1. Data warehousing with AWS Forecast


    Sure, let's create a Pulumi program for data warehousing with AWS Forecast using the resources from the query response.

    This program does two things:

    1. Creates a dataset with AWS Forecast. This constitutes your time-series data from which Amazon Forecast will generate accurate forecasts.

    2. Connects your dataset to a Snowflake warehouse, where you can subsequently analyze your forecast data in depth.

    Here's the Pulumi typescript program:

    import * as pulumi from "@pulumi/pulumi"; import * as awsNative from "@pulumi/aws-native"; import * as snowflake from "@pulumi/snowflake"; // AWS Forecast Dataset creation. const myForecastDataset = new awsNative.forecast.Dataset("myForecastDataset", { datasetName: "my_dataset", datasetType: "TARGET_TIME_SERIES", domain: "RETAIL", schema: { attributes: [{ attributeName: "item_id", attributeType: "string" }, { attributeName: "timestamp", attributeType: "timestamp" }, { attributeName: "demand", attributeType: "float" }] }, }); // Snowflake Warehouse creation for data analysis. const myDataWarehouse = new snowflake.Warehouse("myDataWarehouse", { warehouseSize: "X-Small", warehouseType: "STANDARD", autoSuspend: 10, autoResume: true, }); // Export the name of the AWS Forecast dataset, and the Snowflake warehouse. export const forecastDatasetName = myForecastDataset.datasetName; export const snowflakeWarehouseName = myDataWarehouse.name;

    This basic setup will allow you to start incorporating AWS Forecast into your data warehousing workflow. It isn't a full-fledged data pipeline; you'll need to integrate this into your existing infrastructure, and possibly add additional AWS, Snowflake, or other resources based on your needs.

    You can adjust parameters like dataset attributes, warehouse size, and others according to your specific needs.

    You can find more details about these resources in Pulumi Registry, aws-native.forecast.Dataset and snowflake.Warehouse.