Hybrid big data warehouse for on-demand decision needs

Every day trillions of data are generated across the world and put the information systems facing the emergence of big data phenomenon. This vertiginous evolution makes the enterprise confronting the challenge to build its own big data. To achieve the challenge, the enterprise is supposed to embark on big investments in terms of resource and material to process petabytes of diverse data, this last are sometimes useful and sometimes useless. The problem here is how to optimize data relevancy to extract value from the big data sources. From this Reasons, we propose in this paper an ETL and MapReduce Hybrid Approach based on Data Filtering and Processing to build an effective on-demand Dimensional Big Data, enabling enterprises to process relevant data in efficient and effective way according to the stakeholder's needs.