IBM® InfoSphereTM Change Data Capture for z/OS® uses log-based change data capture technology to provide low impact capture and rapid delivery of changes to and from DB2® z/OS in heterogeneous environments without impacting source systems. Customers get the up-to-date information they need to make actionable, trusted business decisions while optimizing MIPS costs. Change Data Capture can also be used to synchronize data in real time between multiple data environments to support active data warehousing, live reporting, operational business intelligence, application consolidations and migrations, master data management, and to deliver data to SOA environments. This IBM RedpaperTM document describes InfoSphere Change Data Capture, how to install and configure it, and how to migrate to the latest release.

IBM® Db2® Analytics Accelerator is a workload optimized appliance add-on to IBM DB2® for IBM z/OS® that enables the integration of analytic insights into operational processes to drive business critical analytics and exceptional business value. Together, the Db2 Analytics Accelerator and DB2 for z/OS form an integrated hybrid environment that can run transaction processing, complex analytical, and reporting workloads concurrently and efficiently. With IBM DB2 Analytics Accelerator for z/OS V7, the following flexible deployment options are introduced: Accelerator on IBM Integrated Analytics System (IIAS): Deployment on pre-configured hardware and software Accelerator on IBM Z®: Deployment within an IBM Secure Service Container LPAR For using the accelerator for business-critical environments, the need arose to integrate the accelerator into High Availability (HA) architectures and Disaster Recovery (DR) processes. This IBM RedpaperTM publication focuses on different integration aspects of both deployment options of the IBM Db2 Analytics Accelerator into HA and DR environments. It also shares best practices to provide wanted Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO). HA systems often are a requirement in business-critical environments and can be implemented by redundant, independent components. A failure of one of these components is detected automatically and their tasks are taken over by another component. Depending on business requirements, a system can be implemented in a way that users do not notice outages (continuous availability), or in a major disaster, users notice an outage and systems resume services after a defined period, potentially with loss of data from previous work. IBM Z was strong for decades regarding HA and DR. By design, storage and operating systems are implemented in a way to support enhanced availability requirements. IBM Parallel Sysplex® and IBM Globally Dispersed Parallel Sysplex (IBM GDPS®) offer a unique architecture to support various degrees of automated failover and availability concepts. This IBM Redpaper publication shows how IBM Db2 Analytics Accelerator V7 can easily integrate into or complement existing IBM Z topologies for HA and DR. If you are using IBM Db2 Analytics Accelerator V5.1 or lower, see IBM Db2 Analytics Accelerator: High Availability and Disaster Recovery, REDP-5104.

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.