Why dw is subject oriented

For example, "Retrieve the current order for this customer. Data warehouses usually store many months or years of data. This is to support historical analysis. OLTP systems usually store data from only a few weeks or months.

The OLTP system stores only historical data as needed to successfully meet the requirements of the current transaction. Data warehouses and their architectures vary depending upon the specifics of an organization's situation. Three common architectures are:. Figure shows a simple architecture for a data warehouse. End users directly access data derived from several source systems through the data warehouse. In Figure , the metadata and raw data of a traditional OLTP system is present, as is an additional type of data, summary data.

Summaries are very valuable in data warehouses because they pre-compute long operations in advance. For example, a typical data warehouse query is to retrieve something like August sales. A summary in Oracle is called a materialized view. In Figure , you need to clean and process your operational data before putting it into the warehouse.

You can do this programmatically, although most data warehouses use a staging area instead. A staging area simplifies building summaries and general warehouse management.

Figure illustrates this typical architecture. Although the architecture in Figure is quite common, you may want to customize your warehouse's architecture for different groups within your organization. You can do this by adding data marts , which are systems designed for a particular line of business.

Figure illustrates an example where purchasing, sales, and inventories are separated. In this example, a financial analyst might want to analyze historical data for purchases and sales.

Data marts are an important part of many warehouses, but they are not the focus of this book. Data Mart Suites documentation for further information regarding data marts.

One of the primary objects of data warehousing is to provide information to businesses to make strategic decisions. Query tools allow users to interact with the data warehouse system. Reporting tools can be further divided into production reporting tools and desktop report writer. This kind of access tools helps end users to resolve snags in database and SQL and database structure by inserting meta-layer between users and database.

Sometimes built-in graphical and analytical tools do not satisfy the analytical needs of an organization. In such cases, custom reports are developed using Application development tools. Data mining is a process of discovering meaningful new correlation, pattens, and trends by mining large amount data. Data mining tools are used to make this process automatic.

These tools are based on concepts of a multidimensional database. It allows users to analyse the data using elaborate and complex multidimensional views.

Data warehouse Bus determines the flow of data in your warehouse. The data flow in a data warehouse can be categorized as Inflow, Upflow, Downflow, Outflow and Meta flow.

While designing a Data Bus, one needs to consider the shared dimensions, facts across data marts. A data mart is an access layer which is used to get data out to the users. It is presented as an option for large size data warehouse as it takes less time and money to build. However, there is no standard definition of a data mart is differing from person to person. In a simple word Data mart is a subsidiary of a data warehouse.

The data mart is used for partition of data which is created for the specific group of users. Data marts could be created in the same database as the Datawarehouse or a physically separate Database. Skip to content. Data Warehouse Concepts The basic concept of a Data Warehouse is to facilitate a single version of truth for a company for decision making and forecasting.

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Non-volatile : Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered. Ralph Kimball provided a more concise definition of a data warehouse: A data warehouse is a copy of transaction data specifically structured for query and analysis.

The most popular definition came from Bill Inmon, who provided the following: A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process. This is a functional view of a data warehouse. Kimball did not address how the data warehouse is built like Inmon did; rather he focused on the functionality of a data warehouse.