1. Delta Lake for Structured Streaming in Databricks


    Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can integrate Delta Lake with Structured Streaming to provide a robust solution for handling streaming data in Databricks.

    To accomplish this, we first need to create a Databricks workspace where we can run our Delta Lake and Spark applications. After the workspace is set up, you'd typically create a Databricks cluster, and within Databricks, use notebooks or jobs to setup and manage your Structured Streaming jobs using Delta Lake.

    Here's how you can use Pulumi to automate the creation of your Databricks infrastructure to run Delta Lake for Structured Streaming workloads:

    1. Set up a Databricks workspace using the provided azure-native.databricks.Workspace resource if on Azure.
    2. Deploy a Databricks cluster using the databricks.Cluster resource, configured with the appropriate Spark version and node types to support Delta Lake.
    3. Create a Databricks job or notebook that configures Structured Streaming to read from your sources and write to Delta Lake tables using the databricks.Job or scripting APIs.

    Below is a basic example program that shows how to create a Databricks workspace and cluster using Pulumi. The Databricks job or notebook can't be defined directly using Pulumi, but you can automate that by using Databricks REST APIs or CLI tools within your deployment scripts if needed.

    The example uses Azure as the cloud provider, but similar resources are available for AWS and GCP:

    import pulumi import pulumi_azure_native as azure_native # Create a Databricks Workspace databricks_workspace = azure_native.databricks.Workspace( "myDatabricksWorkspace", resource_group_name="myResourceGroup", location="westus", sku=azure_native.databricks.SkuArgs( name="standard" # Choose sku based on your requirement (standard, premium, or enterprise) ), # Additional options like tags can be provided here. ) # NOTE: The actual workspace creation in Azure might take some time. # Export the Databricks Workspace URL pulumi.export('databricks_workspace_url', databricks_workspace.workspace_url) # Normally, you'd create a Databricks Cluster using databricks.Cluster, but # as of the knowledge cutoff date, Pulumi does not support Databricks on a # granular level for resources like clusters and notebooks. You would typically # use the Databricks workspace URL to log in to the Databricks console and create # a cluster manually or use the Databricks REST API to automate this process. # After setting up the infrastructure with Pulumi, you need to create a cluster # and configure Delta Lake with Structured Streaming jobs using Databricks notebooks # or APIs. # Code to create a cluster or a Job will typically look something like this, # but please note that you cannot directly deploy a Databricks cluster with Pulumi # as of now. # example_cluster = databricks.Cluster( # "deltaLakeCluster", # ... # ) # example_job = databricks.Job( # "structuredStreamingJob", # ... # )

    In the program above, we're setting up a Databricks workspace within which you can manually set up your clusters and jobs, or automate cluster creation and job deployment by scripting against the Databricks API.

    Please remember that to efficiently use Delta Lake for structured streaming within Databricks, having a detailed understanding of both Apache Spark and Databricks is essential. It’s recommended that you review the Databricks documentation for the most up-to-date methods and APIs to work with Delta Lake effectively.

    For more detailed examples and guides, you might want to explore the official Databricks documentation and the Delta Lake Guide.