| Data Warehouse |
Article Index for Data |
Website Links For Data |
Information AboutData Warehouse |
| CATEGORIES ABOUT DATA WAREHOUSE | |
| data warehousing | |
| data management | |
| information technology management | |
| business intelligence | |
|
Bill Inmon , an early and influential practitioner, has formally defined a data warehouse in the following terms;
A data warehouse might be used to find the day of the week on which a company sold the most Widget s in May 1992, or how employee sick leave the week before the winter break differed between California and New York from 2001–2005. While operational systems are optimized for simplicity and speed of modification (see OLTP ) through heavy use of Database Normalization and an Entity-relationship Model , the data warehouse is optimized for reporting and analysis ( Online Analytical Processing , or OLAP). Frequently data in data warehouses are heavily Denormalised , summarised or stored in a Dimension-based Model . This is not always required to achieve acceptable query response times, however. HISTORY Data Warehouses became a distinct type of Computer Database during the late 1980s and early 1990s. They were developed to meet a growing demand for management information and analysis that could not be met by operational systems. Operational systems were unable to meet this need for a range of reasons:
As a result, separate computer databases began to be built that were specifically designed to support management information and analysis purposes. These data warehouses were able to bring in data from a range of different data sources, such as Mainframe Computer s, Minicomputer s, as well as Personal Computer s and office automation software such as Spreadsheet , and integrate this information in a single place. This capability, coupled with user-friendly reporting tools and freedom from operational impacts, has led to a growth of this type of computer system. As technology improved (lower cost for more performance) and user requirements increased (faster data load cycle times and more features), data warehouses have evolved through several fundamental stages:
ARCHITECTURE The term data warehouse architecture is primarily used today to describe the overall structure of a Business Intelligence system. Other historical terms include Decision Support Systems (DSS), Management Information System s (MIS), and others. STORAGE In OLTP — online transaction processing systems relational database design use the discipline of Data Modeling and generally follow the Codd rules of Data Normalization in order to ensure absolute data integrity. Less complex information is broken down into its most simple structures (a table) where all of the individual atomic level elements relate to each other and satisfy the normalization rules. Codd defines 5 increasingly stringent rules of normalization and typically OLTP systems achieve a 3rd level normalization. Fully normalized OLTP database designs often result in having information from a business transaction stored in dozens to hundreds of tables. Relational database managers are efficient at managing the relationships between tables and result in very fast insert/update performance because only a little bit of data is affected in each relational transaction. OLTP databases are efficient because they are typically only dealing with the information around a single transaction. In reporting and analysis, thousands to billions of transactions may need to be reassembled imposing a huge workload on the relational database. Given enough time the software can usually return the requested results, but because of the negative performance impact on the machine and all of its hosted applications, data warehousing professionals recommend that reporting databases be physically separated from the OLTP database. In addition, data warehousing suggests that data be restructured and reformatted to facilitate query and analysis by novice users. OLTP databases are designed to provide good performance by rigidly defined applications built by programmers fluent in the constraints and conventions of the technology. Add in frequent enhancements, and too many a database is just a collection of cryptic names, seemingly unrelated and obscure structures that store data using incomprehensible coding schemes. All factors that while improving performance, complicate use by untrained people. Lastly, the data warehouse needs to support high volumes of data gathered over extended periods of time and are subject to complex queries and need to accommodate formats and definitions inherited from independently designed package and legacy systems. Designing the data warehouse data Architecture synergy is the realm of Data Warehouse Architects. The goal of a data warehouse is to bring data together from a variety of existing databases to support management and reporting needs. The generally accepted principle is that data should be stored at its most elemental level because this provides for the most useful and flexible basis for use in reporting and information analysis. However, because of different focus on specific requirements, there can be alternative methods for design and implementing data warehouses. There are two leading approaches to organizing the data in a data warehouse: the dimensional approach advocated by Ralph Kimball and the normalized approach advocated by Bill Inmon . Whilst the dimension approach is very useful in data mart design, it can result in a rats nest of long term data integration and abstraction complications when used in a data warehouse. In the "dimensional" approach, transaction data is partitioned into either a measured "facts" which are generally numeric data that captures specific values or "dimensions" which contain the reference information that gives each transaction its context. As an example, a sales transaction would be broken up into facts such as the number of products ordered, and the price paid, and dimensions such as date, customer, product, geographical location and salesperson. The main advantages of a dimensional approach is that the data warehouse is easy for business staff with limited Information Technology experience to understand and use. Also, because the data is pre-joined into the dimensional form, the data warehouse tends to operate very quickly. The main disadvantage of the dimensional approach is that it is quite difficult to add or change later if the company changes the way in which it does business. The "normalized" approach uses Database Normalization . In this method, the data in the data warehouse is stored in Third Normal Form . Tables are then grouped together by subject areas that reflect the general definition of the data (customer, product, finance, etc.) The main advantage of this approach is that it is quite straightforward to add new information into the database — the primary disadvantage of this approach is that because of the number of tables involved, it can be rather slow to produce information and reports. Furthermore, since the segregation of facts and dimensions is not explicit in this type of Data Model , it is difficult for users to join the required data elements into meaningful information without a precise understanding of the Data Structure . Subject areas are just a method of organizing information and can be defined along any lines. The traditional approach has subjects defined as the subjects or nouns within a problem space. For example, in a financial services business, you might have customers, products and contracts. An alternative approach is to organize around the business transactions, such as customer enrollment, sales and trades. ADVANTAGES There are many advantages to using a data warehouse, some of them are:
A data warehouse can be a significant enabler of commercial business applications, most notably Customer Relationship Management (CRM). CONCERNS
REFERENCES
|
|
|