Metastore Setup for Machine Learning Pipelines in Databricks

Question

Pulumi · Accepted Answer

Setting up a Metastore for Machine Learning pipelines in Databricks involves creating a managed Hive MetaStore, which stores metadata for the tables created in Databricks Notebooks or through Jobs. It's useful for maintaining a consistent view of your data across different notebooks and clusters, facilitating machine learning workflows that depend on structured data.

In Pulumi, you can define cloud resources using infrastructure as code, and the `databricks.Metastore` resource allows you to create a metastore in Databricks programmatically.

Below is a Pulumi program in Python which demonstrates how to set up a metastore in Databricks:

```python
import pulumi
import pulumi_databricks as databricks

# Instantiate the Databricks provider resource.
# Make sure that the Databricks provider is configured with the necessary
# access credentials within your environment.

# Create a new Databricks Metastore. The Metastore is a centralized repository
# for Databricks SQL metadata, where you store metadata about tables, views, and
# databases that can be used with Databricks clusters.

metastore = databricks.Metastore("myMetastore",
                                  # Set the name for the Metastore. In a real scenario, you would choose a meaningful name.
                                  name="example-metastore",
                                  # Specify the root location of the Metastore for storing metadata.
                                  storage_root="dbfs:/metastores/example-metastore",
                                  # Set the cloud provider where the Metastore will be located.
                                  cloud="AWS",
                                  # Specify the owner of the Metastore. Typically, this could be the account that manages the resource.
                                  owner="owner@example.com",
                                  # The region where the Metastore is to be hosted.
                                  region="us-west-2",
                                  # Provide additional settings such as whether to force destroy the Metastore,
                                  # sharing settings, default data access configurations, etc.
                                  # These options are typically dependent on your specific use case and organizational policies.
                                  )

# Export the ID of the Metastore, which might be useful for referencing in other resources or outputs.
pulumi.export('metastore_id', metastore.metastore_id)
```

In this program, the `databricks.Metastore` constructor is called to create a new Metastore in Databricks. Here are details about some of the parameters used:

- `name`: A name for your Metastore which should be unique.
- `storage_root`: The location in Databricks File System (DBFS) where your Metastore metadata will be stored. This is typically a path in DBFS designated for Metastore use.
- `cloud`: The cloud provider being used, which in this case could be AWS, Azure, or GCP.
- `owner`: The user or entity that owns this Metastore.
- `region`: The cloud region where the Metastore will be located.

The `pulumi.export` statement at the end of the program will output the 'metastore_id' upon successful deployment. This ID can be used to reference the Metastore in other parts of your Pulumi code or cloud infrastructure.

Make sure the Databricks provider is correctly configured in your environment to authenticate with your Databricks workspace. This typically requires setting up the necessary tokens or credentials as environment variables or Pulumi configuration settings. For specific instructions on how to do this, please refer to the Databricks provider documentation.

Once the program is ready, you can use the Pulumi CLI to deploy it — this operation is called a `pulumi up`. After careful review of the proposed changes, confirm the deployment to provision the Metastore in your Databricks environment.