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What is a Data warehouse? What is the purpose of a data warehouse?

A data warehouse can define as a repository  that can be accessed to retrieve information of an organization's electronically stored data, designed to facilitate reporting and analysis.

History:

When we go to the history of data warehouse we can define the concept of data warehousing dates back to the late 1980s .The concept of data warehousing was reviled when IBM researchers Barry Devlin and Paul Murphy developed the business data warehouse. In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments.

Overview:

Data warehouse is a subject-oriented, integrated, time-variant, non-volatile collections of data used to support analytical decision making. When we further explore we can identify that data warehouses are physically separated from operational systems, even though the operational systems feed the Warehouse with source data. Anywhere Data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. We can demonstrates how the data warehouse serves as a facilitator to retrieve data from any source such as independent databases and their external interfaces as follows.

Purpose:

When we go though the data warehouse we can identify following purposes,

Multiple angles:This allows viewing data from multiple angles (dimensions). We can identify this as one of the best aspects of the Data warehouse.

Providing an adaptive and flexible source of information: Data Warehouse has the capabilities to adapt quickly to the changing requirements. So it is really easy to handle the necessary changers when it needed. That’s why we can say that it is adaptive and flexible metherod for the source of information

Establish the foundation for Decision Support: We all know that decisioning process of an organization will involve analysis, data mining, forecasting, decision modeling etc. So by having a common point that allows to provide consistent, quality data with high response time provides the core enabler for making fast and informed decisions.

All systems into one environment: We all know that in enterprise environment which may have many systems of record. The data warehouse allows to aggregate all systems into one environment.

Keeping Analysis/Reporting and Production Separate: Data ware aids to keep analysis/reporting (non-production use data) separate from production data. So this can identify as one of purpose of implementing a data warehouse

Data Consistency and Quality :It is common that organizations are riddled with different types of important systems from which their information comes. Each of these systems may carry the information in different formats and also may be having out of synch information. So by bringing the data from these disparate sources at a common place, one can effectively undertake to bring the uniformity and consistency in data

Operational database

Operational Database can simply define as a database of record, consisting of system specific reference data and event data belonging to a transaction update system which contains enterprise data which are up to date and modifiable.So this is the source of data for the data warehouse. When we further go more detail study about the optional database we can identify that operational database contains detailed data used to run the day to day operations of the business. That's why we can say that it contain data which as up to data as the name implies.

So, operational database, capturing real time data and supplying data for real time computations and other analyzing processes.

--Advantages

There are number of advantages of using an operational database. We can identify those advantages as follows,

 

• System-specific reference data and event data belonging to a transaction-update system
• Contain system control and detailed data used to run the day-to-day operations
• Data are up to date and data are modifiable
• Enterprise data

What is Data mining?

Data mining can define as a process of extracting patterns from data. So it is the drawing out of hidden predictive data from huge databases. Data mining derives its name from the similarities between searching for valuable information in a large database.

We can identify data mining is as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. Because it is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Long process of research and product development Data mining techniques are become results. Data mining has become increasingly common in both the public and private sectors. Organizations use data mining as a tool to survey customer information, reduce fraud and waste, and assist in medical research.

Data mining commonly involving tasks:

When we move for a detail study about the data mining, we can identify that data mining commonly involves four classes of tasks as follows,

Clustering :

It is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

Classification:

It is the task of generalizing known structure to apply to new data.

Regression:

This is the attempts to find a function which models the data with the least error.

Association rule learning :

Searches for relationships between variables.

Mixed strategy database design approach

This database design approach uses both the bottom-up and top-down approach intend of following any particular approach for various parts of the data model before finally combining all parts together. So the requirements are portioned according to a top-down approach and part of the schema is designed for each partition according to a bottom-up approach. Then after as the final phase all the schema parts are combined together.

Inside-out database design approach

The inside-out database design approach can define as a special case of a bottom-up approach. This approach starts with the identification of set of major entities. Then after it spreading out to consider other entities, relationships and attributes associated with those first identifies. In this approach attention is focused at a central set of concepts that are most evident and then spreading outward by considering others in the vicinity of existing ones.

Why top-down database design approach is important?

In top-down approach, it helpful to visualize the system. This visualization of the system use to represent the idea behind the particular system and this will really helpful to identify the system and understand the related system. That's why we can say that top down approach is a really good way for the representation.