1. Predictive Analytics with BigQuery User-Defined Functions


    To achieve predictive analytics with BigQuery using User-Defined Functions (UDFs), you would typically write SQL queries that call custom functions written in JavaScript or SQL for complex operations on your data. These UDFs can be used for a variety of tasks, such as data transformation, custom calculations, or applying machine learning models.

    In the Pulumi context, you can create UDFs within BigQuery by defining a Routine resource. The Routine resource represents a UDF that you can use within your BigQuery SQL queries.

    Below is a Pulumi program that showcases how to create a BigQuery dataset and a corresponding UDF within that dataset. The UDF in this example is a simple JavaScript function that squares a number, but you can replace the function body with any JavaScript code for more advanced predictive analytics operations.

    Here's how you would set it up:

    1. Define a BigQuery dataset: This is where your data will be stored, and where the UDF will be executed.
    2. Define a Routine resource: The routine represents the UDF itself, including its code and signature.
    import pulumi import pulumi_gcp as gcp # Create a BigQuery dataset dataset = gcp.bigquery.Dataset("analytics_dataset", dataset_id="analytics_dataset", description="Dataset for analytics", location="US" ) # Define a User-Defined Function (UDF) in BigQuery udf = gcp.bigquery.Routine("square_function", project=dataset.project, dataset_id=dataset.dataset_id, routine_id="square", language="SQL", routine_type="SCALAR_FUNCTION", return_type="FLOAT64", arguments=[{ "name": "x", "dataType": { "typeKind": "FLOAT64" } }], definition_body=""" CREATE FUNCTION square(x FLOAT64) RETURNS FLOAT64 LANGUAGE js AS """ function square(x) { return x * x; } """"; """ ) # Export the UDF name and its dataset pulumi.export("dataset_id", dataset.dataset_id) pulumi.export("udf_id", udf.routine_id)

    In this code block:

    • We begin by importing the required Pulumi modules.
    • We create a Dataset object, specifying an ID, description, and location for the dataset.
    • We then create a Routine object, which is our UDF named square_function. This routine is assigned to the dataset we created and uses SQL language. The routine is of type SCALAR_FUNCTION, meaning it returns a single value.
    • The return_type is specified as FLOAT64, and it takes a single argument x of the same type.
    • The definition_body contains the SQL statement that creates the function. The embedded JavaScript function within the triple quotes calculates the square of its input.
    • Finally, we export the dataset and UDF IDs using Pulumi's export functionality for use in other Pulumi programs or to reference in the Pulumi Console.

    Remember to replace the UDF's JavaScript code with your predictive algorithm or any complex data processing logic you need in your BigQuery analytics.