Playbooks: Updating Your Custom Metadata

Name: Playbooks: Updating Your Custom Metadata
Uploaded: 2025-09-23T16:09:08.983Z
Duration: 3 min 30 s
Description: Automate custom metadata updates with Atlan playbooks, reducing manual effort and keeping your data assets clean and lean for efficient deprecation review.

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Transcript

4 min

00:00 — Setting up a one time deprecation playbook
Custom metadata allows for the customization of Atlan to fit the metadata needs of any business, whether it's adding in additional context, around assets, SMEs, or stewards or cost centers, it allows anyone to ensure that the right metadata is available for their assets. The great news is that automating updates on any custom metadata can be done through playbooks, reducing the amount of manual work needed to get that data updated and maintained. In this example, we will examine how to identify assets that are deprecation game dates and update a custom metadata field to reflect this.
We click on the governance icon on the left-hand navigation to be taken to the governance section and then click on playbooks to open the playbooks page. Clicking on the create new brings the playbook creation pop-up. Since we were looking to update assets for possible deprecation through custom metadata, we named the playbook identifying possible assets for deprecation. We are doing this once as we are doing a mass cleanup of our state.
01:00 — Filtering by lineage connector and usage
So we do not enter a recurrence in the name, but this is absolutely something that can be scheduled on a weekly or monthly basis to identify new assets for deprecation, ensuring that the data state is kept lean in up to date. We clicked create in the playbook creation page shows for the rules Dame. We enter the same value as the playbook name, as we were only creating a single rule for this. We will be using five filters for this playbook. As we are looking to update big query tables and views that have no lineage in no quarries in the last 30 days.
For a first filter we use the attribute has lineage from the properties dropdown. We use is for the operator and select false to select only our assets without lineage. We add a second filter in select the attribute of connector. The operator of is one of and select big query. For our third filter, we use the attribute of the big query specific filter for usage.
02:00 — Excluding already reviewed assets and validating matches
We select the number of quarries as that attribute added operator of less than and put the value of one. For our fourth filter, we add the attribute of asset type. Operator of his one-off and that select both tables and views. Finally, since we have run a similar playbook before and updated values for our deprecation status, we add a fifth filter. We select our cost optimization, custom metadata property, and the metadata property of deprecation status as our attribute. We update the operator too is empty. This ensures that we do not update the status on any assets we've already tagged or update the status on those assets that have been worked on.
Our asset matches update and we click on view all to validate our results. Everything looks perfect. Next we add our action. We select our custom metadata field of cost optimization in the custom metadata property of deprecation status for the operator.
03:00 — Setting the deprecation status and running once
We keep update and select the value of to be reviewed. Our playbook is set. So we click next. Since we are doing a onetime update, we select the run once option at the top and click complete. Our playbook is now running. In just a few steps. We have successfully marked our assets for deprecation review, allowing us to review assets for deprecation, ensuring we are running clean and lean in our big query warehouse.

About this demo

See how to create playbooks that update custom metadata, such as deprecation status, based on filters for lineage, usage, or asset type. Automating custom metadata updates reduces manual work and keeps governance accurate.