cnolanminich

import dagster as dg 
from datetime import timedelta

# Upstream asset that should be fresh
@dg.asset
def upstream_asset(context: dg.AssetExecutionContext) -> None:
    context.log.info("Upstream asset is being computed")

@dg.asset
def downstream_asset(context: dg.AssetExecutionContext) -> None:

cnolanminich

pipeline {
    agent any

    environment {
        // Construct the IMAGE_TAG using Jenkins environment variables
        IMAGE_TAG = "${env.GIT_COMMIT}-${env.BUILD_ID}
        AWS_ACCESS_KEY_ID = credentials('aws-access-key-id')  // Reference to the AWS access key ID secret
        AWS_SECRET_ACCESS_KEY = credentials('aws-secret-access-key')  // Reference to the AWS secret access key secret
        AWS_REGION = 'us-west-2'  // Set your AWS region
    }

cnolanminich

from dagster import run_status_sensor, DagsterRunStatus, RunRequest, SkipReason, job, sensor, define_asset_job, SensorEvaluationContext, DagsterInstance, RunsFilter, Definitions, asset, AssetExecutionContext  
from datetime import datetime, timedelta
import json

@asset
def first_asset(context: AssetExecutionContext) -> None:
    context.log.info("First asset")

@asset
def second_asset(context: AssetExecutionContext) -> None:

cnolanminich

from dagster import Definitions, load_assets_from_modules, AssetSelection, define_asset_job, op, OpExecutionContext, job, asset, asset_check, ScheduleDefinition
import requests
from datetime import datetime


# can be modified to work with auth in dagster+
graphql_endpoint = "http://localhost:3000/graphql"  # Adjust the URL as needed
code_location_name = "turn_on_schedules_in_bulk"  # Adjust the code location name as needed

# utility function to get all schedules for a code location

cnolanminich

import boto3
import json
import time
from dagster import asset, AssetExecutionContext

@asset
def execute_step_function(context: AssetExecutionContext):
    # Initialize the boto3 client for Step Functions
    client = boto3.client('stepfunctions', region_name='us-west-2')

cnolanminich

query GetAssetDetails($assetKey: AssetKeyInput!) {
  assetNodeOrError(assetKey: $assetKey) {
    ... on AssetNode {
      assetKey {
        path
      }
      dataVersion
      description
      groupName
      hasAssetChecks

cnolanminich

import boto3
import time


# Define an asset that uses the custom resource
@asset
def sagemaker_pipeline() -> str:
  # note: the functions to connect and use Sagemaker would likely be put into a Dagster Resource
  # this is for illustration purposes for how you might integrate a system like Sagemaker into Dagster as simply as possible
  # Step 1: Set up the SageMaker session and client

cnolanminich

import dagster as dg

@dg.op
def first_op(context: dg.OpExecutionContext) -> None:
    context.log.info("Creating op one")

@dg.op(ins={"start": dg.In(dg.Nothing)})
def second_op(context: dg.OpExecutionContext) -> None:
    context.log.info("Creating op two")

cnolanminich

import pandas as pd
from dagster import asset, ResourceParam
from sqlalchemy import create_engine

# Define the PostgreSQL resource
class PostgresResource:
    def __init__(self, connection_string):
        self.connection_string = connection_string

    def get_connection(self):

cnolanminich

name: Dagster Cloud Hybrid Deployment
on:
  push: # For full deployment
    branches:
      - "main"
      - "master"
  pull_request:  # For branch deployments
     types: [opened, synchronize, reopened, closed]
  release:
    types: [published]