Summary
In this chapter, we have tried to cover all the main aspects of how Azure Databricks works. Some of the things we have discovered include how notebooks can be created to execute code, how we can import data to use, how to create and manage clusters, and so on. This is important because when creating ETLs and ML experiments in Azure Databricks within an organization, aside from how to code the ETL in our notebooks, we will need to know how to manage the data and computational resources required, how to share assets, and how to manage the permissions of each one of them.
In the next chapter, we will apply this knowledge to explore in more detail how to create and manage the resources needed to work with data in Azure Databricks, and learn more about custom VNets and the different alternatives that we have in order to interact with them, either through the Azure Databricks UI or the CLI tool.
