IBM® DB2® Query Management FacilityTM for z/OS® provides a zero-footprint, mobile-enabled, highly secure business analytics solution. IBM QMFTM V11.2.1 offers many significant new features and functions in keeping with the ongoing effort to broaden its usage and value to a wider set of users and business areas. In this IBM Redbooks® publication, we explore several of the new features and options that are available within this new release. This publication introduces TSO enhancements for QMF Analytics for TSO and QMF Enhanced Editor. A chapter describes how the QMF Data Service component connects to multiple mainframe data sources to accomplish the consolidation and delivery of data. This publication describes how self-service business intelligence can be achieved by using QMF Vision to enable self-service dashboards and data exploration. A chapter is dedicated to JavaScript support, demonstrating how application developers can use JavaScript to extend the capabilities of QMF. Additionally, this book describes methods to take advantage of caching for reduced CPU consumption, wider access to information, and faster performance. This publication is of interest to anyone who wants to better understand how QMF can enable in-place analytics with live enterprise data.

IBM® DB2® Query Management Facility for z/OS® provides a zero-footprint, mobile-enabled, highly secure business analytics solution. IBM QMF V11.2.1 offers many significant new features and functions in keeping with the ongoing effort to broaden its usage and value to a wider set of users and business areas. In this IBM Redbooks® publication, we explore several of the new features and options that are available within this new release. This publication introduces TSO enhancements for QMF Analytics for TSO and QMF Enhanced Editor. A chapter describes how the QMF Data Service component connects to multiple mainframe data sources to accomplish the consolidation and delivery of data. This publication describes how self-service business intelligence can be achieved by using QMF Vision to enable self-service dashboards and data exploration. A chapter is dedicated to JavaScript support, demonstrating how application developers can use JavaScript to extend the capabilities of QMF. Additionally, this book describes methods to take advantage of caching for reduced CPU consumption, wider access to information, and faster performance. This publication is of interest to anyone who wants to better understand how QMF can enable in-place analytics with live enterprise data.

There is enormous pressure today for businesses across all industries to cut costs, enhance business performance, and deliver greater value with fewer resources. To take business analytics to the next level and drive tangible improvements to the bottom line, it is important to manage not only the volume of data, but the speed with which actionable findings can be drawn from a wide variety of disparate sources. The findings must be easily communicated to those responsible for making both strategic and tactical decisions. At the same time, strained IT budgets require that the solution be self-service for everyone from DBAs to business users, and easily deployed to thin, browser-based clients. Business analytics hosted in the Query Management FacilityTM (QMFTM) on DB2® and System z® allow you to tackle these challenges in a practical way, using new features and functions that are easily deployed across the enterprise and easily consumed by business users who do not have prior IT experience. This IBM® Redbooks® publication provides step-by-step instructions on using these new features: Access to data that resides in any JDBC-compliant data source OLAP access through XMLA 150+ new analytical functions Graphical query interfaces and graphical reports Graphical, interactive dashboards Ability to integrate QMF functions with third-party applications Support for the IBM DB2 Analytics Accelerator A new QMF Classic perspective in QMF for Workstation Ability to start QMF for TSO as a DB2 for z/OS stored procedure New metadata capabilities, including ER diagrams and capability to federate data into a single virtual source

There is enormous pressure today for businesses across all industries to cut costs, enhance business performance, and deliver greater value with fewer resources. To take business analytics to the next level and drive tangible improvements to the bottom line, it is important to manage not only the volume of data, but the speed with which actionable findings can be drawn from a wide variety of disparate sources. The findings must be easily communicated to those responsible for making both strategic and tactical decisions. At the same time, strained IT budgets require that the solution be self-service for everyone from DBAs to business users, and easily deployed to thin, browser-based clients. Business analytics hosted in the Query Management FacilityTM (QMFTM) on DB2® and System z® allow you to tackle these challenges in a practical way, using new features and functions that are easily deployed across the enterprise and easily consumed by business users who do not have prior IT experience. This IBM® Redbooks® publication provides step-by-step instructions on using these new features: Access to data that resides in any JDBC-compliant data source OLAP access through XMLA 150+ new analytical functions Graphical query interfaces and graphical reports Graphical, interactive dashboards Ability to integrate QMF functions with third-party applications Support for the IBM DB2 Analytics Accelerator A new QMF Classic perspective in QMF for Workstation Ability to start QMF for TSO as a DB2 for z/OS stored procedure New metadata capabilities, including ER diagrams and capability to federate data into a single virtual source.

IBM® DB2® Version 11.1 for z/OS® (DB2 11 for z/OS or just DB2 11 throughout this book) is the fifteenth release of DB2 for IBM MVSTM. The DB2 11 environment is available either for new installations of DB2 or for migrations from DB2 10 for z/OS subsystems only. This IBM Redbooks® publication describes enhancements that are available with DB2 11 for z/OS. The contents help database administrators to understand the new extensions and performance enhancements, to plan for ways to use the key new capabilities, and to justify the investment in installing or migrating to DB2 11. Businesses are faced with a global and increasingly competitive business environment, and they need to collect and analyze ever increasing amounts of data (Figure 1). Governments also need to collect and analyze large amounts of data. The main focus of this book is to introduce recent DB2 capability that can be used to address challenges facing organizations with storing and analyzing exploding amounts of business or organizational data, while managing risk and trying to meet new regulatory and compliance requirements. This book describes recent extensions to DB2 for z/OS in V10 and V11 that can help organizations address these challenges.

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.

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