Data warehouse

The data warehouse is a central respiratory medium for all the essential parts of the data that an enterprise’s various business systems collect. Data warehouse, Data collected from multiple online transaction processing applications and other sources are extracted and organized selectively.

A data warehousing system collects methods, techniques, and tools that support the so-called knowledge worker (one who works primarily with information or develops and uses knowledge in the workplace: for instance, a corporate manager or a data analyst) in decision making by transforming data into information.

Some of the essential features of data warehousing are collected and listed as follows:

1.The collected data is denormalized to simplify the process and to improve the performance.

2.The data is loaded and controlled.

3.In the data warehouse usually uses enormous amounts of historical data.

At the core of this process, the data warehouse is a repository that responds to the above requirements. According to the classic definition by Bill Inmon, a data warehouse is a collection of data that exhibits the following characteristics:

1. Subject-oriented means that all the data items related to the same business object are connected.
2. Time-variant, which means that the history of business gets tracked and recorded to enable temporal reports.
3. Nonvolatile, which means that data are read-only and never updated or deleted.
4. Integrated, which means that data from different enterprise applications are collected and made consistent.

ACCESSING THE DATA WAREHOUSE

This section discusses how users can exploit information stored in the data warehouse for decision making. In the

following subsection, after introducing the particular features of the multidimensional model, we will survey the two main approaches for analyzing information:

reporting and OLAP.

Reporting

Reporting orients to users who need to access information structured fixedly periodically. For instance, a hospital must send monthly reports of the costs of patient stays to a regional office. These reports always have the same form, so the designer can write the query that generates the information and “freeze” it within an application to execute the users’ needs. A report is associated with a question and a presentation. The query typically entails selecting and aggregating

Multidimensional data stored in one or more facts. The presentation can be tabular or graphical (a diagram, a histogram, a cake, etc.). Most reporting tools also allow for automatically distributing periodic reports to

interested users by email on a subscription basis or posting reports in the corporate internet server for downloading.

Data Source Analysis

More literature about schema integration has been accumulating over the past twenty years. Methodologies like integration have been proposed, together with formalisms to code the relevant information. The integration tools improved so far should still be included in research prototypes more than industrial tools.

Significant Differences between the data warehouse and the Operational System  Operational System

• The operational system is high volume as well as detailed.
• It has a frequency that is highly updated.
• An operational system is record-oriented and optimized for data performance
• It only uses the Current data
• Internal data of one application

Data Warehouse

• The data warehouse has a Medium volume, and we’ll summarize.
• Data warehouse contains the frequency which is Low updated
• It is well optimized for questions, doubts and also accessible for analysis.
• Uses Present and past data
• It also used for several applications like OLAP, OSS

Implementation of a Data Warehouse

Some providers like IBM and oracle generally implement data Warehouse Systems. But:

• The shelf products from the warehouse are not sold.
• Available products often only support part of the functionality of a warehouse (middleware for information transport, database)
• Implementation of a vital data warehouse is a more significant project with several risk factors.
• Data Warehouses want neat and good maintenance to be usable further.

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