Oracle DBA Guide to Data Warehousing and Star Schemas
Decision support systems have a long tradition in the world of business. Since the s companies have been using analysis methods with the aim of gaining dispositive data. The aim is to support management with the strategic direction of business processes using data-based reports, models, and prognoses. They can be difficult to distinguish from one another and have, in the areas of business practice as well as well as marketing, been categorised since the nineties under the collective term business intelligence BI. BI should generate knowledge that serves as a decision maker for the strategic direction and orientation of a company. The database of the decision support systems in the context of BI is nowadays usually provided by a central so-called data warehouse. We guide you through the basics of data warehousing, trace the reference architecture of such an information system, and suggest established providers of commercial DWH solutions as well as free open source alternatives.
A Datawarehouse is the repository of a data and it is used for Management decision support system. Datawarehouse consists of wide variety of data that has high level of business conditions at a single point in time. In single sentence, it is repository of integrated information which can be available for queries and analysis. Even, it helps to see the data on the information itself. Dimension table is a table which contain attributes of measurements stored in fact tables.
In computing , the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema is an important special case of the snowflake schema , and is more effective for handling simpler queries. The star schema gets its name from the physical model's  resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points. The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes related to fact data. Examples of fact data include sales price, sale quantity, and time, distance, speed and weight measurements. Related dimension attribute examples include product models, product colors, product sizes, geographic locations, and salesperson names.
View larger. Additional order info. This book gives the reader best practices for implementing and managing a datawarehouse on the Oracle Platform.
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What is a data warehouse?
This chapter explains how to create a logical design for a data warehousing environment and includes the following topics:. About Third Normal Form Schemas. About In-Memory Aggregation. Your organization has decided to build an enterprise data warehouse. You have defined the business requirements and agreed upon the scope of your business goals, and created a conceptual design. Now you need to translate your requirements into a system deliverable. To do so, you create the logical and physical design for the data warehouse.