Data Warehousing Training In Bangalore

Data Warehousing Training In Bangalore

Before you plan to join one of the training institutes of data warehousing, you should know the some of the basics and the history of the data warehousing. This article will help you to do so.



Data warehousing is combining data from multiple and usually varied sources into one comprehensive and easily manipulated database. Common accessing systems of data warehousing include queries, analysis and reporting. Because data warehousing creates one database in the end, the number of sources can be anything you want it to be, provided that the system can handle the volume, of course. The final result, however, is homogeneous data, which can be more easily manipulated.



Data warehousing is commonly used by companies to analyze trends over time. In other words, companies may very well use data warehousing to view day-to-day operations, but its primary function is facilitating strategic planning resulting from long-term data overviews.
From such overviews, business models, forecasts, and other reports and projections can be made. Routinely, because the data stored in data warehouses is intended to provide more overview-like reporting, the data is read-only. If you want to update the data stored via data warehousing, you'll need to build a new query when you're done.


The beginning of the data warehousing

The concept of data warehousing dates back to the late 1980s 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.
The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments.


In larger corporations it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as legacy systems), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "data marts" that were tailored for ready access by users.



By using a data warehouse you are in effect providing a common data model for all data that is of interest without having to be concerned about the origin of the source. This ability makes it far easier to compile a report and analyze all available information than it would be if you had to retrieve information, invoices, ledgers, orders and so on from a multitude of data models.



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Data Warehousing

Data Warehousing
A data warehouse is a database used for reporting. The data is offloaded from the operational systems for reporting. The data may pass through an operational data store for additional operations before it is used in the DW for reporting. A data warehouse maintains its functions in three layers: staging, integration, and access. Staging is used to store raw data for use by developers (analysis and support). The integration layer is used to integrate data and to have a level of abstraction from users. The access layer is for getting data out for users.


Earlier
The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems, was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged.

There are two leading approaches to storing data in a data warehouse
1. Dimensional approach
2. Normalized approach.

Dimensional approach, transaction data are partitioned into either "facts", which are generally numeric transaction data, or "dimensions", which are the reference information that gives context to the facts. For example, sales transaction can be broken up into facts such as the number of products ordered and the price paid for the products and into dimensions such as order date, customer name and so on.

Normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by subject areas that reflect general data categories the normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joints.

Some benefits of Data Warehousing.

# Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.

# Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.
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