Explain how the information collected is transformed into a valuable form for the customer.
Explain how the information collected is transformed into a valuable form for the customer.
ETL (Extraction, Transformation, and Load) is a three-phase process that involves extracting, transforming, and loading data. Data is gathered from sources during the extraction stage by acquiring flat text files and storing them in the repository (ODS) in intermediate tables. During the transformation stage, the data is adjusted. It’s typical to duplicate tables with valuable data and generate new column data during this procedure. This could be done to organize data based on region, period, or hierarchical structure. Finally, data loading programs are run in the third step of ETL, and once the data has been restructured, it is put into the data repository’s definitive tables (Colabroy & Bell, 2019). The tables with accurate data are duplicated once more, and new fields required holding all of the data are generated.
Data audit
A data audit is highly recommended before the definitive load since inaccurate data must not be included in the analytical model. This would influence the results by introducing duplicates or extreme values. The first step is to identify the possible causes of inaccurate data. These causes are frequently detected since most of the information comes from an existing operational database. Simple operations like counting nulls or accumulating data can assist avoid last-minute surprises.
Cleaning applies to all procedures for removing records from our data storage that will not be used. If a back-out is required, it is recommended that these records be kept in databases that will not be used as a backup. This is followed by standardization, which assures that all values referring to the same data have consistent data in a specific value, such as people’s names (Colabroy & Bell, 2019). The deduplication step includes identifying possible data duplicates and isolating them until they are deleted according to the criteria. If all of the preceding parts of the audit have been successfully performed, the data’s integrity is assured, and the data can then be moved to the final load.
There are technologies on the market that can help with all of these phases by automation and allowing for the construction of a workflow so that moETL modifications have the least influence on development costs (Colabroy & Bell, 2019). The ETL and data collecting component of the project is finished once these steps have been accomplished.