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Implementing tivoli data warehouse v 1.2 sg247100
1. Front cover
Implementing Tivoli
Data Warehouse 1.2
A primer for deployments of any size
and proof of concepts
Latest Version 1.2 features
including Crystal Enterprise
Warehouse enablement
pack case studies
Edson Manoel
Cristiano Colantuono
Hans-Georg Köhne
Devi Raju
Ghufran Shah
Sergio Henrique Soares Monteiro
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22. Trademarks
The following terms are trademarks of the International Business Machines Corporation in the United States,
other countries, or both:
AIX® IBM® Redbooks™
CICS® IMS™ RMF™
DataJoiner® Informix® S/390®
DB2 Universal Database™ Lotus® SP2®
DB2® MQSeries® Tivoli Enterprise Console®
Domino® MVS™ Tivoli Enterprise™
DRDA® NetView® Tivoli®
^™ NetVista™ WebSphere®
™ OS/390® z/OS®
Everyplace® RACF®
ibm.com® Redbooks (logo) ™
The following terms are trademarks of other companies:
Crystal and Crystal Enterprise are trademarks of Business Objects.
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both.
Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the
United States, other countries, or both.
Java and all Java-based trademarks and logos are trademarks or registered trademarks of Sun
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UNIX is a registered trademark of The Open Group in the United States and other countries.
Other company, product, and service names may be trademarks or service marks of others.
xx Implementing Tivoli Data Warehouse 1.2
24. administrator. He graduated in Physics at the University of Rome and
collaborated with the Italian National Institute of Nuclear Physics developing
simulation programs for high energy physics experiments.
Dr. Hans-Georg Köhne is a software architect for SerCon in Germany. He
graduated in physics at the University of Muenster developing simulation
programs for high energy physics experiments. He joined SerCon in 1996,
working in the distributed systems management area. He planned and
implemented several systems management solutions in the areas software
distribution, availability management, and business automation.
Devi Raju is a Tivoli Implementation Specialist for IBM India.
She started her career with IBM and has been with IBM for 8 years now. Devi
has 4 years of experience in Enterprise System Management. She has worked in
various large Tivoli customer projects. She is also a Tivoli Certified Consultant on
PACO products.
Ghufran Shah is an IBM Certified Deployment Professional and an IBM Certified
Instructor based in the UK with os-security.com. He holds a degree in Computer
Science, and has over 8 years of experience in Systems Development and
Enterprise Systems Management. As well as teaching Tivoli courses worldwide,
his areas of expertise include Tivoli Systems Management Architecture,
Implementation, and Training together with Provisioning and Orchestration. His
focus in now on leveraging IBM solutions to provide customers with the vision
and reality of an OnDemand environment.
Sergio Henrique Soares Monteiro is an IT Specialist in Brazil. He has over 10
years of experience in database administration and development fields. He has
worked with Oracle, DB2, Informix and SQL Server on UNIX and Windows,
including clustered servers. He currently works as a Database administrator in
the CTI’s IBM in Hortolandia, Brazil. His areas of expertise include sizing,
performance tuning, and internals of RDBMS.
Thanks to the following people for their contributions to this project:
Budi Darmawan
International Technical Support Organization, Austin Center
David Stephenson
IBM Global Services, Australia
Diana Marcattili
IBM Global Services, Italy
Georg Holzknecht
Senior Systems Consultant, T-Systems CDS GmbH, Germany
xxii Implementing Tivoli Data Warehouse 1.2
25. Jonathan Cook, Brian Jeffrey, Mike Mallo
Tivoli Data Warehouse development team, IBM Software Group, Austin
Ken Hannigan
IBM Tivoli Storage Manager development team, IBM Software Group, Tucson
Yvonne Lyon, editor
International Technical Support Organization, San Jose Center
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Preface xxiii
30. 1.1 Data warehousing basics
Data warehousing is the process of managing a data warehouse and its
components, called data marts. This management process includes all the
ongoing support needs of the refresh cycle, database maintenance, and
continual refinements to the underlying data model. In addition to that, data
warehousing can be thought of as a tool to enable and support business
intelligence.
The concept of data warehousing carries several other important terms
mentioned in the above paragraph. Such terms will be explained in the sections
to follow. They are:
Data warehouse
Data mart
Business intelligence
Data mining
1.1.1 Data warehouse
A data warehouse is the cohesive data model that defines the central data
repository for an organization. An important point is that we don't define a
warehouse in terms of the number of databases. Instead, we consider it a
complete, integrated data model of the enterprise, regardless of how or where
the data is stored.
A data warehouse is a collection of databases where data is collected for the
purpose of being analyzed. This collection of databases can be formed by one or
more databases. The defining characteristic of a data warehouse is its purpose.
Most data is collected to handle a company's on-going business. This type of
data can be called operational data. The systems used to collect operational
data are referred to as OLTP.
A data warehouse collects, organizes, and makes data available for the purpose
of analysis in order to give management the ability to access and analyze
information about its business. This type of data can be called informational
data. The systems used to work with informational data are referred to as online
analytical processing (OLAP).
Bill Inmon coined the term data warehouse in 1990. His definition is as follows:
“A (data) warehouse is a subject-oriented, integrated, time-variant, and
non-volatile collection of data in support of management's decision-making
process.”
4 Implementing Tivoli Data Warehouse 1.2
31. These are the main types of data:
Subject-oriented: Data that gives information about a particular subject
instead of about a company's on-going operations
Integrated: Data that is gathered into the data warehouse from a variety of
sources and merged into a coherent whole
Time-variant: All data in the data warehouse that is identified with a
particular time period
1.1.2 Data mart
A data mart is a repository containing data specific to a particular business group
in an enterprise. All data in a data mart derives from the data warehouse, and all
data relates directly to the enterprise wide data model. Often, data marts contain
summarized or aggregated data that the user community can easily consume.
Another way to differentiate a data warehouse from a data mart is to look at the
data's consumers and format. IT analysts and canned reporting utilities consume
warehouse data, whose storage is usually coded and cryptic. The user
community consumes data mart data, whose storage is usually in a more
readable format. For example, to reduce the need for complex queries and assist
business users who might be uncomfortable with the SQL language, data tables
could contain the de-normalized code table values.
A data mart contains a subset of corporate data that is of value to a specific
business unit, department, or set of users. This subset consists of historical,
summarized, and possibly detailed data captured from transaction processing
systems, or from an enterprise data warehouse. It is important to realize that a
data mart is defined by the functional scope of its users, and not by the size of
the data mart database. In parallel to increasing data mart usage, the underlying
databases will rapidly increase in size.
1.1.3 Business intelligence
Business intelligence (BI) is not business as usual. It is about making better
decisions more quickly and easily.
Businesses collect enormous amounts of data every day: Information about
orders, inventory, accounts payable, point-of-sale transactions, and, of course,
customers. Businesses also acquire data, such as demographics and mailing
lists, from outside sources. Unfortunately, based on a recent survey, over 93
percent of corporate data is not usable in the business decision-making process
today. This applies also to systems management, where data tends to be of
more technical nature.
Chapter 1. Introducing Tivoli Data Warehouse 1.2 5
32. Consolidating and organizing data for better business decisions can lead to a
competitive advantage, and learning to uncover and leverage those advantages
is what business intelligence is all about.
The amount of business data is increasing exponentially. In fact, it doubles every
two to three years. More information means more competition. In the age of the
information explosion, executives, managers, professionals, and workers all
need to be able to make better decisions faster. Because now, more than ever,
time is money.
Much more than a combination of data and technology, BI helps you to create
knowledge from a world of information. Get the right data, discover its power,
and share the value, BI transforms information into knowledge. Business
intelligence is the application of putting the right information into the hands of
the right user at the right time to support the decision-making process.
Business driving forces
It can be noted that there are some business driving forces behind business
intelligence, one being the need to improve ease-of-use and reduce the
resources required to implement and use new information technologies. Other
driving forces behind business intelligence include these:
The need to increase revenues, reduce costs, and compete more effectively.
Gone are the days when end users could manage and plan business
operations using monthly batch reports, and IT organizations had months to
implement new applications. Today companies need to deploy informational
applications rapidly, and provide business users with easy and fast access to
business information that reflects the rapidly changing business environment.
Business intelligence systems are focused towards end user information
access and delivery, and provide packaged business solutions in addition to
supporting the sophisticated information technologies required for the
processing of today’s business information.
The need to manage and model the complexity of today’s business
environment.
Corporate mergers and deregulation means that companies today are
providing and supporting a wider range of products and services to a broader
and more diverse audience than ever before. Understanding and managing
such a complex business environment and maximizing business investment
is becoming increasingly more difficult. Business intelligence systems provide
more than just basic query and reporting mechanisms, they also offer
sophisticated information analysis and information discovery tools that are
designed to handle and process the complex business information associated
with today’s business environment.
6 Implementing Tivoli Data Warehouse 1.2
33. The need to reduce IT costs and leverage existing corporate business
information.
The investment in IT systems today is usually a significant percentage of
corporate expenses, and there is a need not only to reduce this overhead, but
also to gain the maximum business benefits from the information managed by
IT systems. New information technologies like corporate intranets, thin-client
computing, and subscription-driven information delivery help reduce the cost
of deploying business intelligence systems to a wider user audience,
especially information consumers like executives and business managers.
Business intelligence systems also broaden the scope of the information that
can be processed to include not only operational and warehouse data, but
also information managed by office systems and corporate Web servers.
1.1.4 Data mining
Data mining is the process of extracting valid, useful, previously unknown, and
comprehensible information from data and using it to make business decisions.
The organizations of today are under tremendous pressure to compete in an
environment of tight deadlines and reduced profits. Legacy and lengthy business
processes that require data to be extracted and manipulated prior to use will no
longer be acceptable. Instead, enterprises need rapid decision support based on
the analysis and forecasting of predictive behavior. Data warehousing and data
mining techniques provide this capability.
Data mining can be defined as the extraction of hidden predictive information
from large databases, and is a powerful technology with great potential to help
companies focus on the most important information in their data warehouses.
Once a Tivoli Data Warehouse has been established, data mining tools can then
be used to predict future trends and behaviors, allowing businesses to make
proactive, knowledge-driven decisions.
Data mining tools can answer business questions that traditionally were too time
consuming to resolve. These tools hunt databases for hidden patterns, finding
predictive information that experts may miss because it lies outside their
expectations.
The art of data mining is not trivial, and it can be similar to “finding the needle in
the haystack”. In this case, the needle is that single piece of intelligence your
business needs, and the haystack is the large data warehouse you've built up
over a period of time within your business.
Chapter 1. Introducing Tivoli Data Warehouse 1.2 7
34. Most companies already collect and analyze massive quantities of data.
Data mining techniques can be implemented rapidly on existing software and
hardware platforms to enhance the value of existing information resources, and
can be integrated with new products and systems as they are brought on-line.
Given databases of sufficient size and quality, data mining technology can
generate new business opportunities by providing these capabilities:
Automated prediction of trends and behaviors: Data mining automates
the process of finding predictive information in large databases. Questions
that traditionally required extensive hands-on analysis can now be answered
directly from the data and quickly. A typical example of a predictive problem is
targeted server performance. Data mining uses data on past critical events to
identify the servers most likely to cause future critical problems. Other
predictive problems include forecasting server outage and other forms of
performance degradation that is likely to occur, given certain events.
Automated discovery of previously unknown patterns: Data mining tools
sweep through databases and identify previously hidden patterns in one step.
An example of pattern discovery is the analysis of IBM Tivoli Monitoring data
to identify seemingly unrelated events that are often received together.
1.2 Tivoli Data Warehouse
The Tivoli Data Warehouse 1.2 is built on an IBM DB2® Data Warehouse. It
offers all IBM DB2 Data Warehouse functionality with additional Tivoli specific
extensions.
The IBM Data Warehouse Management uses the IBM DB2 Universal Database
Enterprise Edition and the IBM DB2 Data Warehouse Manager feature. It
provides an integrated, distributed, heterogeneous warehouse management
infrastructure for designing, building, maintaining, governing, and accessing
highly scalable, robust data warehouses, operational data stores, and data marts
stored in IBM DB2 databases.
IBM DB2 Data Warehouse Manager helps warehouse administrators:
To manage data volumes, to move data directly from source to target (also
allowing packaged and simplified access to popular partner products such as
SAP R/3), and to control the servers on which transformations take place with
distributed warehouse agents
To speed warehouse and data mart deployment with commonly used,
pre-built data cleansing and statistical transformations
To build and manage from a central point of control, integrated in IBM DB2,
utilizing the Data Warehouse Center graphical user interface
8 Implementing Tivoli Data Warehouse 1.2
35. DB2 warehouse management consists of:
An administrative client to define and manage data warehousing tasks and
objects, and warehouse or data mart operations: the Data Warehouse Center
A manager to manage and control the flow of data: the warehouse server
Agents residing on IBM DB2 Universal Database Enterprise Edition server
platforms to perform requests from the manager or warehouse server: the
local or remote warehouse agent
A warehouse control database storing the warehouse management metadata
on a IBM DB2 database server
A metadata administrative and publishing tool with its own administration
graphical user interface (GUI): Information Catalog Manager to manage and
present both technical and business metadata
The different components of the IBM DB2 Data Warehouse Manager are shown
in Figure 1-1.
Clients Warehouse Warehouse Databases End Users
Server Agents
Data
Warehouse Data Relational
Center Message Source
Data
Message
DB2
Data Target
Message
Non-
Data Relational
Source
Message
Metadata
Data
Metadata Non-DB2
yy Target
Data
Log
Control Editions
Database Configuration
Data
Flat Files,
DB2 Web or
SAP R/3
Included with IBM DB2
Figure 1-1 IBM DB2 Data Warehouse Manager
Chapter 1. Introducing Tivoli Data Warehouse 1.2 9
36. 1.3 What is new in Tivoli Data Warehouse 1.2
Tivoli Data Warehouse 1.2 provides a number of enhancements and new
features over Version 1.1, such as these:
Improved interfaced and Web-based reporting using Crystal Enterprise™
DB2 UDB for OS/390 and z/OS support
Flexible and extended configuration support
Installation enhancements
Serviceability and scalability improvements
We now discuss each of these areas.
1.3.1 Crystal Enterprise™
Among the enhancements that Tivoli Data Warehouse 1.2 provides, an important
change is the new mechanism for producing reports and the user interface.
Version 1.1 of Tivoli Data Warehouse used Tivoli Presentation Services and the
IBM Console. Tivoli Data Warehouse 1.2 does not use the IBM Console nor
Tivoli Presentation Services. The reporting technology is now provided by
Crystal Enterprise™, by Business Objects, which is a world standard for
high-quality and high-performance reporting.
Crystal Enterprise™ provides:
Out-of-the-box Web-based reporting and information delivery for all your
Tivoli products
An extendable, scalable reporting solution to meet the information delivery
needs of your IT organization
A report scheduling capability
An export feature to export reports to variety of formats (Excel, Word, PDF)
The capability to change the look and feel of the reports
The Tivoli Data Warehouse 1.2 comes supplied with Crystal Enterprise
Professional Version 9 for Tivoli (limited use version) to analyze and deliver
out-of-the-box Reports from the Tivoli Data Warehouse into the hands of
decision-makers using a Web browser.
This will allow for a rapid return on investment you have made in your Tivoli
solution, by providing out-of-the-box Web based reporting, including scheduling
and report export capability. All of this is achieved using a customizable platform
for organizing, categorizing, and delivering information.
10 Implementing Tivoli Data Warehouse 1.2
37. If we define a report as an entity that visualizes the output of SQL clauses, or an
“SQL Pull”, then the Crystal Enterprise Professional Version 9 for Tivoli, which is
shipped with the Tivoli Data Warehouse 1.2 product, is supplied with a number of
standard reports provided by the Tivoli Data Warehouse Enablement Packs
(WEPs). When a report is made available by the WEP to Crystal Enterprise, the
layout, legends, colors, and the look-and-feel of the report can all be customized.
However, to create a new report (using the definition above), or to modify the
SQL pull criteria of an existing report, Crystal Reports and a different version of
Crystal Enterprise™ is required: Crystal Enterprise Version 9 Special Edition.
A license for Crystal Enterprise Version 9 Special Edition must be purchased
separately. The Crystal Enterprise Version 9 Special Edition will allow you to:
Extend your reporting capabilities to develop, deliver, and analyze new
reports created from your Tivoli Systems Management Data using Crystal
Reports version 9
Provide support for approximately 75 concurrent online users
Add, modify, and design new reports from your Tivoli Systems Management
Data using Crystal Reports version 9
For the tasks listed above, Crystal Reports Version 9 Special Edition is required
and must be purchased separately.
Next we present a brief introduction to the Crystal Enterprise architecture. As
shown in Figure 1-2, by using the Crystal Enterprise™ multi-tier architecture, the
IBM Tivoli product portfolio has a key partnership developed to ensure the
deepest level of integration and ongoing support for this solution. Please note
that some of the functions may not be available on the Crystal Enterprise
Professional for Tivoli product.
Chapter 1. Introducing Tivoli Data Warehouse 1.2 11
38. Browser or Crystal applications
Crystal Management Console
eProtfolio Client Tier
Crystal Configuration Manager
Publishing Wizard
Import Wizard
Web Server / Web Connector
Crystal Enterprise Framework Intelligence Tier
Web Component Server
File Repository Server Automated Process Scheduler
Cache Server Event Server
Processing Tier
Job Server
Page Server Report Application Server
Data Tier
OLAP
Relational ODBC XML, ERP, CRM, COM
Figure 1-2 Crystal Enterprise multi-tier architecture
In Crystal Enterprise, there are four tiers, each of which can be installed on one
machine, or with the Crystal Enterprise Version 9 Special Edition, spread across
many. The Crystal Enterprise architecture tiers are as follows:
Client tier: Administrators and end users interact with this component
directly, which is made up of the applications that enable people to
administer, publish, and view reports.
Intelligence tier: These components manage the Crystal Enterprise
administration system, which consists of maintaining all aspects of the
security information, storing report instances, and controlling the flow of
requests to the appropriate servers.
12 Implementing Tivoli Data Warehouse 1.2
39. Processing tier: These components access the data and generate the
reports. This is the only tier that communicates directly with the databases
that contain the report data.
Data tier: The databases that contain the data used in the reports fall into this
tier. These databases are referred as Data Sources in Crystal Enterprise, and
a wide range of databases are supported. These databases could contain
historic data and/or operational data.
This redbook does not go into the details of Crystal Enterprise Professional
Version 9 for Tivoli administration and configuration. Refer to the following
documentation shipped with the product:
Crystal Enterprise 9 Installation Guide
Crystal Enterprise 9 Administrator’s Guide
Crystal Enterprise 9 Getting Started Guide
Crystal Enterprise 9 ePortfolio User’s Guide
1.3.2 IBM DB2 UDB for OS/390 and z/OS support
On z/OS® systems, operational data is extracted from a Tivoli Decision Support
for OS/390® database.
As the next generation of historical data reporting and analysis solutions, Tivoli
Data Warehouse is a successor to Tivoli Decision Support (TDS) on distributed
platforms (Wintel/UNIX) and a companion product for Tivoli Decision Support for
OS/390. The following sections explain how Tivoli Data Warehouse relates to
and works with Tivoli Decision Support and Tivoli Decision Support for OS/390.
Tivoli Decision Support for OS/390 and Tivoli Data Warehouse
The following items compare and contrast Tivoli Data Warehouse and Tivoli
Decision Support:
Both Tivoli Data Warehouse and Tivoli Decision Support collect and analyze
data gathered by the system management products in your enterprise.
Both provide an infrastructure for reporting and analysis, but do not
themselves extract data or provide reports. Each relies on other applications
to use the infrastructure to extract and analyze data and to provide reports
that satisfy a specific reporting or analysis need.
In Tivoli Decision Support, an application that provides a solution to a specific
reporting need is called a Tivoli Decision Support Guide. In Tivoli Data
Warehouse, the corresponding application is called a Warehouse
Enablement Pack.
Chapter 1. Introducing Tivoli Data Warehouse 1.2 13
40. Some Tivoli Decision Support Guides require direct access to the data in your
operational data stores, which can decrease the performance of the products
creating and using those data stores. Tivoli Data Warehouse ensures that
your operational data stores are not impacted by users running reports. It also
ensures that users can run reports efficiently by accessing databases that are
optimized for interactive reporting.
By saving historical data in a central location and in a common format, Tivoli
Data Warehouse makes it easier to create reports that draw on data collected
by more than one product.
Tivoli Decision Support stores and accesses data using Cognos Powerplay
and Crystal Reports. In contrast, Tivoli Data Warehouse publishes the format
of its data, as well as the format of the data in the products that feed the
warehouse, allowing the use of various reporting tools.
This enables you to use the business intelligence solutions you already know.
In addition, Tivoli software uses Crystal Enterprise, which is provided with
Tivoli Data Warehouse, as a common reporting solution.
Tivoli Data Warehouse provides support for multiple languages. Tivoli
Decision Support is available only in English. Tivoli Decision Support for
OS/390 is available in English and Japanese.
TDS for OS/390 and Tivoli Data Warehouse 1.2 Interaction
This section describes how Tivoli Decision Support for OS/390 works with Tivoli
Data Warehouse 1.2 to store and aggregate data.
Tivoli Decision Support for OS/390 collects system management data from
System Management Facility (SMF), Information Management System (IMS™),
and other logs. It aggregates and summarizes data on a hourly, daily, and
monthly basis and places the data into its own database.
The Tivoli Decision Support for OS/390 database contains data primarily from
z/OS systems. Tivoli Data Warehouse 1.2 can use the Tivoli Decision Support for
OS/390 databases as an operational data source for the z/OS applications.
The flow of data between Tivoli Decision Support for OS/390 and Tivoli Data
Warehouse 1.2 can be seen in Figure 1-3.
14 Implementing Tivoli Data Warehouse 1.2