Scalable End-to-End Machine Learning Pipelines on Databricks

Question

Pulumi · Accepted Answer

To construct a scalable end-to-end machine learning pipeline on Databricks using Pulumi, you'll need to define several types of resources, such as clusters to run your computations, tables to manage your data, and jobs for orchestrating your machine learning workflows.

Databricks is a platform that provides collaborative data science and engineering environments, allowing you to prepare and explore data, as well as build and train machine learning models. To deploy these resources on Databricks using Pulumi, you will use the `pulumi_databricks` provider, which allows you to create and manage each component programmatically.

Let's build out the core components of a machine learning pipeline:

1. **Workspace**: An environment for accessing all your Databricks assets. If you are using Databricks on Azure, this would typically involve creating a Databricks Workspace resource.
   
2. **Databricks Cluster**: A set of computation resources where you can run your data preparation steps, machine learning algorithms, etc. It's where your actual code runs.
   
3. **Databricks Job**: Automates the process of setting up your data ingestion, preparation, model training, and prediction, and can be scheduled or triggered as needed.

4. **Databricks Table**: Tables in Databricks allow you to structure your data in a queryable format, making it easier for analysis and feeding into machine learning models.

Now, let's write a Pulumi program that provisions these resources. The following Python program illustrates how to create these Pulumi resources, although the specific attributes for each resource would depend on the requirements of your machine learning use case.

```python
import pulumi
import pulumi_databricks as databricks

# Provision a new Databricks Workspace (Azure Databricks Example)
# This would be the initial step if you're setting up from scratch on Azure.
# For AWS there's no equivalent resource at the Pulumi level, as Databricks would be set up directly.
workspace = azure_native.databricks.Workspace(
    "myWorkspace", 
    resource_group_name="myResourceGroup", 
    location="West US 2",
    sku=azure_native.databricks.SkuArgs(
        name="standard"
    )
)

# Provision a Databricks Cluster
# A cluster is a set of computation resources and configurations where you will run your ML workload.
cluster = databricks.Cluster(
    "myCluster",
    num_workers=3,  # This defines a small cluster, for larger workloads you would increase this and possibly use autoscaling
    spark_version="7.3.x-scala2.12",
    node_type_id="Standard_D3_v2"
    # You can provide additional attributes such as autotermination_minutes, aws_attributes, etc.
)

# Define a Databricks Job
# Jobs can run notebooks, JARs, and compile code on a Databricks cluster.
job = databricks.Job(
    "myJob",
    new_cluster=databricks.JobClusterArgs(
        num_workers=2,
        spark_version="7.3.x-scala2.12",
        node_type_id="Standard_D3_v2"
    ),
    notebook_task=databricks.JobNotebookTaskArgs(
        notebook_path="/Users/me/my_notebook"  # This should point to the notebook that you want to run
    ),
    # You can also define triggers for how often this job should run (e.g., cron schedule)
)

# Define a Databricks Table
# Tables are useful to structure your data for easy querying and analysis.
table = databricks.Table(
    "myTable",
    name="my_table",
    catalog_name="my_catalog",
    schema_name="my_schema",
    columns=[
        databricks.TableColumnArgs(name="id", type="int"),
        databricks.TableColumnArgs(name="feature", type="double"),
        databricks.TableColumnArgs(name="label", type="double"),
    ],
    table_type="DELTA",  # Using Delta Lake for ACID transactions and scalable metadata handling
    # Additional properties can be set such as location, properties, etc.
)

# Output the resource attributes, such as the Databricks cluster URL, which can be useful for access or integrations
pulumi.export('cluster_id', cluster.id)
pulumi.export('job_id', job.id)
pulumi.export('table_name', table.name)

# Note: Make sure that you've provided the necessary Databricks provider configuration 
# settings like host, token etc., so Pulumi can authenticate and manage resources.
```

This Pulumi program sets up the basic resources needed to create a machine learning pipeline with Databricks. Here's what each part does:

- **Workspace**: We start by creating a Databricks workspace in Azure, this is the environment where you can collaborate and deploy your pipelines. In the AWS cloud environment, your Databricks setup might be slightly different as Databricks integrates differently with AWS.

- **Cluster**: The cluster is the computational backbone of the Databricks platform. It's where all the data processing and model training will occur.

- **Job**: The job resource encapsulates the workload that you run on a cluster. It might point to a specific notebook or script within your Databricks workspace that contains the logic for your data transformation and model training steps.

- **Table**: Tables organize your data in a structured format within Databricks, making your data accessible for querying, machine learning, and other analytical operations.

Configure Databricks provider credentials and specifics as environment variables or through Pulumi configuration before running this program. Export the IDs of the resources so that you can reference them outside of Pulumi as needed.