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Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
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Michael_Galli
by • Contributor III
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Resolved! Pipelines with alot of Spark Caching - best practices for cleanup?

We have the situation where many concurrent Azure Datafactory Notebooks are running in one single Databricks Interactive Cluster (Azure E8 Series Driver, 1-10 E4 Series Drivers autoscaling).Each notebook reads data, does a dataframe.cache(), just to ...

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Hubert-Dudek
Esteemed Contributor III
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This cache is dynamically saved to disk if there is no place in memory. So I don't see it as an issue. However, the best practice is to use "unpersist()" method in your code after caching. As in the example below, my answer, the cache/persist method ...

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