To define the term Data Warehouse (DW) especially to software developers who are new to the industry, have tried asking them a few simple questions before getting to the classic definition in the words of Bill Inmon. Some of the questions which leads to defining a Data Warehouse are:
Q: What is Data?
A: ‘Data’ is a collection of facts which are captured as it happens.
E.g., the content present in a Survey Sheet is ‘Data’
A: ‘Data’ is a collection of facts which are captured as it happens.
E.g., the content present in a Survey Sheet is ‘Data’
Q: What is information?
A: The details that are derived by processing the ‘Data’ are called Information.
E.g., the details that are arrived from the survey data like total, average etc are called Information
A: The details that are derived by processing the ‘Data’ are called Information.
E.g., the details that are arrived from the survey data like total, average etc are called Information
Q: What is a system that collects ‘Data‘ called?
A: A computer system that collects ‘Data’ is usually called an OLTP (Online Transaction Processing System) system. This system is designed to collect data in a much more rapid way.
E.g., The survey data could be captured into a laptop using a software application,An ATM machine or a Core banking system for deposit/debit interaction…
A: A computer system that collects ‘Data’ is usually called an OLTP (Online Transaction Processing System) system. This system is designed to collect data in a much more rapid way.
E.g., The survey data could be captured into a laptop using a software application,An ATM machine or a Core banking system for deposit/debit interaction…
Q: How is ‘Information’ derived from ‘Data’?
A: The ‘Data’ is pulled out from the OLTP system and moved to a separate data store/ system and then processed to derive Information. A computer system that acts as a platform for processing the ‘Data’ to derive ‘Information’ is called a Data warehouse.
A: The ‘Data’ is pulled out from the OLTP system and moved to a separate data store/ system and then processed to derive Information. A computer system that acts as a platform for processing the ‘Data’ to derive ‘Information’ is called a Data warehouse.
The ‘Information’ gathered from DW system helps an Organization in gaining more Knowledge about their business. This gained Knowledge helps the Organization in Decision making hence the DW system which supports decision making is part of the “Decision Support System”
Q: What are the key characteristics of a Data Warehouse?
A: A DW is designed to
1. store large quantity of data across years
2. push out ‘Data’ faster from its storage to the Information processing engine
A: A DW is designed to
1. store large quantity of data across years
2. push out ‘Data’ faster from its storage to the Information processing engine
Q: Why is a Data Warehouse required?
A: The OLTP system is usually used by many people to collect (push) data from the outside world into its storage where as the DW system is usually used by few people to pull the data out from its storage. Volume of data lying inside a DW system is very much higher that that in an OLTP system. The purpose of each system is different so designing a separate OLTP and DW system to cater to their unique requirement became imperative.
A: The OLTP system is usually used by many people to collect (push) data from the outside world into its storage where as the DW system is usually used by few people to pull the data out from its storage. Volume of data lying inside a DW system is very much higher that that in an OLTP system. The purpose of each system is different so designing a separate OLTP and DW system to cater to their unique requirement became imperative.
But this segregation between OLTP and DW has happened gradually. During the initial years the DW related activities were more done on OLTP systems and it still happens before an organization or department feels the need for a DW system.
The need for a DW system is felt due to issues related to
1. Performance
2. Maintenance
3. Data Integration
1. Performance
2. Maintenance
3. Data Integration
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