ETL and Data ManagementDetailed Discussion of ETLThe system of ETL is generally used to merge data from numerous applications into systems, typically established and strengthened by a number of existing or other vendors held on distinct computer hardware. The distinct systems that comprise the actual data are built multiple times and maintained by a number of employees. Referring to the example of the system used for cost accounting, it is clear that this system would thus collect the flow of information from payroll, transactions and procurement. In the ETL process, the initial phase includes extracting data from the number of sources in existing systems. In numerous circumstances this refers to the actual challenging factor of the ETL process, subsequently data mining appropriately initiates the effectiveness platform to determine by what means subsequent developments could advance further. The second transformation step in the ETL process involves a chain of guidelines along with the necessary functions applied to the data after extraction from the source to develop the output data for effective loading (Wyatt, L., Caufield, B., & Pol , 2009 ). Many data sources require minimal or sometimes no data manipulation at all. The last stage of loading data to the target end is generally referred to as a data warehouse. Depending on the needs of businesses, the overall ETL process differs greatly. Some data warehouses will eventually overwrite existing data using collective information; commonly, the evaluation of the extracted data is done on a daily, week-to-week, or month-to-month basis.ETL and ELTCon...... middle of the paper ......adis, P., Karagiannis, A., Tziovara, V., Simitsis, A., & Hellas, I. (2007). Towards a benchmark for ETL workflows. Vassiliadis, P., Simitsis, A., & Skiadopoulos, S. (2002, January). On the logical modeling of ETL processes. In Advanced information systems engineering (pp. 782-786). Springer Berlin Heidelberg.Hellerstein, J.M., Stonebraker, M., & Caccia, R. (1999). Independent and open enterprise data integration. IEEE Data Eng. Bull., 22(1), 43-49.Rahm, E., & Do, H.H. (2000). Data Cleaning: Current Issues and Approaches. IEEE Data Eng. Bull., 23(4), 3-13. Wyatt, L., Caufield, B., & Pol, D. (2009). Principles for an ETL benchmark. Performance evaluation and benchmarking (pp. 183-198). Springer Berlin Heidelberg.Wang, Y. Z., & Li, H. B. (2002). Design and implementation of OLE DB based Data ETL tools. MINIMICRO-SHENYANG SYSTEMS-, 23(4), 453-455.