Thank you for posting your query!
In Azure Data Factory (ADF), when working with Delta Lake, you can manage the lifecycle of your Delta tables, including vacuuming, but there are some limitations when it comes to directly executing Delta Lake commands like
VACUUM
from within a Data Flow.
Steps to Vacuum Delta Table
- Use a Databricks Notebook: You can create a Databricks notebook to run the vacuum command and then call this notebook from your ADF pipeline.
- Vacuum Command: In the Databricks notebook, you can use the following command to vacuum the Delta table and keep no history:
spark.sql("VACUUM delta.`<path-to-delta-table>` RETAIN 0 HOURS")
- Integration with ADF: In your ADF pipeline, use the Databricks Notebook activity to call the notebook you created. This will ensure that the vacuum operation is executed immediately after your data flow completes.
-
Important Notes
- The
RETAIN 0 HOURS
option in theVACUUM
command will remove all files that are no longer referenced by the Delta table, effectively keeping no history. Be cautious with this option, as it will make it impossible to roll back to previous versions of the data. - Ensure that the ADF service has the necessary permissions to execute the notebook in Databricks.
please refer:https://learn.microsoft.com/en-us/azure/data-factory/format-delta
I hope this information helps. Please do let us know if you have any further queries.
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