If so, you can copy the new and changed files only by setting " modifiedDatetimeStart" and "modifiedDatetimeEnd" in ADF dataset. If your data pattern is not belong to scenario #1 or #2, you can try to find if your file property “LastModifiedDate” can be used to differentiate the new files from the old ones. You can do this following the instruction below:ĪDF tutorial on incremental copy from time partitioned folder name If so, you can leverage the ADF system variable with parameter to get the new files only via time partitioned folder name or file name. For example, your folder structure may follow the pattern like “yyyy/mm/dd/”. If the files can not be deleted from data source after being moved to the destination, you can find if your folders or files are time-based partitioned or not. By doing so, all the files which show up in source store are new files by nature. If your files become useless in source store after being moved to the destination store, we suggest you to simply delete files from source store after successfully moving them to the destination store by setting “deleteFilesAfterCompletion” as true in copy activity. Given that, we will illustrate 4 different scenarios below with the best practices for each of using ADF to copy new files only. ![]() When we try to come up with the best approaches to copy new files only, it is impossible to achieve that without understanding your data pattern as well as the scenario environment. In this blog, we will talk about several best practices of using ADF copy activity to achieve that use case. One of the typical examples is that files can be continually dropped to a landing folder of your source store, where you want an easy way to copy the new files only to data lake store instead of repeatedly copy any files which have already been copied last time. In on-going ELT scenario, how to easily load new files only after an initial full data loading is a very common use case.
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