Databricks bundles - good practice for multiprocessing envs

[mderela]

I'm seeking advice regarding Databricks bundles. In my scenario, I have multiple production environments where I aim to execute the same DLT. To simplify, let's assume the DLT reads data from 'eventhub-region-name,' with this being the only differing factor. Therefore, I can parameterize a variable and utilize it as a configuration parameter in the DLT definition.

However, I'm unsure about the best practice for targeting. Both environments are production, and I prefer to keep the code simple. Should I define something like 'prod-1' and 'prod-2' as targets, or is there a better approach?

[Kaniz_Fatma]

Hi @mderela,

When dealing with Databricks bundles in a multi-environment setup, there are some best practices you can follow to ensure smooth execution and maintainable code.

Let’s explore a couple of recommendations:

1. Parameterization and Configuration:

   • Your approach of parameterizing the differing factor (in this case, the ‘eventhub-region-name’) is a good start. By using a variable, you can easily switch between environments without modifying the core logic of your DLT.
   • Consider defining environment-specific configuration parameters (such as connection strings, credentials, or other settings) in a central location (e.g., a configuration file or a secret store). This way, you can keep the code clean and avoid hardcoding environment-specific details directly in your DLT definition.

2. Dynamic Cluster Selection:

   • Create a mapping table that associates each target environment with a specific Databricks cluster. In your job definition, look up the target environment and select the corresponding cluster from the table.
   • This approach provides flexibility and avoids hardcoding cluster names directly in your job definition. It allows you to scale clusters independently for different environments and manage them separately¹.

3. Service Principals for Production Deployments:

   • Databricks recommends using service principals for production deployments. You can enforce this by setting the run_as property to a service principal.
   • Service principals provide better security and allow you to manage access control more effectively. See the Databricks documentation for details on managing service principals and specifying a run ide...².

For more detailed information, you can refer to the Databricks documentation on Databricks Asset Bundle deployment modes.² Additionally, consider exploring best practices for integrating notebooks into production assets, a...³.