![]() At this stage, firms might choose to employ data quality rules on the data itself, as well as to measure the data’s suitability for specific regulations such as BCBS 239. It’s a vitally important step to help reinforce data integrity. The T ransform phase involves converting the data into the desired format, which may involve cleansing, filtering, or aggregation.Tools for ETL aid in automating the extraction process and saving the considerable time (not to mention risk of human error) in performing the task manually. Additionally, many organisations are constantly undergoing digital transformation, moving away from legacy systems to newer storage options, meaning that there is no constant ‘perfect’ state of data storage for any enterprise. This is usually because source systems, lakes and warehouses are not designed to perform analytics or computational analysis in situ. ![]() Pretty much any repository or system that an organisation uses to store data will need to have that data extracted as part of an ETL process. The Extract phase involves accessing and retrieving the raw data from the source system, such as a data silo, warehouse or data lake.Without ETL it would be impossible to programmatically analyze heterogeneous data and derive business intelligence from it.As its name suggests, ETL is made up of three distinct phases: ETL takes data that is heterogeneous and makes it homogeneous. It would be great if data from all these sources had a compatible schema from the outset, but this is rarely the case. When creating a data warehouse, it is common for data from disparate sources to be brought together in one place so that it can be analyzed for patterns and insights. ![]() ![]() Once loaded, the ETL process is complete, although in many organizations ETL is performed regularly in order to keep the data warehouse updated with the latest data.
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