3 Main reasons:
1.OLTP systems require high concurrency, reliability, locking which provide good performance for short and simple OLTP queries. An OLAP query is very complex and does not require these properties. Use of OLAP query on OLTP system degrades its performance.
2.An OLAP query reads HUGE amount of data and generates the required result. The query is very complex too. Thus special primitiveshave to provided to support this kind of data access.
3.OLAP systems access historical data and not current volatile data while OLTP systems access current up-to-date data and do not need historical data.
Data warehouse is the pool of huge amount of data. The data in data ware house can be archived. And when the data is needed you can extract it from the archived files.
Hi, 1. Subject Oriented 2. Integrated 3. Nonvolatile 4. Time Variant
This is proportional to the intrinsic value of the data, need and method for accesing such data, and the need to keep data organized.
The answer will depends on where in the world the warehouse is!
To provide balance flow of the material componentsTo make adequate of materialsTo receive, issue and store scraps work in progress, finished goods and other materials as need may ariseto preserve goods against defects while in warehouse
No, Lotus Notes is not considered a data warehouse. Lotus Notes is a collaborative software platform primarily used for email and document management, while a data warehouse is a central repository of integrated data from different sources used for reporting and analysis.
There are four key characteristics which separate the data warehouse from other major operational systems:Subject Orientation: Data organized by subjectIntegration: Consistency of defining parametersNon-volatility: Stable data storage mediumTime-variance: Timeliness of data and access terms
it's data warehouse....data warehouse: it is a collection of multiple databases or it it is repository of data.data mining it is the process of extracting data from data warehouse.
Data warehouse is a house where current as well as historical data can be stored.
Data warehouse is the database on which we apply data mining.
Data marts are combined into a data warehouse cannot be built alone without considering data marts. Both has equal importance to built proper data warehouse.
Metadata is data about data that provides information such as the structure, format, and characteristics of the data stored in a data warehouse. It is used in data warehouse architecture to facilitate data integration, data governance, and data lineage. Metadata helps users understand and manage the data in the data warehouse efficiently.
A facility for storing and retrieving data; a type of server farm. Large facilities are usually duplicated in two or more separate locations to guard against data loss and communication outages.
Every data structure in the data warehouse contains the time element. Why?
Hi, There are many reasons for need Data warehouse for any company.if company want to improve their performance ,operation and want to grow with large data of them, all of the reasons are create requirement of Data warehouse for any company. By Anaya, The Cheesy Animation factory(India, Ahmedabad)
One of the biggest benefits is that you can archive your data to a data warehouse. This can keep your main "production" database smaller which can provide some performance benefits. Also you can use the data warehouse to run complex queries and data-mining without adverse effects on the performance of your "production" application.
A data warehouse architecture is similar to various relational database systems. What makes the best architecture is the organization of the warehouse itself and the data it consist of.