@Scout i wanna use a sensor which checks if a bigquery table has fresh data. and if there is fresh data it shall trigger a dbt cloud job. give me examples on how to do this

To create a sensor that checks for fresh data in a BigQuery table and triggers a dbt Cloud job when fresh data is found, you would typically follow these steps: 1. Define a sensor function that queries the BigQuery table to check for fresh data. 2. Use the sensor to trigger a job or an operation that initiates a dbt Cloud job run. Here's an example based on the documentation:

python
from dagster import sensor, RunRequest, SkipReason
from dagster_dbt import dbt_cli_resource
from dagster_gcp.bigquery.resources import bigquery_resource

# Define your resource configuration for BigQuery and dbt
resources = {
    "bigquery": bigquery_resource.configured({"project": "my-gcp-project"}),
    "dbt": dbt_cli_resource.configured({"project_dir": "path/to/your/dbt/project"})
}

# Define a sensor that checks for fresh data in a BigQuery table
@sensor(job=my_dbt_job, minimum_interval_seconds=3600)  # Run this sensor hourly
def my_bigquery_sensor(context):
    # Query BigQuery to check for fresh data
    # Replace 'my_table' and 'my_column' with your actual table and column names
    query = "SELECT MAX(my_column) FROM `my-gcp-project.my_dataset.my_table`"
    result = context.resources.bigquery.execute_query(query)
    latest_timestamp = result[0][0] if result else None

    # Check if the latest timestamp is greater than the last recorded timestamp
    last_recorded_timestamp = context.cursor or None
    if latest_timestamp and (last_recorded_timestamp is None or latest_timestamp > last_recorded_timestamp):
        # Update the cursor with the latest timestamp
        context.update_cursor(str(latest_timestamp))
        # Trigger the dbt Cloud job run
        return RunRequest(run_key=str(latest_timestamp), run_config={})
    else:
        # Skip if no fresh data is found
        return SkipReason(f"No fresh data found as of {context.scheduled_execution_time}.")

# Define your dbt job that should be triggered by the sensor
# This is a placeholder for your actual dbt job definition
my_dbt_job = ...

# Note: You will need to replace the placeholder `my_dbt_job` with your actual dbt job definition.
# The dbt job should be configured to run the dbt Cloud job using the dbt CLI or another method.

Please note that the above example assumes you have the necessary Dagster resources configured for BigQuery and dbt. The

bigquery_resource

is used to execute the query in BigQuery, and the

dbt_cli_resource

is a placeholder for how you might configure dbt within your Dagster environment. The

my_dbt_job

is a placeholder for your actual dbt job definition, which you would need to define separately. This job should be configured to run the dbt Cloud job using the dbt CLI or another method that is appropriate for your setup. To trigger a dbt Cloud job specifically

how can i trigger a dbt cloud job via dbt api from an dagster asset? @Scout

To trigger a dbt Cloud job from a Dagster asset, you would typically use the dbt Cloud API to start a job run. This involves making an HTTP POST request to the dbt Cloud API endpoint for triggering jobs. You would need to include the appropriate authorization header and payload in your request. Here's a conceptual example of how you might define a Dagster asset that triggers a dbt Cloud job using the

requests

library to make the API call. Please note that this example is for illustrative purposes, and you will need to adapt it to your specific requirements and environment.

python
import requests
from dagster import asset

# Define your dbt Cloud API configuration
DBT_CLOUD_API_URL = "<https://cloud.getdbt.com/api/v2/accounts/{account_id}/jobs/{job_id}/run/>"
DBT_CLOUD_ACCOUNT_ID = "your-dbt-cloud-account-id"
DBT_CLOUD_JOB_ID = "your-dbt-cloud-job-id"
DBT_CLOUD_API_TOKEN = "your-dbt-cloud-api-token"

# Define an asset that triggers a dbt Cloud job
@asset
def trigger_dbt_cloud_job(context):
    # Construct the API URL with the account and job IDs
    url = DBT_CLOUD_API_URL.format(account_id=DBT_CLOUD_ACCOUNT_ID, job_id=DBT_CLOUD_JOB_ID)
    
    # Set up the headers with the API token for authorization
    headers = {
        "Authorization": f"Token {DBT_CLOUD_API_TOKEN}",
        "Content-Type": "application/json"
    }
    
    # Make the POST request to trigger the dbt Cloud job
    response = <http://requests.post|requests.post>(url, headers=headers)
    
    # Check if the request was successful
    if response.status_code == 200:
        # Job triggered successfully, you can log or process the response as needed
        <http://context.log.info|context.log.info>("dbt Cloud job triggered successfully.")
        return response.json()
    else:
        # Handle errors
        context.log.error(f"Failed to trigger dbt Cloud job: {response.text}")
        raise Exception(f"Failed to trigger dbt Cloud job: {response.text}")

# Note: You will need to replace the placeholders like `your-dbt-cloud-account-id`,
# `your-dbt-cloud-job-id`, and `your-dbt-cloud-api-token` with your actual dbt Cloud account ID,
# job ID, and API token.

In this example, the

trigger_dbt_cloud_job

asset makes an HTTP POST request to the dbt Cloud API to trigger a job run. It uses the

requests

library to send the request with the necessary authorization header. Please ensure that you replace the placeholders with your actual dbt Cloud account ID, job ID, and API token. You will also need to handle the response from the API call appropriately, depending on whether the job was triggered successfully or if there was an error. Keep in mind that this asset should be part of a larger Dagster job or pipeline that includes any necessary dependencies and is scheduled or triggered as needed. Additionally, you should mana