As organizations load an increasing amount of data from different sources into their data warehouses, monitoring these data loads becomes ever more crucial. Traditional database monitoring happens at the level of the whole database. When a solution incorporates Python scripts with SQL, businesses can monitor their data loads at individual tables and receive daily reports on which loads have succeeded and failed. While there are no commercial tool sets for table-level data load monitoring currently available, there is code that can be used to automate the process, generating reports that teams can use to quickly repair failed loads.
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