The key vault should always be a core component of your Azure design because we can store keys, secrets, certicates thus abstract / hide the true connection string within files. When working with databricks to mount storage to ingest your data and query it ideally you should be leveraging this to create secrets and secret scopes.
Before discussing why you would want to pin a cluster it would be useful to understand the different states of a cluster. We can have:
A very common approach is to query data straight from Databricks via Power BI. For this you need Databricks token and the JDBC address URL. This is found within Account settings of the cluster.
Data engineers, pipe line developers, general data enthusiasts will be spending most of their time within a notebook. Here you develop your code, nice visualisations and commentary boxes are possible too, a very rich web-based interface and is best experienced with google chrome ( in my opinion).
I have spent many long weekends getting stuck into Azure Databricks, plenty of time to understand the core functionality from mounting storage, streaming data, knowing the delta lake and how it fits into the bigger picture with tech like Event hubs, Azure SQL DW, Power BI etc.
So, I am going to show you how easy it is to create a cluster (that’s the end goal), you will appreciate the ease of deployment for huge amounts of infrastructure.
Recently I got to a stage where I leveraged Databricks to the best of my ability to join couple of CSV files together, play around some aggregations and then output it back to a different mount point ( based on Azure Storage) as a parquet file, I decided that I actually wanted to move this data into Azure SQL DB, which you may want to do one day.