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This week's Databricks post in our mini-series is focused on adding custom code libraries in Databricks. Databricks comes with many curated libraries that they have added into the runtime, so you don’t have to pull them in. There are installed libraries in Python, R, Java, and Scala which you can get in the release notes in the System Environment section of Databricks.
It’s common to need to add in custom code of some kind and in my video, I’ll demo three ways to add custom libraries in Databricks in a cluster, workspace, and notebook, as well as some use cases for each. Below is the summary of each option and how to get started but be sure to watch my demo which goes into much more detail and will walk you through each option.
I hope this was helpful in showing you three ways to add custom code to Databricks at the cluster level, workspace, and notebook. If your organization needs help with Databricks, Power Platform or Azure in general, we’re here to help. Contact us to learn more about how our customized solutions can help you gain valuable insights into your data for better business decisions.
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