1. Real-time Data Visualization with Databricks SQL Endpoints

    Python

    To create real-time data visualization with Databricks SQL endpoints using Pulumi, you need to set up a Databricks SQL endpoint that can interact with visualization and analytics tools. Here's how you can achieve this:

    1. Databricks SQL Endpoint: A compute resource for analytics workloads that provides a serverless experience and allows you to connect BI tools using JDBC/ODBC. It's the execution engine for your SQL queries.

    2. Databricks SQL Dashboard: You can use SQL dashboards to create and share visualizations based on the output of SQL queries.

    3. Databricks SQL Query: The actual SQL queries that you want to execute against your data. These queries can then be visualized in a dashboard.

    Below is a Pulumi program written in Python to set up a Databricks SQL endpoint, SQL Dashboard, and a SQL Query. This will allow you to create visualizations of your data in real-time.

    import pulumi import pulumi_databricks as databricks # Creating a Databricks SQL Endpoint sql_endpoint = databricks.SqlEndpoint("sql-endpoint", name="sql-endpoint-demo", state="RUNNING", cluster_size="ExtraSmall", # Further configuration can be added such as tags, number of clusters etc. ) # Creating a Databricks SQL Dashboard sql_dashboard = databricks.SqlDashboard("sql-dashboard", name="sql-dashboard-demo", # Further configuration can be added such as tags, parents, etc. ) # Creating a Databricks SQL Query sql_query = databricks.SqlQuery("sql-query", name="sql-query-demo", query="SELECT * FROM demo_table WHERE value > 100", # Replace with your actual SQL query. data_source_id=sql_endpoint.id.apply(lambda id: f"{id}"), # Linking the SQL query to our endpoint. # Further configuration can be added such as tags, schedules, etc. ) # Exporting the names of our created resources pulumi.export("sql_endpoint_name", sql_endpoint.name) pulumi.export("sql_dashboard_name", sql_dashboard.name) pulumi.export("sql_query_name", sql_query.name)

    This program performs the following steps:

    • SqlEndpoint: Creates a Databricks SQL endpoint called sql-endpoint-demo which we will use to execute our queries. It is set to running state with an extra small cluster size, reflecting a cost-effective option suitable for a demo or development environment.

    • SqlDashboard: Initiates a Databricks SQL dashboard with the name sql-dashboard-demo. This is where you will visualize query results.

    • SqlQuery: Defines a sample SQL query resource that you can replace with your own query. The data_source_id property links the query to the previously created SQL endpoint.

    Finally, the program exports the names of these resources which you can use for reference or in outputs in your CI/CD pipeline or elsewhere. You would typically interface with these resources using the Databricks UI or API to manage and visualize your data in real-time.

    To run this Pulumi program, you'll need access to a Databricks account and the corresponding Pulumi provider setup. Ensure you have the appropriate credentials configured for Pulumi to interact with Databricks.

    Please note that real-time data visualization often involves additional setup like event streaming resources, data sources, and specific queries tailored to your data. The above program assumes that you have such data sources and tables available in your Databricks workspace.


GPT4o|Modeexplainbalancedcode