With the ever changing business environment and an extremely volatile economy, the financial services and the banking industries are facing acute challenges in quickly generating proficient analytical information/reports through data warehousing, which enables businesses to make informed decisions.

In the recent past, the architectural design of data warehouses has predominantly been ETL (extract, transform and load). However, with emerging technologies taking center stage, the architectural vision of data warehouse systems has gone through a radical shift. This shift has led to a new data warehousing process, as the previous data models were not equipped to handle the exponential increase in user data. Often, businesses were also unable to meet their critical data-related business needs due to their inability to incorporate changes into their large single data repositories. In turn, that translated into a longer waiting periods for reports that were requested by business users. Eventually, organizations ended up specifically dedicating resources to "extract data,” performing additional and often redundant analytics and reporting activities outside the data warehouse itself. 

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