TRENDS IN DATA WAREHOUSING TECHNIQUES
Financial data volumes are increasing, and this appears to be a long-term trend, implying that data managementdevelopment will be crucial over the next few decades. Because financial data is sometimes real-time data, itis constantly generated, resulting in a massive amount of financial data produced in a short period of time.The volume, diversity, and velocity of Big Financial Data are highlighting the significant limitations oftraditional Data Warehouses (DWs). Their rigid relational model, high scalability costs, and sometimesinefficient performance pave the way for new methods and technologies. The majority of the technologiesused in background processing and storage research were previously the subject of research in their earlystages. The Apache Foundation and Google are the two most important initiatives. For dealing with largefinancial data, three techniques outperform relational databases and traditional ETL processing: NoSQL andNewSQL storage, and MapReduce processing.