Oops! The input is malformed! Data Warehousing Architecture by Krish Krishnan - BeyeNETWORK UK


Data Warehousing Architecture

Originally published 1 May 2008

What is architecture? Architecture is the combination of the science and the art of designing and constructing physical structures. A wider definition often includes the design of the total built environment, from the macro level of the physical structure itself to the micro level of architectural or construction details.

An information system (IS) is the system of persons, data and activities that process the data and information in any organization, including manual and automated processes. A data warehouse is a subset of the information systems, whose purpose is to collect data from disparate sources and present it as integrated solutions to customers.

Data warehousing architecture is a complex subject. It is not a simple database on a server with a data model and processes to load and query data. Rather, it is the foundational layer for the business intelligence initiatives in the organization. A data warehouse is a program that will enable multiple projects. Its architecture and the blueprint that will drive its construction are critical to the success or the failure of the program and its projects.

Data warehouse architecture can be classified into the following areas:

  • Business Architecture

  • Technology Architecture

    o Hardware Architecture
    o Software Architecture
    o Database Architecture
    o Security Architecture
    o IT Governance Architecture

  • Data Architecture

    o Data Integration Architecture
    o Data Movement Architecture
    o Metadata Architecture
    o Master Data Architecture
    o Data Governance Architecture

  • Business Intelligence Architecture

    o Data Visualization Architecture
    o Data Querying Architecture
    o Data Analysis Architecture
    o BI Governance Architecture

As we list these major components, there are several interesting approaches to integrating the major components at the micro level in a given data warehouse. This brings us to another major component for a data warehouse program – methodology. While architecture describes what we need to build and lists the components, methodology represents the delivery mechanism of how to build the data warehouse and deliver the same.

Architecture and methodology need to work together for the overall success of any data warehouse program. While on this subject, architecture and methodology describe the technology and process legs of a three-legged stool. The other leg of this stool is the people or the data warehouse team.

There are several architectures available to choose for a data warehouse implementation. How do you select the right architecture for your organization? An easy technique will be to assess the components listed above in a weighted scorecard against the architectures and select the top two as your ideal choices.


As shown in this simplified approach, the four steps to selecting and implementing architectures can be easily confined to the process of defining the needs, assessing the choices, designing a solution on one or more selections and doing an easy prototype. This approach will definitely help you in the entire program, though you might choose to implement instead of prototype.

As we conclude this article, the fundamental goal of a data warehouse architecture is to present the blueprint and a road map to build a complex data processing and integrating infrastructure, while enabling the delivery process to control the build and deploy in a selected methodology framework.

My next article will discuss the different architectures across the multiple components by subject areas.

SOURCE: Data Warehousing Architecture

  • Krish KrishnanKrish Krishnan
    Krish Krishnan is a worldwide-recognized expert in the strategy, architecture, and implementation of high-performance data warehousing solutions and big data. He is a visionary data warehouse thought leader and is ranked as one of the top data warehouse consultants in the world. As an independent analyst, Krish regularly speaks at leading industry conferences and user groups. He has written prolifically in trade publications and eBooks, contributing over 150 articles, viewpoints, and case studies on big data, business intelligence, data warehousing, data warehouse appliances, and high-performance architectures. He co-authored Building the Unstructured Data Warehouse with Bill Inmon in 2011, and Morgan Kaufmann will publish his first independent writing project, Data Warehousing in the Age of Big Data, in August 2013.

    With over 21 years of professional experience, Krish has solved complex solution architecture problems for global Fortune 1000 clients, and has designed and tuned some of the world’s largest data warehouses and business intelligence platforms. He is currently promoting the next generation of data warehousing, focusing on big data, semantic technologies, crowdsourcing, analytics, and platform engineering.

    Krish is the president of Sixth Sense Advisors Inc., a Chicago-based company providing independent analyst, management consulting, strategy and innovation advisory and technology consulting services in big data, data warehousing, and business intelligence. He serves as a technology advisor to several companies, and is actively sought after by investors to assess startup companies in data management and associated emerging technology areas. He publishes with the BeyeNETWORK.com where he leads the Data Warehouse Appliances and Architecture Expert Channel.

    Editor's Note: More articles and resources are available in Krish's BeyeNETWORK Expert Channel. Be sure to visit today!

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