Improved performance of data warehouse

Data quality is the most important variables for data warehousing. Numerous data warehouse ventures fall flat because of low quality of the data. It is trusted that the issues can be altered later and hence, a great deal of the reality of the situation will become obvious eventually spent to settle the errors. In the event that low-quality data nourished in the data warehouse, the outcome will be not precise if these data are utilized as a part of the decision making. ETL is the center procedure of data integration and normally connected with the data warehousing. ETL is the lead procedure to fetch all the data in the form of homogenous, standard environment. In the event that source data obtained from different sources is not cleansed, extracted perfectly, converted and incorporated in the best possible way then we use Query Cache method to enhance the performance of ETL processing and minimize response time. Later if data is not cleansed properly after coming out data from ETL then there is algorithm which detects errors and dirty data inside the data warehouse. By using data coalition rule which applies in data warehouse to detect errors, dirty data and faults that could be.