NextGen Big DWH: Big Data Oriented Data Warehouse Architecture for Improved Business Intelligence

Data Warehousing is highly essential for achieving Business Intelligence in an Enterprise. A traditional Data Warehouse is built in par with the Inmon’s Architecture which follows Extract, Transform and Load (ETL) strategy for data pre-processing and Online Analytical Processing (OLAP) for Analysis. With several recent trends like the Online Social Networks (OSNs), e-commerce and increasingnumber of internet users, the amount of data has risen exponentially. The Data is highly dynamic where existing Data Warehouse Architectures are unable to keep in par with large amount of data for processing. Though the ETL strategy performs fairly well, it consumes a lot of time for realtime data processing. To enhance the processing capability of large volumes of Data, several Big Data Technologies and frameworks are introduced. In this paper, a Big Data Oriented Data Warehouse Architecture is proposed where the Big Data Technologies are accommodated in the Data Warehouse Architecture in a highly logical manner with an essence of chronological arrangement of the Big Data technologies. A detailed Empirical Evaluation of the proposed architecture is conducted based on a survey involving big data expertsin order to validate the proposed Data Warehouse Architectureincorporating Big Data Technologies. Incorporation of Intelligent and Semantic agents is also achieved for customizing and making the Analysis of Enterprise Level data more efficientand in turn paving a way for improved Business Intelligence at the Enterprise Level