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Optimizing Costs in Databricks by Dynamically Choosing Cluster Sizes
Databricks is a popular unified data analytics platform known for its powerful data processing capabilities and seamless integration with Apache Spark. However, managing and optimizing costs in Databricks can be challenging, especially when it comes ...
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How can this actually be used to choose a cluster pool for a Databricks workflow dynamically, that is, at run time? In other words, what can you actually do with the value of `selected_pool` other than printing it out?
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