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Extraction, Transformation and Loading (ETL) processes aim to extract data from data sources to targets, via a set of transformations. In many situations, an ETL process can be subject to changes for several reasons. For instance, data sources changes, new requirements and bug fixing. When changes happen, analyzing the impact of change is mandatory to avoid errors and mitigate the risk of breaking existent treatments. Several solutions have been proposed for this issue. The authors propose a new approach, based on matrices, for analyzing the impact of change on ETL processes.
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