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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Data warehousing Training Institutes in Bangalore

Data warehousing Training Institutes in Bangalore

The Data Warehousing Institutes provides education, training, certification, news, and research for executives and IT professionals across the world. These training institutes are the premier educational institute for business intelligence and data warehousing.

 

A data warehouse (DW) 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 (ODS) 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. A principle in data warehousing is that there is a place for each needed function in the DW. The functions are in the DW to meet the users' reporting needs. 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.

 

This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, catalogued and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.

 

Data warehousing arises in an organization's need for reliable, consolidated, unique and integrated analysis and reporting of its data at different levels of aggregation.

 

The practical reality of most organizations is that their data infrastructure is made up by a collection of heterogeneous systems.

For example, an organization might have one system that handles customer-relationship, a system that handles employees, systems that handle sales data or production data, yet another system for finance and budgeting data, etc. In practice, these systems are often poorly or not at all integrated and simple questions like: "How much time did sales person A spend on customer C, how much did we sell to Customer C, was customer C happy with the provided service, did Customer C pay his bills?" can be very hard to answer, even though the information is available "somewhere" in the different data systems.

 

Another problem is that enterprise resource planning (ERP) systems are designed to support relevant operations. For example, a finance system might keep track of every single stamp bought; When it was ordered, when it was delivered, when it was paid and the system might offer accounting principles (like double entry bookkeeping) that further complicates the data model. Such information is great for the person in charge of buying "stamps" or the accountant trying to sort out an irregularity, but the CEO is definitely not interested in such detailed information, the CEO wants to know stuff like "What's the cost?", "What's the revenue?", "Did our latest initiative reduce costs?" and wants to have this information at an aggregated level.

 

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