IBM Redbooks

Mark Simmonds

Vasfi Gucer

Deepak Rangarao

Simon Cambridge

Neil Patterson

Frank Ketelaars

Malcolm Singh

Lakshmana Ekambaram

Stephen D Gawtry

Audrey Holloman

Darren King

Karen Medhat

Mark Moloney

Payal Patel

Tamara Tatian

Henry L Quach

IBM Cloud Pak for Data platform is a unified, integrated and collaborative data and artificial intelligence (AI) platform that helps organizations collect, organize, and analyze data and infuse AI within and across business process and applications. Its ultimate goal is to deliver deeper business insights that enable smarter business outcomes. IBM Cloud Pak for Data delivers a set of capabilities core to a data fabric. A data fabric can help organizations improve productivity and reduce complexities when accessing, managing, and understanding disparate, siloed data that is distributed across a hybrid cloud landscape. The platform offers a wide selection of IBM and third-party services that span the entire data lifecycle. Deployment options include an on-premises software version that is built on the Red Hat OpenShift Container Platform, and a fully or partially managed version that can run on the IBM Cloud, and other hyper-scalers, such as Amazon Web Services (AWS) and Microsoft Azure. This IBM Redbooks publication provides a broad understanding of the IBM Cloud Pak for Data concepts and architecture, and the services that are available in the product. In addition, several common use cases and hands-on scenarios are included that help you better understand the capabilities of this product. This publication is for IBM Cloud Pak for Data customers who seek best practices and real-world examples of how to best implement their solutions while optimizing the value of their existing and future technology, data, and skills investments.

IBM Cloud Pak for Data Version 4.5: A practical, hands-on guide with best practices, examples, use cases, and walk-throughs

Robert Rios

Simon Casey

Anil Patil

Harald Seipp

Maciej Lasota

Reginaldo Marcelo dos Santos

Rob Markovic

Stephen Bollers

Andy Lin

Casey Richardson

Ryan VanAlstine

Tim Medlin

The digital enterprise has resulted in an explosion of data, and data volumes are expected to grow in zettabyte scale in the next few years. This explosive growth is largely fueled by unstructured data, such as video, social media, photos, and text. IBM® Cloud Object Storage (previously known as Cleversafe®) provides organizations the flexibility, scalability, and simplicity required to store, manage, and access today's rapidly growing unstructured data. Cloud Object Storage (COS) provides access to your unstructured data via a self-service portal from anywhere in the world with RESTful APIs, including OpenStack Swift API and S3-compatible API, enterprise availability, and security. IBM COS is available in the following deployment models: Private on-premises object storage Dedicated object storage (single-tenant) Public object storage (multi-tenant) Hybrid object storage (a mix of on-premises, dedicated or public offerings) This IBM Redbooks® publication focuses on the IBM COS public offering, IBM COS Public Services, and hybrid solutions leveraging this offering. This book is for solution developers, architects, and IT specialists who are implementing Cloud Object Storage solutions.

Cloud Object Storage as a Service: IBM Cloud Object Storage from Theory to Practice - For developers, IT architects and IT specialists

Chuck Ballard

Alec Beaton

Mark Ketchie

Anzar Noor

Judy Parkes

Bill Shubin

Wim Van Tichelen

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.

Smarter Business: Dynamic Information with IBM InfoSphere Data Replication CDC

Mike Ebbers

Michael Schapira

Dino Tonelli

Jason Arnold

Patric Becker

Yuan-Chi Chang

Willie Favero

Shantan Kethireddy

Nin Lei

Shirley Lin

Ron Lounsbury

Susan Widing Lynch

Cristian Molaro

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

Co-locating Transactional and Data Warehouse Workloads on System z

Deepak Rangarao

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