1. Ad-hoc Data Exploration using Databricks SQL Endpoint


    To perform ad-hoc data exploration using a Databricks SQL endpoint, we'll use Pulumi to provision the necessary resources. Databricks is a unified data analytics platform that is excellent for ad-hoc analysis because it enables you to query massive datasets quickly.

    A SQL endpoint in Databricks provides you with an interface to execute SQL queries against your data. It allocates resources to execute your SQL commands and can connect to various data sources.

    Here is a step-by-step guide on creating a Databricks SQL endpoint with Pulumi:

    1. Setup Pulumi with Databricks: Make sure you have configured Pulumi to use the Databricks provider. This typically involves setting up authentication to allow Pulumi to communicate with your Databricks workspace.

    2. Define the SQL Endpoint: We will define an instance of a SQL Endpoint using the databricks.SqlEndpoint class. This instance represents a Databricks SQL endpoint resource, and we can configure it with the necessary properties, such as the cluster size, the maximum number of clusters, and data source ID, among others.

    Let's look at the Python code to set up a Databricks SQL Endpoint for ad-hoc data exploration.

    import pulumi import pulumi_databricks as databricks # Create a Databricks SQL Endpoint. # Adjust the resource configuration according to your specific requirements. sql_endpoint = databricks.SqlEndpoint("my-sql-endpoint", cluster_size="Medium", auto_stop_mins=120, enable_photon=True, num_clusters=1, max_num_clusters=1 ) # The endpoint URL can be outputted if you want to connect to it from other tools. # Note: This URL should be kept secure as it gives direct access to the SQL Endpoint. endpoint_url = sql_endpoint.jdbc_url.apply(lambda jdbc: jdbc if jdbc else "Unavailable") pulumi.export("databricks_sql_endpoint_url", endpoint_url)

    In the code above, we initialize the Pulumi Databricks package and proceed to define the SqlEndpoint resource. The endpoint is configured with a medium cluster size for cost-effective but capable query performance, auto-stop to save costs when not in use, and the Photon-powered execution engine for faster performance. We set both num_clusters and max_num_clusters to 1 since we're doing ad-hoc exploration and don't need a scalable set of clusters.

    The pulumi.export statement at the end outputs the JDBC URL of the endpoint. You can use this URL to connect to the SQL endpoint from various tools that support JDBC connections for running your ad-hoc queries.

    Remember that you'll need to have the appropriate access set up to utilize Databricks resources, and be mindful of the security implications of accessing the JDBC URL. It's also essential to manage costs when working with cloud resources, hence the inclusion of an auto-stop parameter.

    Before running the above Pulumi code, ensure you've set up your environment with Pulumi and the necessary cloud provider's CLI tools installed and authenticated. You would run this Pulumi program within a Python environment where the pulumi and pulumi_databricks packages are installed. After executing the pulumi up command in the CLI, Pulumi will provision the resources as described in the code.