DBFS as Intermediate Storage for ETL Workflows

Question

Pulumi · Accepted Answer

DBFS, or Databricks File System, is a file storage system layered on top of scalable object storage like Amazon S3, Azure Blob Storage, or Google Cloud Storage. In an ETL (Extract, Transform, Load) workflow on a platform like Databricks, DBFS can act as an intermediary storage layer where you extract data from various sources, store it in DBFS, perform transformations, and then load it into your target data warehouse or data mart.

Using Pulumi with Python, we can automate the provisioning of DBFS within a Databricks workspace to facilitate ETL workflows.

Below is a Pulumi program that creates a Databricks workspace and a file in DBFS, which could be part of an ETL data pipeline. The `databricks.DbfsFile` resource is used to manage files in DBFS. We'll simulate an ETL workflow by creating a single text file, but in a real-world scenario, you would extract your data from the source, transform it as needed using Databricks jobs or notebooks, and load it into DBFS.

Please ensure you have the appropriate Pulumi providers and Databricks tokens configured for use in this program.

```python
import pulumi
import pulumi_databricks as databricks

# Create a Databricks workspace where the ETL workflow will occur.
# The specific parameters such as Sku and others need to be tailored to match your requirements and pricing plans.
workspace = databricks.Workspace("etl-workspace",
    sku="premium", # Choose an SKU that matches your Databricks plan
    location="westus2", # Choose the location best suited to your requirements
    # Further configuration can be added here depending on the specifics of the workspace you need
)

# DBFS file - This example content represents the "Extract" phase where data is loaded into DBFS
dbfs_file = databricks.DbfsFile("etl-file",
    path="/mnt/etl-data/example_data.txt", # The path within DBFS where the data file will reside
    content="Extracted data content", # The content of the file, would be result of an extraction in a real ETL
)

# Output the URL to the file in the DBFS
pulumi.export("dbfs_file_url", dbfs_file.path)
```

In the program above:

- We create a Databricks workspace resource. The workspace is the environment where all Databricks resources like notebooks, clusters, and DBFS files reside.
- We then define a `DbfsFile` which simulates extracted ETL data and put into DBFS at a specified path. Although the content here is a simple text string, in practice, this would likely be a data file resulting from an extraction job.
- Lastly, we export the DBFS file path, which can be used as reference in subsequent stages of your data pipeline or for validation and testing purposes.

To run this Pulumi program, you would typically execute `pulumi up` in the command-line interface within the directory of your Pulumi project, which will provision the resources as defined in the program.

Remember that the actual ETL process involves more steps. You would need additional Pulumi scripting and Databricks configurations for data transformation jobs within Databricks and loading the data to a data warehouse or data store of your choice.