to the ETL processing that is involved with moving data to a stand-alone data warehouse environment. That change has opened up new opportunities for operational systems to extend the breadth of analytics processing without affecting the mission-critical system and integrating near real-time analytics within that system, all while maintaining the same z Systems qualities of service. Apache Spark on z/OS and Linux for System z also allow analytics in-place, in real-time or near real-time. Enabling Spark natively on z Systems reduces the security risk of multiple copies of the Enterprise data, while providing an application developer-friendly platform for faster insight in a simplified and more secure analytics framework. How is all of this relevant to DB2 for z/OS? Given that z Systems is proving again to be the core Enterprise Hybrid Transactional/Analytical Processing (HTAP) system, it is critical that DB2 for z/OS can handle its traditional transactional applications and address the requirements for analytics processing that might not be candidates for these rapidly evolving targeted analytics systems. And not only are there opportunities for DB2 for z/OS to play an increasing role in analytics, the complexity of the transactional systems is increasing. Analytics is being integrated within the scope of those transactions. DB2 12 for z/OS has targeted performance to increase the success of new application deployments and integration of analytics to ensure that we keep pace with the rapid evolution of IDAA and Spark as equal partners in HTAP systems. This paper describes the enhancements delivered specifically by the query processing engine of DB2. This engine is generally called the optimizer or the Relational Data Services (RDS) components, which encompasses the query transformation, access path selection, run time, and parallelism. DB2 12 for z/OS also delivers improvements targeted at OLTP applications, which are the realm of the Data Manager, Index Manager, and Buffer Manager components (to name a few), and are not identified here. Although the performance measurement focus is based on reducing CPU, improvement in elapsed time is likely to be similarly achieved as CPU is reduced and performance constraints alleviated. However, elapsed time improvements can be achieved with parallelism, and DB2 12 does increase the percentage offload for parallel child tasks, which can further reduce chargeable CPU for analytics workloads.

IBM Common Data Provider for z Systems collects, filters, and formats IT operational data in near real-time and provides that data to target analytics solutions. IBM Common Data Provider for z Systems enables authorized IT operations teams using a single web-based interface to specify the IT operational data to be gathered and how it needs to be handled. This data is provided to both on- and off-platform analytic solutions, in a consistent, consumable format for analysis. This Redpaper discusses the value of IBM Common Data Provider for z Systems, provides a high-level reference architecture for IBM Common Data Provider for z Systems, and introduces key components of the architecture. It shows how IBM Common Data Provider for z Systems provides operational data to various analytic solutions. The publication provides high-level integration guidance, preferred practices, tips on planning for IBM Common Data Provider for z Systems, and example integration scenarios.

The IBM® DB2® Analytics Accelerator Version 3.1 for IBM z/OS® (simply called Accelerator in this book) is a union of the IBM System z® quality of service and IBM Netezza® technology to accelerate complex queries in a DB2 for z/OS highly secure and available environment. Superior performance and scalability with rapid appliance deployment provide an ideal solution for complex analysis. In this IBM Redbooks® publication, we provide technical decision-makers with a broad understanding of the benefits of Version 3.1 of the Accelerator's major new functions. We describe their installation and the advantages to existing analytical processes as measured in our test environment. We also describe the IBM zEnterprise® Analytics System 9700, a hybrid System z solution offering that is surrounded by a complete set of optional packs to enable customers to custom tailor the system to their unique needs..

Analytics is increasingly an integral part of day-to-day operations at today's leading businesses, and transformation is also occurring through huge growth in mobile and digital channels. Enterprise organizations are attempting to leverage analytics in new ways and transition existing analytics capabilities to respond with more flexibility while making the most efficient use of highly valuable data science skills. The recent growth and adoption of Apache Spark as an analytics framework and platform is very timely and helps meet these challenging demands. The Apache Spark environment on IBM z/OS® and Linux on IBM z SystemsTM platforms allows this analytics framework to run on the same enterprise platform as the originating sources of data and transactions that feed it. If most of the data that will be used for Apache Spark analytics, or the most sensitive or quickly changing data is originating on z/OS, then an Apache Spark z/OS based environment will be the optimal choice for performance, security, and governance. This IBM® RedpaperTM publication explores the enterprise analytics market, use of Apache Spark on IBM z SystemsTM platforms, integration between Apache Spark and other enterprise data sources, and case studies and examples of what can be achieved with Apache Spark in enterprise environments. It is of interest to data scientists, data engineers, enterprise architects, or anybody looking to better understand how to combine an analytics framework and platform on enterprise systems.

Digital business has been driving the transformation of underlying IT infrastructure to be more efficient, secure, adaptive, and integrated. Information Technology (IT) must be able to handle the explosive growth of mobile clients and employees. IT also must be able to use enormous amounts of data to provide deep and real-time insights to help achieve the greatest business impact. This IBM® Redbooks® publication addresses the IBM Mainframe, the IBM z13TM. The IBM z13 is the trusted enterprise platform for integrating data, transactions, and insight. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It needs to be an integrated infrastructure that can support new applications. It needs to have integrated capabilities that can provide new mobile capabilities with real-time analytics delivered by a secure cloud infrastructure. IBM z13 is designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows the z13 to deliver a record level of capacity over the prior IBM z SystemsTM. In its maximum configuration, z13 is powered by up to 141 client characterizable microprocessors (cores) running at 5 GHz. This configuration can run more than 110,000 millions of instructions per second (MIPS) and up to 10 TB of client memory. The IBM z13 Model NE1 is estimated to provide up to 40% more total system capacity than the IBM zEnterprise® EC12 (zEC1) Model HA1. This book provides information about the IBM z13 and its functions, features, and associated software support. Greater detail is offered in areas relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM z Systems functions and plan for their usage. It is not intended as an introduction to mainframes. Readers are expected to be generally familiar with existing IBM z Systems technology and terminology.

The term big data refers to extremely large sets of data that are analyzed to reveal insights, such as patterns, trends, and associations. The algorithms that analyze this data to provide these insights must extract value from a wide range of data sources, including business data and live, streaming, social media data. However, the real value of these insights comes from their timeliness. Rapid delivery of insights enables anyone (not only data scientists) to make effective decisions, applying deep intelligence to every enterprise application. Apache Spark is an integrated analytics framework and runtime to accelerate and simplify algorithm development, depoyment, and realization of business insight from analytics. Apache Spark on IBM® z/OS® puts the open source engine, augmented with unique differentiated features, built specifically for data science, where big data resides. This IBM Redbooks® publication describes the installation and configuration of IBM z/OS Platform for Apache Spark for field teams and clients. Additionally, it includes examples of business analytics scenarios.

Transforming data from operational data models to purpose-oriented data structures has been commonplace for the last decades. Data transformations are heavily used in all types of industries to provide information to various users at different levels. Depending on individual needs, the transformed data is stored in various different systems. Sending operational data to other systems for further processing is then required, and introduces much complexity to an existing information technology (IT) infrastructure. Although maintenance of additional hardware and software is one component, potential inconsistencies and individually managed refresh cycles are others. For decades, there was no simple and efficient way to perform data transformations on the source system of operational data. With IBM® DB2® Analytics Accelerator, DB2 for z/OS is now in a unique position to complete these transformations in an efficient and well-performing way. DB2 for z/OS completes these while connecting to the same platform as for operational transactions, helping you to minimize your efforts to manage existing IT infrastructure. Real-time analytics on incoming operational transactions is another demand. Creating a comprehensive scoring model to detect specific patterns inside your data can easily require multiple iterations and multiple hours to complete. By enabling a first set of analytical functionality in DB2 Analytics Accelerator, those dedicated mining algorithms can now be run on an accelerator to efficiently perform these modeling tasks. Given the speed of query processing on an accelerator, these modeling tasks can now be performed much quicker compared to traditional relational database management systems. This speed enables you to keep your scoring algorithms more up-to-date, and ultimately adapt more quickly to constantly changing customer behaviors. This IBM Redbooks® publication describes the new table type that is introduced with DB2 Analytics Accelerator V4.1 PTF5 that enables more efficient data transformations. These tables are called accelerator-only tables, and can exist on an accelerator only. The tables benefit from the accelerator performance characteristics, while maintaining access through existing DB2 for z/OS application programming interfaces (APIs). Additionally, we describe the newly introduced analytical capabilities with DB2 Analytics Accelerator V5.1, putting you in the position to efficiently perform data modeling for online analytical requirements in your DB2 for z/OS environment. This book is intended for technical decision-makers who want to get a broad understanding about the analytical capabilities and accelerator-only tables of DB2 Analytics Accelerator. In addition, you learn about how these capabilities can be used to accelerate in-database transformations and in-database analytics in various environments and scenarios, including the following scenarios: Multi-step processing and reporting in IBM DB2 Query Management FacilityTM, IBM Campaign, or Microstrategy environments In-database transformations using IBM InfoSphere® DataStage® Ad hoc data analysis for data scientists In-database analytics using IBM SPSS® Modeler

This practical guide to artificial intelligence and its impact on industry dispels common myths and calls for cross-sector, collaborative leadership for the responsible design and embedding of AI in the daily work of businesses and oversight by boards. Artificial intelligence has arrived, and it's coming to a business near you. The disruptive impact of AI on the global economy—from health care to energy, financial services to agriculture, and defense to media—is enormous. Technology literacy is a must for traditional businesses, their boards, policy makers, and governance professionals. This is the first book to explain where AI comes from, why it has emerged as one of the most powerful forces in mergers and acquisitions and research and development, and what companies need to do to implement it successfully. It equips business leaders with a practical roadmap for competing and even thriving in the face of the coming AI revolution. The authors analyze competitive trends, provide industry and governance examples, and explain interactions between AI and other digital technologies, such as blockchain, cybersecurity, and the Internet of Things. At the same time, AI experts will learn how their research and products can increase the competitiveness of their businesses, and corporate boards will come away with a thorough knowledge of the AI governance, ethics, and risk questions to ask.