Adaptive Approach for Joining and Submissive View of Data in Data Warehouse Using Etl

Data warehouses have emerged as a new business intelligence paradigm where data store and maintain in concurrent. The modifications are required in the implementation of Extract Transform Load (ETL) operations which now need to be executed in an online fashion. The adaptive approach takes two phases. The Extraction phase and the joining phase. The Extraction phase recognition of the subset of source data that should be selected. The joining phase is accountable for producing join results if the two sources are adequate. Both phases of the process are associated and its bring into being highly aggregated data.Real time based data distributed and stored in the data warehouse. Now a day process on streaming warehouses has give attention to on speeding up the Extract-Transform-Load. To improve the performance and efficiency of join operation in active data warehouse, in this paper we proposed to adaptive approach for joining a continuous stream.