Big Data implementation for Inventory warehouse systems

Inventory Warehouse Systems as the back-office application for dealing with a supplier is an essential application for supporting front office application which coping with daily customer transactions. To increase the inventory warehouse system performance, then business process as Online Transactional Processing (OLTP) should be separated from the reporting process as Online Analytical Processing (OLAP) application. Big Data as the current most alluring technology, is undoubtedly the top choice in intelligent application implementation. To understand what is “Big Data,” then we should refer to previous technology and Big Data as a metamorphosis from last OLAP technology such as Data Warehouse, DSS, EIS, BI and so on. Thus, since Big Data as a metamorphosis from previous technology then we can apply Big Data for OLAP process to create an intelligent application which supports reporting for the decision-making process. This Big Data implementation was running on one single node/computer, where Hadoop for windows x64 was installed. The proposed Big Data application was split into two sub-processes such as Big Data process and Big Data result. Big Data process reads the OLTP database and export into an input text file, and the text file was processed with MapReduce algorithm and import into output text file. Meanwhile, Big Data result shows the summarization of Map and Reduce process from output text file.