Brief Introduction Of Data Warehouse Methodology

ANSWER: Data Warehouse methodology mainly focuses on the “bottom-up approach which mainly focuses on the value of the data warehouse to the users as fastly as possible”. According to this method, a data warehouse can be said as the copying of the various transactional data, which was particularly structures or made for the “analytical querying and reporting in order to support the decision support system”.

When Did The Data Warehouse Methodology Has Been Started And Whom?

ANSWER: The idea and the concept of Data warehouse Methodology can be traced back in the late 1980s when the researcher from the International Business Machines Organization named Barry Devlin and Paul Murphy developed the "business data warehouse". In the same essence, the concept of the Data Warehousing Methodology aims to provide an agricultural model for the flow of the data from the “operational systems to the decision support environments”. The idea of the Data Warehousing Methodology was attempted to address the various problems, which is linked with this flow of the data, which mainly includes the factor of high costs. “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”.

Need Of This Data Warehouse Methodology

ANSWER: The Data Warehousing Methodology helps in combining the various data from the multiple sources in to “single database and a data model”. More combination of data into “one database so a single query engine” could be used to give in the present data in an Operational Data Storage. The Data Warehousing Methodology helps in moving the problem of the “database isolation level lock contention” in the transaction processing systems which is caused by the ability to “run large, long-running, analysis queries in transaction processing databases”. The Data Warehouse Methodology helps in maintaining the Data History even if it is not maintained in the source transaction systems. The Data Warehouse Methodology helps in integrating the various data from the different source systems which in enabling the central view across the enterprise. This helps is always valuable but is particularly when the organization has grown due to mergers. The data warehousing methodology has is also benefited in the improvement of data quality by “providing consistent codes and descriptions, flagging or even fixing bad data”. Data Methodology also helps in providing a consistent organization information’s.

Uses Of The Data Warehouse Methodology

ANSWER: The Data Warehouse Methodology helps in providing a single data model, which is common for all data of interest irrespective of the various data sources. The Data Warehousing Model is used in restructuring the data so as to make “sense to the various business users”. The Data Warehousing Model helps in restructuring the various data that helps in delivering an excellent quality performance even for the complex analytic queries without having an impact on the operational systems. The Data Warehousing methodology helps in adding value to the operational business applications and more visibly customer relationship management (CRM) systems. The Data Warehousing System makes decision-making queries easier to write. The Data warehousing Methodology helps in organizing and disambiguate the various repetitive data.

Criticism Of Data Warehouse Methodology

ANSWER: The first steps for proper Data Warehouse Methodology includes “Data Extraction and cleaning”. The quality of the data is very vital in any type of database methodology. In today’s time, there are a number of tools available for extracting the data and getting the desire and cleaning. However, that does not give the desired results. “Forgetting the quality result it is obvious that one should have the quality data. Therefore extraction and cleaning of the data to get the quality data is one of keen research area for data warehouse”.