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    This is Santosh Kumar Gidadmani, a Business Intelligence and Data Warehouse Enthusiast passionate about blogging articles in the BI, Data warehousing, space. This is my attempt to share my experience and knowledge on Oracle BI & Data Warehousing Subjects.

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Archive for the ‘DW Definitions’ Category

Data Warehousing – Definitions

Posted by Santosh Kumar Gidadmani on January 31, 2011

Thought to put some basics of Data Warehousing Concepts before I start with more focused subjects. I did lot of research in gathering information on these topics. Let us see some important topics. I would cover all these topics in series, so check out all series to gain some basics of data warehousing, data warehouse, architecture, life cycle etc. These topics would be helpful for any data warehouse beginners.

What is Data Warehousing?

“A process of transforming data into information and making it available to users in a timely enough manner to make a difference” – Forrester Research, April 1996.

What is a Data Warehouse?

1. “A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context” – Barry Devlin.

2. “A data warehouse is a subject-oriented, integrated, time-varying, non-volatile. It is collection of data that is used primarily in organizational decision making” – Bill Inmon, Building the Data Warehouse 1996.

3. A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources – Oracle.

A fundamental concept of a data warehouse is the distinction between data and information. Data is composed of observable and recordable facts that are often found in operational or transactional systems. The Information is an integrated collection of facts and is used as the basis for decisionmaking.

What are the characteristics of Data Warehouse?

Subject-Oriented: Information is presented according to specific subjects or areas of interest, not simply as computer files.

Integrated: Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. That is, if two different source systems store conflicting data about entities, or attributes of an entity, the differences need to be resolved during the process of transforming the source data and loading it into the data warehouse.

Non-Volatile: Stable information that doesn’t change each time an operational process is executed. Information is consistent regardless of when the warehouse is accessed.

Time-Variant: Containing a history of the subject, as well as current information. Historical information is an important component of a data warehouse.

Accessible: The primary purpose of a data warehouse is to provide readily accessible information to end-users.

Process-Oriented: It is important to view data warehousing as a process for delivery of information. The maintenance of a data warehouse is ongoing and iterative in nature.

What is the Advantages of Data warehouse?

Enhances end-user access to a wide variety of data.

Business decision makers can obtain various kinds of trend reports.

Increased data consistency.

Potentially lower computing costs and increased productivity.

Providing a place to combine related data from separate sources.

Creation of a computing infrastructure that can support changes in computer systems and business structures.

Empowering end-users to perform any level of ad-hoc queries or reports without impacting the performance of the operational systems.


What are the historical developments in the areas data warehousing?

1960s — General Mills and Dartmouth College, in a joint research project, develop the terms dimensions and facts.

1970s — ACNielsen and IRI provide dimensional data marts for retail sales.

1970s — Bill Inmon begins to define and discuss the term: Data Warehouse

1983 — Teradata introduces a database management system specifically designed for decision support.

1988 — Barry Devlin and Paul Murphy publish the article An architecture for a business and information systems in IBM Systems Journal where they introduce the term "business data warehouse".

1990 — Daniel Linstedt begins work on Developing the Data Vault model and methodology for data warehouses

1990 — Red Brick Systems introduces Red Brick Warehouse, a database management system specifically for data warehousing.

1991 — Prism Solutions introduces Prism Warehouse Manager, software for developing a data warehouse.

1991 — Bill Inmon publishes the book Building the Data Warehouse.

1995 — The Data Warehousing Institute, a for-profit organization that promotes data warehousing, is founded.

1995 — Daniel Linstedt adds SEI/CMMI and Six Sigma to the Data Vault Methodology to manage projects in data warehousing.

1996 — Ralph Kimball publishes the book The Data Warehouse Toolkit.

2000 — Daniel Linstedt releases the Data Vault, enabling real time auditable Data Warehouses

Source: Wiki

– Santosh

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