Big Data Processing with Databricks for AI Applications

Question

Pulumi · Accepted Answer

Big data processing with Databricks for AI applications involves setting up a Databricks workspace and leveraging various components such as clusters for computation, tables for data storage, and notebooks for collaborative coding and data exploration. Databricks provides a unified analytics platform that simplifies big data processing and machine learning tasks.

In this example, we will set up a Databricks environment using Pulumi to serve as a platform for AI applications. We will:

1. Create a Databricks workspace.
2. Set up a Databricks cluster that can run our data-processing jobs.
3. Create a Databricks job that can run tasks such as executing notebooks or Spark jobs. This job could represent an AI workload, for example, training a machine learning model.

To begin, ensure you have the following prerequisites:

- The Pulumi CLI installed and configured.
- Access to an Azure or AWS account where you'll deploy the Databricks workspace.
- Python 3.x and `pip` installed.

The following program will set up a Databricks workspace on Azure (since Databricks is natively supported on Azure and has first-class integration). Here is how you can accomplish this using Pulumi with Python:

```python
import pulumi
import pulumi_databricks as databricks
import pulumi_azure_native as azure_native

# Create an Azure Resource Group to hold the resources
resource_group = azure_native.resources.ResourceGroup('rg')

# Create a Databricks Workspace
workspace = azure_native.databricks.Workspace('workspace',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure_native.databricks.SkuArgs(
        name="standard"  # You can change to "premium" based on your needs
    ),
    managed_resource_group_id=pulumi.Output.concat('/subscriptions/', pulumi.get_stack(), '/resourceGroups/', resource_group.name, '-databricks')
)

# Create a Databricks Cluster
# Here we specify the node type (VM size), the Databricks runtime version,
# and enable autoscaling for the cluster.
cluster = databricks.Cluster('cluster',
    resource_group_name=resource_group.name,
    workspace_name=workspace.name,
    spark_version='7.3.x-scala2.12',  # This runtime version is an example; check for the latest version in Databricks documentation.
    node_type_id='Standard_DS3_v2',  # This VM size is an example; select a size appropriate for your workload.
    autoscale=databricks.ClusterAutoscaleArgs(
        min_workers=1,
        max_workers=10
    ),
)

# Create a Databricks Job to submit AI jobs or run notebooks. Specify the cluster to run the job on,
# and define the tasks that the job will execute.
job = databricks.Job('job',
    resource_group_name=resource_group.name,
    workspace_name=workspace.name,
    existing_cluster_id=cluster.id,
    tasks=[
        databricks.JobTasksArgs(
            notebook_task=databricks.JobNotebookTaskArgs(
                notebook_path='/Users/your.user@example.com/MyNotebook'  # Specify the path to the notebook.
            )
        )
    ]
)

# Export the Databricks Workspace URL
pulumi.export('databricks_workspace_url', workspace.uiDefinitionUri)

# Export the ID of the Databricks Cluster
pulumi.export('databricks_cluster_id', cluster.id)

# Export the ID of the Databricks Job
pulumi.export('databricks_job_id', job.id)
```

Here's what each section of the code does:

- We define an Azure Resource Group with `azure_native.resources.ResourceGroup`.
- We create a Databricks workspace within the resource group using `azure_native.databricks.Workspace`.
- We add a cluster with `databricks.Cluster`. The cluster specifications include the node type and the Databricks runtime version. Autoscaling is enabled to automatically adjust the number of nodes based on the workload.
- We define a Databricks job with `databricks.Job`, which can execute tasks like running a notebook. We attach the previously created cluster to this job.
- We use `pulumi.export` to output the important URLs and IDs, so you can easily access them later.

Once you've created this program, run it using the following Pulumi CLI commands:

```sh
pulumi stack init dev  # Initialize a new Pulumi stack named 'dev'
pulumi up              # Preview and deploy the changes
```

This will provision the necessary infrastructure on Azure for big data processing with Databricks, suitable for AI applications. You would replace the notebook path with your own and ensure your notebooks and jobs are set up to run your specific AI models or data processing workflows.