ABSTRACT

Extract, transform and load (ETL) is the core process of data integration and is typically associated with data warehousing. ETL tools extract data from a chosen source, transform it into new formats according to business rules, and then load it into target data structure. Managing rules and processes for the increasing diversity of data sources and high volumes of data processed that ETL must accommodate, make management, performance and cost the primary and challenges for users. ETL is a key process to bring all the data together in a standard, homogenous environment. ETL functions reshape the relevant data from the source systems into useful information to be stored in the data warehouse. Without these functions, there would be no strategic information in the data warehouse. If source data taken from various sources is not cleanse, extracted properly, transformed and integrated in the proper way, query process which is the backbone of the data warehouse could not happened In this paper we purpose an ultimate advance approach which will increase the speed of Extract, transform and load in data ware house with the support of query cache. Because the query process is the backbone of the data warehouse It will reduce response time and improve the performance of data ware house.