This project is delivered as part of my Masters in Big Data Science (MSc BDS) Program for the module named “Data Mining” in Queen Mary University of London (QMUL), London, United Kingdom. This project covers the Implementation of the Data Warehousing and On-line Analytical Processing (OLAP) concepts of data cubes, data cube measures, OLAP operations and data cube computations using the International Bank for Reconstruction and Development (IBRD) Balance Sheet private dataset. The implementation is executed using Python and its various packages namely cubes and sqlalchemy. The solution includes indexing the OLAP data using bitmap indices, creation of base tables, creation of bitmap index tables, creation of the data cube model in the JSON file format with aggregate functions, creation of the data cube, and computation of the results for the various aggregate measures as defined in the data cube. **NOTE:** Due to the data privacy and the data protection policy to be adhered by the students; the datasets and the solution related code are not exposed and updated in the GitHub public profile; in order to be compliant with the Queen Mary University of London (QMUL) policies.
