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Comparing and Evaluating Data Warehousing Products & Tools

Pieter Mimno

Chicago - July 24-25, 1997

About This Seminar

Selecting data warehousing products is complex because no one vendor provides a fully integrated set of products that support all components of a data warehouse. As a result, data warehouses are typically built using products from multiple vendors. But how do you know what combination of products is right for your organization?

This seminar focuses on a detailed evaluation of data warehousing products and tools. It is designed to provide you with the objective information and practical advice you need to select the best components for your data warehouse. Categories of tools evaluated include data warehousing frameworks, data cleansing tools, data extraction and transformation tools, relational versus multidimensional target databases, managed query and reporting tools, desktop OLAP tools, high-end relational OLAP tools, multidimensional OLAP tools, data mining tools, and data visualization tools. The seminar explains the different categories of products and tools that are available, and shows you how to select a mix of products to meet your specific data warehousing requirements.

Cutting Through The Hype

This seminar cuts through vendor hype to focus on the major issues that must be addressed to select the appropriate tools for the implementation of a successful data warehouse:

  • What data warehouse architecture and components are appropriate to solve business problems, and provide high performance, scalability, and concurrency?
  • How can the Internet/Intranet be used to extend analytical processing capability to large numbers of users worldwide?
  • Can data warehousing frameworks provide an integrated solution that shares central metadata across tools from multiple vendors?
  • Should the target database for the data warehouse be supported by a conventional relational DBMS, and/or a specialized multidimensional DBMS?
  • Should source data be extracted and managed for a data warehouse using the replication facilities of a conventional RDBMS, or through the use of specialized data cleansing, extraction, and transformation tools?
  • Should data be accessed and analyzed using managed query/reporting tools, desktop OLAP tools, relational OLAP tools, multidimensional OLAP tools, data mining tools, or data visualization tools?

The seminar provides technically oriented managers with the information they need to resolve these issues. Included are comparisons of alternative data warehousing architectures, data warehousing frameworks from multiple vendors, and objective evaluations of a wide range of data warehousing tools.

Who Should Attend

  • Project leaders
  • Consultants
  • Technology planning personnel
  • Application and database designers
  • Systems analysts

Others who will be involved in the actual implementation of a Data Warehouse from either a managerial or hands-on perspective.

Benefits Of Attending

  • Discover products and tools that best meet the data warehousing requirements of your organization.
  • Gain the ability to identify the specific products that are most appropriate to support your data warehousing requirements.
  • Receive unbiased, objective information that you can use to make an informed selection of tools for every component of your data warehouse.
  • Learn how to select appropriate data warehousing components and products for the rapid development of a proof-of-concept data mart.
  • Learn to select components for a more complex data warehousing environment, supporting multiple data sources and multiple data marts.
  • Discover how to evolve the data warehouse to an enterprise data warehousing architecture, including implementation of a central data warehouse.
  • Gain the knowledge to expand the data warehouse further to include implementation of an Operational Data Store, if required.
  • Learn to select data warehousing products that provide the functionality you need in both a conventional LAN environment and an Internet/Intranet environment.

Benefits To Your Company

  • Cut through vendor claims to understand the true strengths and limitations of widely used data warehousing products and tools
  • Select the most appropriate, cost-effective products for your data warehouse
  • Learn how to select components that can support high performance, scalability, and concurrency, as the data warehouse grows in size and number of users
  • Ensure that your data warehouse meets the strategic business requirements of your organization, and that it provides a rapid return on investment (ROI)

What You Will Learn

  • How to specify components of a data warehousing architecture that meet the performance and scalability requirements of your organization.
  • How to evaluate the strengths and limitations of specific products using functionality matrices and comparisons provided in the course.
  • How to use the Internet/Intranet to dramatically reduce the cost of providing remote access to a data warehouse.
  • The pros and cons of using a relational DBMS versus a multidimensional DBMS for the target database for the data warehouse.
  • Comparison and evaluation of tools available to extract data from multiple sources, clean it up, transform it, and present the data to end users in a form that is easily understandable.
  • How to select the most appropriate mix of end-user data access and analysis tools, including managed query/reporting tools, desktop OLAP tools, relational OLAP tools, multidimensional OLAP tools, data mining tools, and data visualization tools.
  • How to integrate tools from multiple vendors using a centralized metadata repository.
  • Application of a bottom-up development methodology that enables organizations to build data warehouses rapidly, reduce development risk, and achieve a fast return on investment.

Seminar Outline

  1. Data Warehousing Architectures
    1. Conventional LAN-Based Architecture
    2. Internet/Intranet Architecture
    3. Range of Data Warehousing Configurations
    4. Components of a Data Warehousing Architecture
  2. Alternative Architectures for Data Warehousing
    1. Single Data source, Single Data Mart
    2. Multiple Data Sources, Multiple Data Marts
    3. Enterprise Data Warehouse
    4. Operational Data Store Feeding Data Warehouse
    5. Virtual Data Warehouse - No Data Movement
  3. Data Warehousing on the Web
    1. Trends in Data Warehousing on the Web
    2. Accessing Data Warehouses Via the Web
    3. Strengths and Limitations of Data Warehousing on the Web
    4. Phases in the Evolution of Data Warehousing on the Web
  4. Comparison of Data Warehousing Strategies From Major Vendors
    1. Hewlett-Packard Open Warehouse
    2. IBM Information Warehouse and Visual Warehouse
    3. Informatica PowerMart
    4. Information Builders Incremental Warehouse
    5. Informix Universal Warehouse, FastStart, MetaCube, and Web Server
    6. Microsoft Active Data Warehousing Framework
    7. NCR Scalable Data Warehouse
    8. Oracle Warehouse
    9. Platinum Technology Data Warehouse
    10. Red Brick Warehouse
    11. SAS Data Warehouse, SAS/Warehouse Administrator
    12. Software AG Open Data Warehouse
    13. Sybase Warehouse WORKS
  5. Case Studies of Data Warehousing Applications
    1. Retail Data Mining
    2. Telecommunications
    3. Hospital and Pharmaceutical Applications
    4. Manufacturing Applications
    5. Insurance Industry
    6. Data Warehousing and the Internet
  6. Evaluation of Data Quality and Data Cleansing Tools
    1. Data Cleansing Process
    2. Examples of Rule Discovery and Data Cleansing Tools
    3. Evaluation of Leading Data Cleansing Tools
  7. Evlauation of Data Replication, Extraction and Transformation Tools
    1. Data Replication Using Conventional RDBMSs
    2. Data Extraction and Transformation Tools
    3. Data Extraction and Transformation Process
    4. Central Role of Integrated Metadata Repository
    5. Management and Maintenance of Data Warehouses
    6. Evaluation of Leading Data Extraction and Transformation Tools
  8. Relational Versus Multidimensional Target Databases
    1. Strengths and Limitations of Relational Databases for Data Warehouses
    2. Strengths and Limitations of Multidimensional Databases for Data Warehouses
    3. Strengths and Limitations of Hybrid OLAP for Data Warehouses
    4. On-Line Analytical Processing (OLAP)
    5. End-User View of Data in Target Databases
    6. Physical Organization of Data in Target Databases
  9. Categories of Data Access and Analysis Tools
  10. Evaluation of Managed Query and Reporting Tools
    1. Impromptu from Cognos, Inc.
    2. Discoverer/2000 from Oracle Corporation
    3. Forest & Trees from Platinum Technology
  11. Evaluation of Desktop OLAP Tools
    1. BrioQuery from Brio Technology, Inc.
    2. BusinessObjects from Business Objects, Inc.
    3. Impromptu/PowerPlay from Cognos, Inc.
    4. IQ Objects/IQ Vision from IQ Software Corp.
    5. Web-Enabled Desktop OLAP Tools
  12. Evaluation of Enterprise Class Relational OLAP Tools
    1. DecisionSuite from Information Advantage
    2. DSS Agent from MicroStrategy, Inc.
    3. InfoBeacon from Platinum Technology
    4. MetaCube from Informix
    5. Web-Enabled Relational OLAP Tools
  13. Evaluation of Multidimensional OLAP Tools
    1. Essbase and Essbase Web Gateway from Arbor Software
    2. Gentia from Planning Sciences, Ltd.
    3. Holos from Seagate Software
    4. Pilot Decision Support Suite from Pilot Software
    5. SAS/EIS and SAS/Warehouse Administrator from SAS Institute, Inc.
    6. Web-Enabled Multidimensional OLAP Tools
  14. Data Mining Tools
    1. Types of Information Found by Data Mining Tools
    2. Categories of Data Mining Tools
    3. Representative Data Mining Tools
  15. Data Visualization Tools
    1. Types of Information Found by Data Visualization Tools
    2. Categories of Data Visualization Tools
    3. Representative Data Visualization Tools
  16. Internet/Intranet Architectures
    1. Components of an Internet/Intranet Architecture
    2. Strategy of the Primary Internet/Intranet Vendors
    3. Convergence of Data Warehousing and Internet/Intranet Technology
  17. Methodology for Implementation of Data Warehouses
  18. Data Warehousing Challenges
  19. Future Trends in Data Warehousing and the Internet
  20. References

About Your Instructor

Pieter Mimno is an independent consultant and President of Technology Insight, Inc. Mr. Mimno consults extensively on data warehousing applications, client/server computing, Internet/Intranet applications, evaluation of application packages, rapid prototyping methodologies, and object-oriented techniques. As an independent consultant, not associated with any vendor, Mr. Mimno provides objective guidance and advice on the selection of appropriate data warehousing architectures, components, and tools. His primary role is to assist organizations successfully introduce new technology to achieve strategic business objectives.

FREE Bonus Information

In addition to detailed seminar notes, attendees of this seminar will receive the following valuable information prepared by Pieter Mimno:

  • Detailed functionality matrix comparing leading desktop OLAP tools, high-end ROLAP tools, and multidimensional OLAP tools.
  • White paper describing alternative Data Warehousing Architectures, components and tools

Meeting Site and Hotel Information

Chicago - July 24-25, 1997
The Wyndham Hotel
(630) 773-4000

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Comparing and Evaluating Data Warehousing Products & Tools


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