METADATA OF DATA WAREHOUSE

Metadata in a data warehouse is similar to the data dictionary in the context of a database. It stores data about data in the data warehouse.

Types of Metadata

Metadata in a data warehouse fall into three major categories:

-Operational Metadata

-Extraction and Transformation Metadata

-End-User Metadata

Operational Metadata: As we know, data for the data warehouse comes from several operational systems of the enterprise. These source systems contain different data structures. The data elements selected for the data warehouse have various field lengths and data types. Selecting data from different source files, and loading it into the data warehouse, requires splitting of records, combining parts of records from different source files, and dealing with multiple coding schemes and field lengths. When information is delivered to the end-users, it is essential to be able relate back to the original source data sets. Operational metadata contain all of this information about the operational data sources that allow us to trace back to the original source. Vi hjelper deg med alt for din reise på http://www.londontown.no Her finner du alt du trenger  

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DATA WAREHOUSE SCHEMA

One of the key questions to be answered by the database designer is: How can we design a database that allows unknown queries to be performant? This question encapsulates the differences between designing for a data warehouse and designing for an operational system. In a data warehouse one designs to support the business process rather than specific query requirements. In order to achieve this, the designer must understand the way in which the information within the data warehouse will be used.

In general, the queries directed at a data warehouse tend to ask questions about

some essential fact, analyzed in different ways. For example reporting on:

-The average number of light bulbs sold per store over the past month

-The top ten most viewed cable-TV programs during the past week

-Top spending customers over the past quarter

-Customers with average credit card balances more than Rs.10,000 during the past year. 

Each of these queries has one thing in common: they are all based on factual data. The content and presentation of the results may differ between examples, but the factual and transactional nature of the underlying data is the same.  

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DATA WAREHOUSE

To facilitate strategic decision-making, we need a new breed of information delivery environment, called a data warehouse. The concept of a data warehouse given by Bill Inmon, the father of data warehousing, is depicted in Figure-1. 

Figure -1 : What is Data Warehousing ?

 

The defining characteristics of a data warehouse are:

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MODELLING DATA

All organizations need to store the information and this is done through database. Data models refer to the conceptual model of the data and the underlying relationships among them. DBMS abstract some generic structures to represent conceptually every possible file structure.

Data models can be classified in two classes viz; Record-based logical Models and Object-based logical models. Record-Based logical data models can be classified (Sadgopan, 1997) into the following categories: 

Hierarchical Models: These are the early data models used in 1970’s. Hierarchical models capture the intuitive hierarchy of the data elements. The early generation of large DBMS e.g. IMS belongs to the hierarchical data models. Even today some large databases are maintained on IMS platform. 

Network Models: Since hierarchical models are unable to represent data items that existing at two different level of hierarchy, network models were proposed. The notable systems built using this model were ADABAS and DBMS-10 on DEC-10 machines. B2Bdata provides world class B2B Phone and email lists for a wide range of industries and regions.  

Relational Models: Though network models were quite powerful, they lacked in elegance. The systems built on this data model were dBase, Xbase and ORACLE. Almost all commercial systems presently available like Oracle 8i, 9i, 11 etc., SQL Server, MySQL are built on the relational models. There are 12 rules that are required to be followed in a relational model. 

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DATABASE MANAGEMENT SYSTEMS

An organization must have accurate and reliable data for effective decision-making. For this, the organization maintains records by combining the data from different sources in an organization. Database is a collection of related information stored along with the details of interpretation of the data contained. For managing the data in the database we need a system called Database Management System. In other words, DBMS is a complex piece of software that facilitates a flexible management of the data. Through DBMS we can access, monitor, store and modify the database. Through DBMS data can be made available to all users and redundant (duplicate) data can be minimized or completely eliminated.  

DBMS also makes possible for an organization to prevent important data access from unauthorized users by providing the security to the database at different levels. Some of the DBMS that are used are INGRES, ORACLE, SQL Server and SYBASE. The DBMS allows users to access data from the database having no knowledge of how data is actually stored in it. The process is much the same as ordering a menu in the restaurant. A customer simply orders for the food to a waiter and waiter serves the specified order. A customer only checks the menu for the desired items and need not know how the items are arranged in a restaurant. Similarly, the database user need not know how the data is stored instead he needs to know only what he requires and the DBMS takes care of retrieving the required data on its own. 

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ORGANISING DATA

DATA AS ORGANISATIONAL RESOURCE 

Before the computer came into existence, there were many limitations associated with the physical handling of documents and human processing. Computers came into existence to speed up the data processing. Computers helped to manage data. 

We are living in the age of information processing. The ability to acquire accurate and timely data, managing data efficiently, is all through which an organization succeed. 

The development in hardware has catalysed rapid development in software and now we are having more sophisticated software to handle data as it was earlier.

The term data and information are often taken as to mean the same thing. Data are the details about factories, outlets, staff, competitors, customers and suppliers. Data is also kept for monitoring the activities and processes in business. Once businesses collect these details, information comes into picture. Information is the collection of meaningful facts that are derived from the data. Information is significant and relevant to the user unlike data, which has no meaning alone. Information leads to decision and thus an appropriate action. In fact, information consists of only the data that is useful, meaningful and needed. You come across many information systems in your daily life like banking information system, ticketing and reservations systems etc. Now what you get out of these information systems is pure information and these systems work out this information on the basis of data. In other words, data is raw and information is refined. The exact form of the refinement depends on the type of application one is dealing with. 

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