1. DBFS for Storing Large-Scale Machine Learning Datasets


    To store large-scale machine learning datasets, cloud providers offer services that are optimized for high-throughput and scalability, such as the Azure Machine Learning Datastore or Databricks File System (DBFS) on Azure Databricks. In this context, I will guide you on how to use Pulumi to provision a Databricks DBFS using Azure as your cloud provider.

    We will use the databricks.DbfsFile resource from the Pulumi Databricks provider to create a simple file in DBFS. Remember that in a real-world scenario, you would likely use more complex structures or upload large quantities of data.

    The databricks.DbfsFile object represents a file in Databricks File System (DBFS). Databricks is widely used for machine learning and big data processing. By defining files in Pulumi, you can version control your file definitions and changes, making it easy to manage your data assets alongside your infrastructure.

    Here’s a Pulumi Python program that outlines how you can create a file on DBFS. In this example, we create a file with some base64-encoded content, which could represent a serialized dataset:

    import pulumi import pulumi_databricks as databricks # Base64 content for the file file_content = "SGVsbG8sIERhdGFGcmVha3Mh" # "Hello, DataFreaks!" in base64 encoding # Create a DBFS file in Databricks dbfs_file = databricks.DbfsFile("my-dataset", content_base64=file_content, path="/mnt/my-datasets/hello.txt" ) # Export the URL of the file that we just created pulumi.export("dbfs_file_url", dbfs_file.path)

    This program does the following:

    • It imports the required Pulumi packages.
    • Sets the content we want to place in the file in base64 format.
    • Creates a DbfsFile resource with the base64 content and places it at the specified path within the DBFS mounted storage.
    • Exports the path of the file so you can locate it within Databricks.

    Note: You would need to set up the Databricks workspace and ensure that Pulumi has access to create resources within it. Ensure that you have the appropriate access rights and have authenticated with Databricks from the environment where you run Pulumi.

    After running this Pulumi program, you will have a new file in DBFS at the specified path. You can then access this file within Databricks notebooks, jobs, or workflows to use your large-scale machine learning datasets.

    This is a simple starting point, and you might want to further expand this to accept files from your local machine or another data source, handle directories of files, or work with more sophisticated permissions and lifecycle policies.

    For more information, visit the DbfsFile documentation.