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How to Identify

Dependent datasets

When making edits to a dataset, check the Dependencies graph to see if there are potential impacts. This graph is in the menu bar in the Transformer page. you can verify if your changes potentially impact other datasets that rely on it. In the Transformer page, click the drop-down next to the current dataset's name to open the Dataset Navigator.


Tip: In the Dependencies graph, the second number is the count of dependent datasets. If this value is greater than 1, then other datasets depend If your current dataset is connected to datasets to the right of it, those datasets are dependent on the current one. Changes in your current dataset can affect downstream datasets. Before After you make edits changes to your the current datasetone, you should review the transform steps in dependent datasets that pull in the current dataset to identify possible impacts.


use the Dataset Navigator to open wrangled datasets that are connected to it and to the right of it in flow view.

See Dataset Navigator.

Broken data integrations

When you make some changes in an upstream dataset, the recipes for any downstream datasets can break, such that you cannot generate satisfactory results. In the downstream dataset, you may see errors in the Recipe panel, such as the following:

D caption
Dependency error in the Recipe panel

In the above, the column Item_Nbr Day does not exist in the upstream datasetcurrent dataset, which is causing problems in the last two recipe stepsThese types of errors are generated when a column in the upstream dataset has been dropped or renamed.


  1. In the Transformer page, click the Dependencies graph. In the Uses open the Dataset Navigator from the drop-down next to the current dataset name. In the Flow View tab, open the dataset referenced in the error message. 
  2. In the Recipe panel, locate the step where the column was removed.


    Tip: In some cases, it may be easier to download the recipe from the panel and search it for the name of the column (Item_Nbr).

  3. Fix the issue. See Fixing Dependencies belowDetails are below.

Hidden breakages

If you make changes to specific values in a dataset, recipe steps in downstream datasets can break if they rely on detecting specific values. Depending on the usage, the step may not actually be broken, but the generated results are incorrect. 


  1. Fix the issue in the source dataset. Verify that the change does not impact other datasets.


    NOTE: If you fix the issue in the source dataset, you should verify if any other downstream datasets are impacted by this change. If the downstream value for the Dependencies Graph is more than 1, you should explore those datasets to verify that your change is not propagating issues into those datasets.

  2. Change the input dataset to use one a dataset that is not broken. 


    Tip: If you must freeze the data in the dataset that you are using as an input, you can create a copy of the dataset as a snapshot. See Dataset Details Page.

    To use the copy, repair or rebuild the integration using the copied version.

  3. Fix the issue in the dataset that depends on it. In this case, you must redefine the transformation that brings in the data.