Data Engineering Pipelines with Databricks Spark

Question

Pulumi · Accepted Answer

Data Engineering Pipelines are crucial in processing and transforming large amounts of data. They are often used to ingest, clean, enrich, and move data from various sources to data lakes, warehouses, or other systems. Databricks, a data analytics platform, provides an integrated environment using Apache Spark that simplifies the process of building data pipelines. Pulumi, as an Infrastructure as Code tool, allows you to define, deploy, and manage these data engineering infrastructure resources efficiently.

Here, I will explain how to use Pulumi to set up a data engineering pipeline using Databricks Spark on the cloud. We will create a Databricks workspace, a Databricks Spark cluster, and a job that runs on the cluster to execute our data engineering tasks.

Firstly, you need to have Pulumi installed and configured for your cloud provider. For this example, we'll assume you are using Azure, but the process is similar if you use AWS or Google Cloud.

### Setting up a Databricks Workspace

The workspace is the high-level container for all your Databricks assets. It contains notebooks, libraries, and dashboards. Here's how to create one with Pulumi:

```python
import pulumi
import pulumi_azure as azure

# Create an Azure Resource Group for our resources
resource_group = azure.core.ResourceGroup('resource_group')

# Create a Databricks Workspace in the Resource Group
workspace = azure.databricks.Workspace('workspace',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku="standard")

# Output the Databricks Workspace URL for easy access
pulumi.export('Databricks Workspace URL', workspace.workspace_url)
```

### Creating a Databricks Spark Cluster

Once we have a workspace, our next step is to create a Spark cluster where we can run Spark jobs for processing data.

```python
import pulumi_databricks as databricks

# Define Spark cluster settings
cluster = databricks.Cluster("spark-cluster",
    resource_group_name=resource_group.name,
    workspace_name=workspace.name,
    num_workers=2,
    spark_version="7.3.x-scala2.12",
    node_type_id="Standard_D3_v2",
    )

# Output the Spark Cluster ID
pulumi.export('Spark Cluster ID', cluster.cluster_id)
```

### Creating a Job for Data Processing Tasks

Finally, let’s define a job to run on the Spark cluster. The job references a notebook, JAR, or a Python script that defines the data transformation tasks.

```python
# Suppose we have a Databricks notebook in DBFS or GitHub that we want to run as a job
notebook_path = "/Workspace/path/to/notebook"

# Create a job that runs a notebook
job = databricks.Job("data-engineering-job",
    existing_cluster_id=cluster.cluster_id,
    notebook_path=notebook_path,
    )

# Output the Job ID
pulumi.export('Job ID', job.job_id)
```

In the above code snippet, we have specified:
- `existing_cluster_id`: This links our job to the previously defined Spark cluster.
- `notebook_path`: The path to the notebook having your Spark code.

### Summary

With these resources, Pulumi will create a Databricks workspace and a cluster, and set up a job to run a Spark notebook for your data engineering tasks. After running this program with Pulumi CLI, it will output the Workspace URL, Cluster ID, and Job ID that can be used to access and manage your Databricks resources.

Here is the complete Pulumi program to accomplish this:

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

# Creating an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Creating a Databricks Workspace within the Azure Resource Group
workspace = azure_native.databricks.Workspace("workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku="standard")

# Output the Databricks Workspace URL
pulumi.export('Databricks Workspace URL', workspace.workspace_url)

# Define Spark cluster settings (assuming Databricks provider is already configured)
cluster = databricks.Cluster("spark-cluster",
    resource_group_name=resource_group.name,
    workspace_name=workspace.name,
    num_workers=2,
    spark_version="7.3.x-scala2.12",
    node_type_id="Standard_D3_v2")

# Output the Spark Cluster ID
pulumi.export('Spark Cluster ID', cluster.cluster_id)

# Specify a Databricks notebook to run as a job
notebook_path = "/Workspace/path/to/notebook"

# Create a job running the specified notebook
job = databricks.Job("data-engineering-job",
    existing_cluster_id=cluster.cluster_id,
    notebook_path=notebook_path)

# Output the Job ID
pulumi.export('Job ID', job.job_id)
```

You will execute this code in your local development environment where Pulumi and the cloud provider CLI are installed. It will communicate with the cloud provider to spin up the necessary resources in the cloud. This process is called "deploying the stack."

When the deployment is done, you'll have a fully configured Databricks workspace and a Spark cluster, and your data processing job will be set up and ready to execute. You can then navigate to the outputted URL to work with your Databricks workspace and manage your jobs within it.