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The Extract-Transform-Load (ETL) flows are essential for the success of a data warehouse and the business intelligence and decision support mechanisms that are attached to it. During both the ETL design phase and the entire ETL lifecycle, the ETL architect needs to design and improve an ETL design in a way that satisfies both performance and correctness guarantees and often, she has to choose among various alternative designs. In this paper, we focus on ways to predict the maintenance effort of ETL workflows and we explore techniques for assessing the quality of ETL designs under the prism of evolution. We focus on a set of graph-theoretic metrics for the prediction of evolution impact and we investigate their fit into real-world ETL scenarios. We present our experimental findings and describe the lessons we learned working on real-world cases