IBM Redbooks

Chuck Ballard

Marcia Miskimen

Ute Baumbach

Holly Hayes

Lakshmi Palaniappan

Marichu Scanlon

Yong Hua Zeng

This IBM® Redbooks® publication describes the architecture and components of IBM InfoSphere® OptimTM Performance Manager Extended Edition. Intended for DBAs and those involved in systems performance, it provides information for installation, configuration, and deployment. InfoSphere Optim Performance Manager delivers a new paradigm used to monitor and manage database and database application performance issues. It describes product dashboards and reports and provides scenarios for how they can be used to identify, diagnose, prevent, and resolve database performance problems. IBM InfoSphere Optim Query Workload Tuner facilitates query and query workload analysis and provides expert recommendations for improving query and query workload performance. Use InfoSphere Optim Performance Manager to identify slow running queries, top CPU consumers, or query workloads needing performance improvements and seamlessly transfer them to InfoSphere Optim Query Workload Tuner for analysis and recommendations. This is done using query formatting annotated with relevant statistics, access plan graphical or hierarchical views, and access plan analysis. It further provides recommendations for improving query structure, statistics collection, and indexes including generated command syntax and rationale for the recommendations.

Performance Management: Using IBM InfoSphere Optim Performance Manager and Query Workload Tuner

Deepak Rangarao

Alec Beaton

Mark Ketchie

Frank Ketelaars

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

Brian Williams

Kevin Foster

Mike Spicer

Andy Frenkiel

Bugra Gedik

Michael P. Koranda

Senthil Nathan

Deepak Rajan

Roger Rea

Vitali N. Zoubov

In this IBM® Redbooks® publication, we discuss and describe the positioning, functions, capabilities, and advanced programming techniques for IBM InfoSphereTM Streams (V2), a new paradigm and key component of IBM Big Data platform. Data has traditionally been stored in files or databases, and then analyzed by queries and applications. With stream computing, analysis is performed moment by moment as the data is in motion. In fact, the data might never be stored (perhaps only the analytic results). The ability to analyze data in motion is called real-time analytic processing (RTAP). IBM InfoSphere Streams takes a fundamentally different approach to Big Data analytics and differentiates itself with its distributed runtime platform, programming model, and tools for developing and debugging analytic applications that have a high volume and variety of data types. Using in-memory techniques and analyzing record by record enables high velocity. Volume, variety and velocity are the key attributes of Big Data. The data streams that are consumable by IBM InfoSphere Streams can originate from sensors, cameras, news feeds, stock tickers, and a variety of other sources, including traditional databases. It provides an execution platform and services for applications that ingest, filter, analyze, and correlate potentially massive volumes of continuous data streams. This book is intended for professionals that require an understanding of how to process high volumes of streaming data or need information about how to implement systems to satisfy those requirements. See: http://www.redbooks.ibm.com/abstracts/sg247865.html for the IBM InfoSphere Streams (V1) release.

IBM InfoSphere Streams: Assembling Continuous Insight in the Information Revolution

Amit Gupta

Daniel M. Farrell

Carlos Mazuela

Stanislav Vohnik

In this IBM Redbooks publication we describe and demonstrate dimensional data modeling techniques and technology, specifically focused on business intelligence and data warehousing. It is to help the reader understand how to design, maintain, and use a dimensional model for data warehousing that can provide the data access and performance required for business intelligence. Business intelligence is comprised of a data warehousing infrastructure, and a query, analysis, and reporting environment. Here we focus on the data warehousing infrastructure. But only a specific element of it, the data model - which we consider the base building block of the data warehouse. Or, more precisely, the topic of data modeling and its impact on the business and business applications. The objective is not to provide a treatise on dimensional modeling techniques, but to focus at a more practical level. There is technical content for designing and maintaining such an environment, but also business content. For example, we use case studies to demonstrate how dimensional modeling can impact the business intelligence requirements for your business initiatives. In addition, we provide a detailed discussion on the query aspects of BI and data modeling. For example, we discuss query optimization and how you can determine performance of the data model prior to implementation. You need a solid base for your data warehousing infrastructure . . . . a solid data model.

Dimensional Modeling: In a Business Intelligence Environment

Chuck Ballard

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