1. Large-scale Structured Data Processing with Databricks SQL Endpoint

    Python

    To accomplish large-scale structured data processing, you can use Databricks SQL Endpoints, which provide a serverless SQL query service that enables you to run SQL queries on your data lake with fine-grained control over performance and cost. In Databricks, SQL Endpoints are a set of compute resources that you can use to run interactive and automated data workloads that include SQL queries, SQL jobs, and BI dashboards.

    A SQL Endpoint is a serverless, Databricks-managed compute service for executing SQL queries in Databricks, and it's perfect for running powerful analytical queries on large datasets.

    In this Pulumi program, I'll guide you through setting up a Databricks SQL Endpoint using the Pulumi Databricks provider. You'll need an existing Databricks workspace and enough permissions to create resources within that workspace.

    Here's how you create a Databricks SQL Endpoint with Pulumi:

    1. Import the necessary packages.
    2. Define your Databricks provider configuration.
    3. Create a SQL Endpoint specifying the necessary parameters like the cluster size, which determines the compute power and memory available for the queries.
    4. Define resources like databases, tables, and queries that you want to run against the SQL Endpoint.
    import pulumi import pulumi_databricks as databricks # Configure the Databricks provider databricks_provider = databricks.Provider("mydatabricks_provider") # Create a SQL Endpoint for processing structured data at scale sql_endpoint = databricks.SqlEndpoint("my-sql-endpoint", channel="CURRENT", cluster_size="Large", enable_photon=True, # Photon is a query engine that accelerates queries. auto_stop_mins=120, # Automatically stop the SQL Endpoint after inactivity to save on costs. num_clusters=1, opts=pulumi.ResourceOptions(provider=databricks_provider) ) # Expose the JDBC URL of the SQL Endpoint for connecting BI tools or other SQL clients pulumi.export("sql_endpoint_jdbc_url", sql_endpoint.jdbc_url)

    In the sample above:

    • We configure the Databricks provider. You would need to configure the Pulumi Databricks provider with the necessary credentials to interact with your Databricks instance. This typically involves setting up the Databricks workspace URL and personal access token which can be sourced from environment variables or Pulumi configuration.
    • We define a SqlEndpoint. This endpoint is where your SQL queries will be executed. We're using a size of 'Large' for significant computational power and enabling the Photon query engine for accelerated query performance.
    • We set the auto_stop_mins to automatically stop the SQL Endpoint after 120 minutes of inactivity which is cost-effective.
    • We export the JDBC URL that can be used to connect to the SQL Endpoint from outside applications, like business intelligence tools.

    To run this program:

    1. Install Pulumi.
    2. Set up the Databricks provider configuration (workspace URL and personal access token).
    3. Save the above code in a file __main__.py.
    4. Execute pulumi up to deploy your infrastructure.

    Make sure to check out the Pulumi Databricks Provider documentation for more details on configuring the provider and the SQL Endpoint.


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