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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-45c26b2133","isDeveloperGuidesPage":false,"quickstartHeroFirstCertifiedTag":{"tagText":"Partner 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Hume"},"flexible_column_cont":{"id":"flexible-column-container-014c12550e","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-cceed9a3fc",":items":{"contentfragment":{"id":"contentfragment-1f717bf446","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/dagster_snowflake.png?v=db387812\" alt=\"Dagster and Snowflake\"\u003E\u003C/p\u003E\n","\u003Cp\u003EModern data teams face a common challenge: raw data arrives from external sources, needs to be loaded into Snowflake, transformed through multiple layers, and made available for analytics — all while maintaining clear visibility into what ran, when, and why.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://dagster.io\"\u003EDagster\u003C/a\u003E solves this by treating every data artifact — a Snowflake table, a dbt model, a file — as an \u003Ca href=\"https://docs.dagster.io/guides/build/assets\"\u003Easset\u003C/a\u003E. The Dagster asset graph becomes the source of truth for your entire pipeline. You can see every upstream dependency and every downstream consumer in one view, trigger any subset of the pipeline with a click, and know exactly what data exists in your warehouse at any moment.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, you will build a pipeline that loads NYC yellow taxi trip data from a public S3/CloudFront URL into Snowflake, transforms it through two \u003Ca href=\"https://www.getdbt.com/\"\u003Edbt\u003C/a\u003E model layers (staging and marts), and exposes the full lineage in the Dagster UI from raw parquet file through every transformation to the final analytics table.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003EThis guide assumes basic familiarity with Python, SQL, and the command line. No prior Dagster or dbt experience is required.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Dagster project using the \u003Ca href=\"https://docs.dagster.io/api/clis/cli\"\u003E\u003Ccode\u003Edg\u003C/code\u003E CLI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EHow to configure a dbt project as a \u003Ca href=\"https://docs.dagster.io/guides/build/components\"\u003EDagster component\u003C/a\u003E using YAML\u003C/li\u003E\u003Cli\u003EHow to write a Dagster Python asset that creates a Snowflake table\u003C/li\u003E\u003Cli\u003EHow dbt sources, dbt models, and Dagster Python assets connect in a single asset graph\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EA Snowflake Account\u003C/strong\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access to create roles, warehouses, and databases\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPython 3.10 or newer\u003C/strong\u003E installed on your machine\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Euv\u003C/strong\u003E — the Python package manager used by the Dagster \u003Ccode\u003Edg\u003C/code\u003E CLI. Install with:\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsSf https://astral.sh/uv/install.sh | sh\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA code editor\u003C/strong\u003E — \u003Ca href=\"https://code.visualstudio.com/\"\u003EVisual Studio Code\u003C/a\u003E is recommended\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFull end-to-end asset lineage in the Dagster UI from raw Snowflake table through all dbt transformations\u003C/li\u003E\u003Cli\u003EA daily schedule that refreshes the entire pipeline automatically\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet Up Snowflake\u003C/h2\u003E\n","\u003Cp\u003EBefore writing any orchestration code, create the Snowflake objects the pipeline will use: a dedicated database, two schemas (one for raw ingested data, one for dbt-managed objects), a virtual warehouse, and a role with the minimum necessary permissions.\u003C/p\u003E\n","\u003Cp\u003ELog into your Snowflake account as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, open a SQL worksheet, and run the following script in its entirety:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Database and schemas\nCREATE DATABASE IF NOT EXISTS TAXI_DATA;\nCREATE SCHEMA IF NOT EXISTS TAXI_DATA.RAW;\nCREATE SCHEMA IF NOT EXISTS TAXI_DATA.DBT;\n\n-- XSmall warehouse that auto-suspends after 2 minutes of inactivity\nCREATE WAREHOUSE IF NOT EXISTS TAXI_WH\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n       AUTO_SUSPEND    = 120\n       AUTO_RESUME     = TRUE\n       INITIALLY_SUSPENDED = TRUE;\n\n-- Dedicated role for Dagster\nCREATE ROLE IF NOT EXISTS DAGSTER_ROLE;\nGRANT USAGE  ON WAREHOUSE TAXI_WH              TO ROLE DAGSTER_ROLE;\nGRANT USAGE  ON DATABASE  TAXI_DATA            TO ROLE DAGSTER_ROLE;\nGRANT USAGE  ON SCHEMA    TAXI_DATA.RAW        TO ROLE DAGSTER_ROLE;\nGRANT USAGE  ON SCHEMA    TAXI_DATA.DBT        TO ROLE DAGSTER_ROLE;\nGRANT CREATE TABLE ON SCHEMA TAXI_DATA.RAW     TO ROLE DAGSTER_ROLE;\nGRANT CREATE TABLE ON SCHEMA TAXI_DATA.DBT     TO ROLE DAGSTER_ROLE;\nGRANT CREATE VIEW  ON SCHEMA TAXI_DATA.DBT     TO ROLE DAGSTER_ROLE;\n\n-- User for Dagster (replace <YOUR_PASSWORD> with a strong password)\nCREATE USER IF NOT EXISTS DAGSTER_USER\n  PASSWORD         = '<YOUR_PASSWORD>'\n  DEFAULT_ROLE     = DAGSTER_ROLE\n  DEFAULT_WAREHOUSE = TAXI_WH;\nGRANT ROLE DAGSTER_ROLE TO USER DAGSTER_USER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EKeep your Snowflake account identifier, username, and password handy for the \u003Ccode\u003E.env\u003C/code\u003E file you will create in the next section.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate the Dagster Project\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Edg\u003C/code\u003E CLI (part of the \u003Ccode\u003Edagster\u003C/code\u003E package, invoked via \u003Ccode\u003Euvx create-dagster\u003C/code\u003E) scaffolds a fully-structured Python project with all the configuration files Dagster needs.\u003C/p\u003E\n","\u003Cp\u003ERun the following commands to create the project, set up its environment, and add the Snowflake and dbt integration libraries:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Euvx create-dagster@latest project dagster-snowflake-tutorial\ncd dagster-snowflake-tutorial\nuv add dagster-snowflake dagster-dbt dbt-snowflake\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Edagster-snowflake\u003C/code\u003E provides the \u003Ccode\u003ESnowflakeResource\u003C/code\u003E used in the ingestion asset\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Edagster-dbt\u003C/code\u003E provides the \u003Ccode\u003EDbtProjectComponent\u003C/code\u003E that turns your dbt project into Dagster assets\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Edbt-snowflake\u003C/code\u003E is the dbt adapter that compiles the project manifest against Snowflake — without it, \u003Ccode\u003Edbt parse\u003C/code\u003E fails and no dbt assets load\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EActivate the virtual environment:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esource .venv/bin/activate\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EThe Generated Project Structure\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Ecreate-dagster\u003C/code\u003E command produces this layout:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E.\n├── pyproject.toml\n├── README.md\n├── src\n│   └── dagster_snowflake_tutorial\n│       ├── __init__.py\n│       ├── definitions.py\n│       └── defs\n│           └── __init__.py\n├── tests\n│   └── __init__.py\n└── uv.lock\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe critical file is \u003Ccode\u003Edefinitions.py\u003C/code\u003E, which contains the auto-discovery pattern:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom pathlib import Path\n\nfrom dagster import definitions, load_from_defs_folder\n\n\n@definitions\ndef defs():\n    return load_from_defs_folder(path_within_project=Path(__file__).parent)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis single file automatically discovers every asset, schedule, sensor, and component placed anywhere inside the \u003Ccode\u003Edefs/\u003C/code\u003E directory. You never need to manually import new definitions as the project evolves.\u003C/p\u003E\n","\u003Ch3\u003ESet Environment Variables\u003C/h3\u003E\n","\u003Cp\u003ECreate the \u003Ccode\u003E.env\u003C/code\u003E file at the project root:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003ESNOWFLAKE_ACCOUNT=<your-account-identifier>\nSNOWFLAKE_USER=DAGSTER_USER\nSNOWFLAKE_PASSWORD=<your-password>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E.env\u003C/code\u003E file is loaded automatically when you run \u003Ccode\u003Edg dev\u003C/code\u003E. It is already listed in \u003Ccode\u003E.gitignore\u003C/code\u003E by the project scaffold — never commit credentials to source control.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate the dbt Project\u003C/h2\u003E\n","\u003Cp\u003EThe dbt project lives inside the Dagster project directory, keeping everything in one repository.\u003C/p\u003E\n","\u003Ch3\u003EInitialize the dbt Project\u003C/h3\u003E\n","\u003Cp\u003EFrom inside \u003Ccode\u003Edagster-snowflake-tutorial/\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Euv run dbt init analytics --skip-profile-setup\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates an \u003Ccode\u003Eanalytics/\u003C/code\u003E directory at the project root. The \u003Ccode\u003E--skip-profile-setup\u003C/code\u003E flag skips the interactive profile wizard; you will configure the Snowflake profile manually below.\u003C/p\u003E\n","\u003Ch3\u003EConfigure the dbt Profile\u003C/h3\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003Eanalytics/profiles.yml\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eanalytics:\n  target: dev\n  outputs:\n    dev:\n      type: snowflake\n      account: "{{ env_var('SNOWFLAKE_ACCOUNT') }}"\n      user: "{{ env_var('SNOWFLAKE_USER') }}"\n      password: "{{ env_var('SNOWFLAKE_PASSWORD') }}"\n      role: DAGSTER_ROLE\n      warehouse: TAXI_WH\n      database: TAXI_DATA\n      schema: DBT\n      threads: 4\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUsing \u003Ccode\u003Eenv_var()\u003C/code\u003E keeps credentials out of version control.\u003C/p\u003E\n","\u003Ch3\u003EConfigure \u003Ccode\u003Edbt_project.yml\u003C/code\u003E\u003C/h3\u003E\n","\u003Cp\u003EReplace the generated content of \u003Ccode\u003Eanalytics/dbt_project.yml\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ename: analytics\nversion: "1.0.0"\nconfig-version: 2\nprofile: analytics\n\nmodel-paths:    ["models"]\nanalysis-paths: ["analyses"]\ntest-paths:     ["tests"]\nseed-paths:     ["seeds"]\nmacro-paths:    ["macros"]\nsnapshot-paths: ["snapshots"]\n\nmodels:\n  analytics:\n    staging:\n      +materialized: view\n      +schema: DBT\n    marts:\n      +materialized: table\n      +schema: DBT\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStaging models are materialized as views (fast to rebuild, no storage cost), while marts are materialized as tables (fast to query for downstream BI tools).\u003C/p\u003E\n","\u003Ch3\u003ECreate the dbt Model Files\u003C/h3\u003E\n","\u003Cp\u003EReplace the generated \u003Ccode\u003Emodels/example/\u003C/code\u003E directory and create the following structure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eanalytics/models/\n├── sources/\n│   └── raw_taxi.yml\n├── staging/\n│   ├── stg_taxi_trips.sql\n│   └── staging.yml\n└── marts/\n    ├── trip_metrics.sql\n    └── marts.yml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eanalytics/models/sources/raw_taxi.yml\u003C/code\u003E\u003C/strong\u003E — declares the Snowflake raw table as a dbt source. The \u003Ccode\u003Emeta\u003C/code\u003E tag connects it to the \u003Ccode\u003Etaxi_trips\u003C/code\u003E Dagster asset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eversion: 2\n\nsources:\n  - name: raw\n    database: TAXI_DATA\n    schema: RAW\n    tables:\n      - name: taxi_trips\n        description: "Raw NYC yellow taxi trip data loaded from S3/CloudFront by Dagster"\n        meta:\n          dagster:\n            asset_key: ["taxi_trips"]\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eanalytics/models/staging/stg_taxi_trips.sql\u003C/code\u003E\u003C/strong\u003E — cleans and renames the raw parquet columns:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith source as (\n    select * from {{ source('raw', 'taxi_trips') }}\n),\n\nrenamed as (\n    select\n        VendorID                                        as vendor_id,\n        tpep_pickup_datetime                            as pickup_at,\n        tpep_dropoff_datetime                           as dropoff_at,\n        passenger_count,\n        trip_distance,\n        PULocationID                                    as pickup_location_id,\n        DOLocationID                                    as dropoff_location_id,\n        payment_type,\n        fare_amount,\n        tip_amount,\n        total_amount,\n        datediff(\n            'minute',\n            tpep_pickup_datetime,\n            tpep_dropoff_datetime\n        )                                               as trip_duration_minutes\n    from source\n    where trip_distance > 0\n      and total_amount  > 0\n      and tpep_pickup_datetime >= '2023-01-01'\n      and tpep_pickup_datetime <  '2023-02-01'\n)\n\nselect * from renamed\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eanalytics/models/staging/staging.yml\u003C/code\u003E\u003C/strong\u003E — dbt documentation and data quality tests:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eversion: 2\n\nmodels:\n  - name: stg_taxi_trips\n    description: "Cleaned and renamed staging model for NYC yellow taxi trips"\n    config:\n      tags: ["staging"]\n    columns:\n      - name: pickup_at\n        description: "Trip pickup timestamp"\n        tests:\n          - not_null\n      - name: trip_distance\n        description: "Trip distance in miles"\n        tests:\n          - not_null\n      - name: total_amount\n        description: "Total fare charged to the passenger"\n        tests:\n          - not_null\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eanalytics/models/marts/trip_metrics.sql\u003C/code\u003E\u003C/strong\u003E — aggregated business-level mart with hourly trip statistics by pickup location:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith trips as (\n    select * from {{ ref('stg_taxi_trips') }}\n),\n\nmetrics as (\n    select\n        date_trunc('hour', pickup_at)       as pickup_hour,\n        pickup_location_id,\n        count(*)                            as trip_count,\n        avg(trip_distance)                  as avg_trip_distance,\n        avg(trip_duration_minutes)          as avg_trip_duration_minutes,\n        avg(total_amount)                   as avg_total_amount,\n        sum(total_amount)                   as total_revenue,\n        avg(tip_amount)                     as avg_tip_amount\n    from trips\n    group by 1, 2\n)\n\nselect * from metrics\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003Eanalytics/models/marts/marts.yml\u003C/code\u003E:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Eversion: 2\n\nmodels:\n  - name: trip_metrics\n    description: "Hourly trip metrics by pickup location — materialized as a Snowflake table"\n    config:\n      tags: ["marts"]\n    columns:\n      - name: pickup_hour\n        description: "Hour bucket of trip pickup time"\n        tests:\n          - not_null\n      - name: trip_count\n        description: "Number of trips in this hour and pickup location"\n        tests:\n          - not_null\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure the dbt Component\u003C/h2\u003E\n","\u003Cp\u003EA single \u003Ccode\u003Edg scaffold\u003C/code\u003E command generates a YAML configuration file for the dbt project. Dagster reads this YAML, runs \u003Ccode\u003Edbt parse\u003C/code\u003E to build the manifest, and automatically creates one Dagster asset for every dbt model and source in the project.\u003C/p\u003E\n","\u003Ch3\u003EScaffold the Component\u003C/h3\u003E\n","\u003Cp\u003EFrom inside \u003Ccode\u003Edagster-snowflake-tutorial/\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg scaffold defs dagster_dbt.DbtProjectComponent dbt_transforms --project-path analytics\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates \u003Ccode\u003Esrc/dagster_snowflake_tutorial/defs/dbt_transforms/defs.yaml\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Etype: dagster_dbt.DbtProjectComponent\n\nattributes:\n  project: "{{ context.project_root }}/analytics"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify the Component Loads\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg check defs\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf dbt parses successfully, all definitions are valid. Any syntax errors will surface here before you start the Dagster server.\u003C/p\u003E\n","\u003Cp\u003EThe dbt project, represented as Dagster assets, can be viewed in the Dagster UI.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg dev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Ehttp://localhost:3000\u003C/code\u003E to view your asset graph.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/graph_1.png?v=db387812\" alt=\"Dagster Asset Graph 1\"\u003E\u003C/p\u003E\n","\u003Cp\u003EMore information can be viewed about each individual asset.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/asset.png?v=db387812\" alt=\"Asset information\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis includes the dbt tests associated with each dbt model, represented as \u003Ca href=\"https://docs.dagster.io/guides/test/asset-checks\"\u003Easset checks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/asset_check.png?v=db387812\" alt=\"Asset Checks\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWrite the Ingestion Asset\u003C/h2\u003E\n","\u003Cp\u003EThe dbt component handles transformations, but raw data still needs to arrive in Snowflake before we can execute our pipeline. This section adds a Dagster Python asset that creates the raw \u003Ccode\u003ETAXI_TRIPS\u003C/code\u003E table and loads a month of NYC taxi parquet data from a public CloudFront URL using Snowflake's native \u003Ccode\u003ECOPY INTO\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Asset File\u003C/h3\u003E\n","\u003Cp\u003EScaffold the ingestion asset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg scaffold defs dagster.asset assets/ingestion.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace the contents of the generated file with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport dagster as dg\nfrom dagster_snowflake import SnowflakeResource\n\n\n@dg.asset(\n    group_name="raw",\n    description="Load January 2023 NYC yellow taxi trips from S3 into Snowflake",\n    kinds={"snowflake", "python"},\n)\ndef taxi_trips(snowflake: SnowflakeResource) -> dg.MaterializeResult:\n    with snowflake.get_connection() as conn:\n        cursor = conn.cursor()\n\n        cursor.execute("""\n            CREATE TABLE IF NOT EXISTS TAXI_DATA.RAW.TAXI_TRIPS (\n                VendorID              INTEGER,\n                tpep_pickup_datetime  TIMESTAMP,\n                tpep_dropoff_datetime TIMESTAMP,\n                passenger_count       FLOAT,\n                trip_distance         FLOAT,\n                RatecodeID            FLOAT,\n                store_and_fwd_flag    STRING,\n                PULocationID          INTEGER,\n                DOLocationID          INTEGER,\n                payment_type          INTEGER,\n                fare_amount           FLOAT,\n                extra                 FLOAT,\n                mta_tax               FLOAT,\n                tip_amount            FLOAT,\n                tolls_amount          FLOAT,\n                improvement_surcharge FLOAT,\n                total_amount          FLOAT,\n                congestion_surcharge  FLOAT,\n                airport_fee           FLOAT\n            )\n        """)\n\n        cursor.execute("TRUNCATE TABLE TAXI_DATA.RAW.TAXI_TRIPS")\n\n        cursor.execute("""\n            COPY INTO TAXI_DATA.RAW.TAXI_TRIPS\n            FROM 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet'\n            FILE_FORMAT = (\n                TYPE                = PARQUET\n                SNAPPY_COMPRESSION  = TRUE\n            )\n            MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE\n            ON_ERROR             = ABORT_STATEMENT\n        """)\n\n        cursor.execute("SELECT COUNT(*) FROM TAXI_DATA.RAW.TAXI_TRIPS")\n        rows_loaded = cursor.fetchone()[0]\n\n    return dg.MaterializeResult(\n        metadata={\n            "rows_loaded": dg.MetadataValue.int(rows_loaded),\n            "source_url": dg.MetadataValue.url(\n                "https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet"\n            ),\n        }\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA few things worth noting:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ECREATE TABLE IF NOT EXISTS\u003C/code\u003E\u003C/strong\u003E — the asset owns the schema of its output. The table is created on first run and left in place on subsequent runs.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ETRUNCATE\u003C/code\u003E before \u003Ccode\u003ECOPY INTO\u003C/code\u003E\u003C/strong\u003E — ensures the asset is idempotent. Re-materializing it always produces the same result.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EMaterializeResult\u003C/code\u003E with metadata\u003C/strong\u003E — the row count and source URL are recorded in the Dagster UI asset details panel after every run, giving you an audit trail without any custom logging.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Esnowflake: SnowflakeResource\u003C/code\u003E\u003C/strong\u003E — Dagster injects this resource at runtime. You declare the dependency; Dagster supplies it.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EConfigure the SnowflakeResource\u003C/h3\u003E\n","\u003Cp\u003EScaffold a resource to make the \u003Ccode\u003ESnowflakeResource\u003C/code\u003E available to all assets in the project.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg scaffold defs dagster.resources resources.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace the code at \u003Ccode\u003Esrc/dagster_snowflake_tutorial/defs/resources.py\u003C/code\u003E with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport dagster as dg\nfrom dagster_snowflake import SnowflakeResource\n\ndefs = dg.Definitions(\n    resources={\n        "snowflake": SnowflakeResource(\n            account=dg.EnvVar("SNOWFLAKE_ACCOUNT"),\n            user=dg.EnvVar("SNOWFLAKE_USER"),\n            password=dg.EnvVar("SNOWFLAKE_PASSWORD"),\n            warehouse="TAXI_WH",\n            database="TAXI_DATA",\n            schema="RAW",\n        )\n    }\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Edg.EnvVar\u003C/code\u003E reads environment variables at runtime rather than import time.\u003C/p\u003E\n","\u003Ch3\u003EView the Graph\u003C/h3\u003E\n","\u003Cp\u003EIf you have closed the UI, launch it again with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg dev\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe asset graph at \u003Ccode\u003Ehttp://localhost:3000\u003C/code\u003E now shows the ingestion asset alongside the dbt assets:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/graph_2.png?v=db387812\" alt=\"Dagster Asset Graph 2\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd a Daily Schedule\u003C/h2\u003E\n","\u003Cp\u003EDagster's schedule system triggers asset materializations on a cron expression. Create a daily schedule that refreshes the entire pipeline.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edg scaffold defs dagster.schedule schedules.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReplace the contents of \u003Ccode\u003Esrc/dagster_snowflake_tutorial/defs/schedules.py\u003C/code\u003E with a job and schedule:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport dagster as dg\n\ntaxi_pipeline_job = dg.define_asset_job(\n    name="taxi_pipeline_daily",\n    selection=dg.AssetSelection.all(),\n    description="Full refresh: ingest from S3, build staging view, build marts table",\n)\n\ndaily_schedule = dg.ScheduleDefinition(\n    name="taxi_pipeline_daily_schedule",\n    job=taxi_pipeline_job,\n    cron_schedule="0 6 * * *",\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERestart \u003Ccode\u003Edg dev\u003C/code\u003E and navigate to \u003Cstrong\u003EAutomation\u003C/strong\u003E. The \u003Ccode\u003Etaxi_pipeline_daily_schedule\u003C/code\u003E will be listed with its next scheduled run time.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/schedule.png?v=db387812\" alt=\"Schedule\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMaterialize the Pipeline\u003C/h2\u003E\n","\u003Cp\u003EWith the asset graph fully wired up, run the pipeline end-to-end. Dagster materializes assets in dependency order: first \u003Ccode\u003Etaxi_trips\u003C/code\u003E (which creates and loads the Snowflake table), then \u003Ccode\u003Estg_taxi_trips\u003C/code\u003E (which runs \u003Ccode\u003Edbt build\u003C/code\u003E for the staging model), then \u003Ccode\u003Etrip_metrics\u003C/code\u003E (which runs \u003Ccode\u003Edbt build\u003C/code\u003E for the marts model).\u003C/p\u003E\n","\u003Ch3\u003EMaterialize from the UI\u003C/h3\u003E\n","\u003Cp\u003EIn the Dagster asset graph view:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003EMaterialize all\u003C/strong\u003E in the top-right corner\u003C/li\u003E\u003Cli\u003EWatch the run progress — each node turns green as it completes\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EClick on any asset node to open the asset detail panel and see the run logs and metadata for that specific asset.\u003C/p\u003E\n","\u003Ch3\u003EVerify in Snowflake\u003C/h3\u003E\n","\u003Cp\u003ESwitch to your Snowflake worksheet and run the following queries to confirm data flowed through every layer:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Confirm raw data loaded (~3 million rows for January 2023)\nSELECT COUNT(*) FROM TAXI_DATA.RAW.TAXI_TRIPS;\n\n-- Inspect the staging view (cleaned column names, filtered rows)\nSELECT * FROM TAXI_DATA.DBT.STG_TAXI_TRIPS LIMIT 10;\n\n-- Query the final mart\nSELECT\n    pickup_hour,\n    SUM(trip_count)        AS total_trips,\n    AVG(avg_total_amount)  AS avg_fare,\n    SUM(total_revenue)     AS total_revenue\nFROM TAXI_DATA.DBT.TRIP_METRICS\nGROUP BY 1\nORDER BY 1\nLIMIT 24;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003EYou have built a production-ready data engineering pipeline using Dagster, dbt, and Snowflake. You started with raw parquet files on S3 and ended with a clean, tested, documented analytics mart in Snowflake with full lineage visible in the Dagster UI at every step.\u003C/p\u003E\n","\u003Ch3\u003EWhat You've Covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use \u003Ccode\u003Ecreate-dagster\u003C/code\u003E and the \u003Ccode\u003Edg\u003C/code\u003E CLI to scaffold a Dagster project with auto-discovery of definitions\u003C/li\u003E\u003Cli\u003EHow to configure a \u003Ccode\u003EDbtProjectComponent\u003C/code\u003E with a YAML file to connect your dbt project to Dagster\u003C/li\u003E\u003Cli\u003EHow to write a Dagster Python asset that creates a Snowflake table and idempotently loads data via \u003Ccode\u003ECOPY INTO\u003C/code\u003E, with row count metadata surfaced in the UI\u003C/li\u003E\u003Cli\u003EHow to add a daily schedule with a single \u003Ccode\u003EScheduleDefinition\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to materialize the full pipeline from the Dagster UI and verify results in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAdditional Resources:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.dagster.io/integrations/libraries/dbt\"\u003Edagster-dbt integration reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.dagster.io/integrations/libraries/snowflake\"\u003Edagster-snowflake integration reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page\"\u003ENYC TLC Trip Record Data\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://dagster.io/slack\"\u003EDagster community Slack\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Data Engineering with Dagster, dbt, and Snowflake","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\n![Dagster and Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/dagster_snowflake.png?v=db387812)\n\nModern data teams face a common challenge: raw data arrives from external sources, needs to be loaded into Snowflake, transformed through multiple layers, and made available for analytics — all while maintaining clear visibility into what ran, when, and why.\n\n[Dagster](https://dagster.io) solves this by treating every data artifact — a Snowflake table, a dbt model, a file — as an [asset](https://docs.dagster.io/guides/build/assets). The Dagster asset graph becomes the source of truth for your entire pipeline. You can see every upstream dependency and every downstream consumer in one view, trigger any subset of the pipeline with a click, and know exactly what data exists in your warehouse at any moment.\n\nIn this guide, you will build a pipeline that loads NYC yellow taxi trip data from a public S3/CloudFront URL into Snowflake, transforms it through two [dbt](https://www.getdbt.com/) model layers (staging and marts), and exposes the full lineage in the Dagster UI from raw parquet file through every transformation to the final analytics table.\n\n### Prerequisites\nThis guide assumes basic familiarity with Python, SQL, and the command line. No prior Dagster or dbt experience is required.\n\n### What You'll Learn\n- How to create a Dagster project using the [`dg` CLI](https://docs.dagster.io/api/clis/cli)\n- How to configure a dbt project as a [Dagster component](https://docs.dagster.io/guides/build/components) using YAML\n- How to write a Dagster Python asset that creates a Snowflake table\n- How dbt sources, dbt models, and Dagster Python assets connect in a single asset graph\n\n### What You'll Need\n1. **A Snowflake Account** with `ACCOUNTADMIN` access to create roles, warehouses, and databases\n2. **Python 3.10 or newer** installed on your machine\n3. **uv** — the Python package manager used by the Dagster `dg` CLI. Install with:\n   ```bash\n   curl -LsSf https://astral.sh/uv/install.sh | sh\n   ```\n4. **A code editor** — [Visual Studio Code](https://code.visualstudio.com/) is recommended\n\n### What You'll Build\n- Full end-to-end asset lineage in the Dagster UI from raw Snowflake table through all dbt transformations\n- A daily schedule that refreshes the entire pipeline automatically\n\n\u003C!-- ------------------------ --\u003E\n## Set Up Snowflake\n\nBefore writing any orchestration code, create the Snowflake objects the pipeline will use: a dedicated database, two schemas (one for raw ingested data, one for dbt-managed objects), a virtual warehouse, and a role with the minimum necessary permissions.\n\nLog into your Snowflake account as `ACCOUNTADMIN`, open a SQL worksheet, and run the following script in its entirety:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Database and schemas\nCREATE DATABASE IF NOT EXISTS TAXI_DATA;\nCREATE SCHEMA IF NOT EXISTS TAXI_DATA.RAW;\nCREATE SCHEMA IF NOT EXISTS TAXI_DATA.DBT;\n\n-- XSmall warehouse that auto-suspends after 2 minutes of inactivity\nCREATE WAREHOUSE IF NOT EXISTS TAXI_WH\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n       AUTO_SUSPEND    = 120\n       AUTO_RESUME     = TRUE\n       INITIALLY_SUSPENDED = TRUE;\n\n-- Dedicated role for Dagster\nCREATE ROLE IF NOT EXISTS DAGSTER_ROLE;\nGRANT USAGE  ON WAREHOUSE TAXI_WH              TO ROLE DAGSTER_ROLE;\nGRANT USAGE  ON DATABASE  TAXI_DATA            TO ROLE DAGSTER_ROLE;\nGRANT USAGE  ON SCHEMA    TAXI_DATA.RAW        TO ROLE DAGSTER_ROLE;\nGRANT USAGE  ON SCHEMA    TAXI_DATA.DBT        TO ROLE DAGSTER_ROLE;\nGRANT CREATE TABLE ON SCHEMA TAXI_DATA.RAW     TO ROLE DAGSTER_ROLE;\nGRANT CREATE TABLE ON SCHEMA TAXI_DATA.DBT     TO ROLE DAGSTER_ROLE;\nGRANT CREATE VIEW  ON SCHEMA TAXI_DATA.DBT     TO ROLE DAGSTER_ROLE;\n\n-- User for Dagster (replace \u003CYOUR_PASSWORD\u003E with a strong password)\nCREATE USER IF NOT EXISTS DAGSTER_USER\n  PASSWORD         = '\u003CYOUR_PASSWORD\u003E'\n  DEFAULT_ROLE     = DAGSTER_ROLE\n  DEFAULT_WAREHOUSE = TAXI_WH;\nGRANT ROLE DAGSTER_ROLE TO USER DAGSTER_USER;\n```\n\nKeep your Snowflake account identifier, username, and password handy for the `.env` file you will create in the next section.\n\n\u003C!-- ------------------------ --\u003E\n## Create the Dagster Project\n\nThe `dg` CLI (part of the `dagster` package, invoked via `uvx create-dagster`) scaffolds a fully-structured Python project with all the configuration files Dagster needs.\n\nRun the following commands to create the project, set up its environment, and add the Snowflake and dbt integration libraries:\n\n```bash\nuvx create-dagster@latest project dagster-snowflake-tutorial\ncd dagster-snowflake-tutorial\nuv add dagster-snowflake dagster-dbt dbt-snowflake\n```\n\n- `dagster-snowflake` provides the `SnowflakeResource` used in the ingestion asset\n- `dagster-dbt` provides the `DbtProjectComponent` that turns your dbt project into Dagster assets\n- `dbt-snowflake` is the dbt adapter that compiles the project manifest against Snowflake — without it, `dbt parse` fails and no dbt assets load\n\nActivate the virtual environment:\n\n```bash\nsource .venv/bin/activate\n```\n\n### The Generated Project Structure\n\nThe `create-dagster` command produces this layout:\n\n```\n.\n├── pyproject.toml\n├── README.md\n├── src\n│   └── dagster_snowflake_tutorial\n│       ├── __init__.py\n│       ├── definitions.py\n│       └── defs\n│           └── __init__.py\n├── tests\n│   └── __init__.py\n└── uv.lock\n```\n\nThe critical file is `definitions.py`, which contains the auto-discovery pattern:\n\n```python\nfrom pathlib import Path\n\nfrom dagster import definitions, load_from_defs_folder\n\n\n@definitions\ndef defs():\n    return load_from_defs_folder(path_within_project=Path(__file__).parent)\n```\n\nThis single file automatically discovers every asset, schedule, sensor, and component placed anywhere inside the `defs/` directory. You never need to manually import new definitions as the project evolves.\n\n### Set Environment Variables\n\nCreate the `.env` file at the project root:\n\n```bash\nSNOWFLAKE_ACCOUNT=\u003Cyour-account-identifier\u003E\nSNOWFLAKE_USER=DAGSTER_USER\nSNOWFLAKE_PASSWORD=\u003Cyour-password\u003E\n```\n\nThe `.env` file is loaded automatically when you run `dg dev`. It is already listed in `.gitignore` by the project scaffold — never commit credentials to source control.\n\n\u003C!-- ------------------------ --\u003E\n## Create the dbt Project\n\nThe dbt project lives inside the Dagster project directory, keeping everything in one repository.\n\n### Initialize the dbt Project\n\nFrom inside `dagster-snowflake-tutorial/`:\n\n```bash\nuv run dbt init analytics --skip-profile-setup\n```\n\nThis creates an `analytics/` directory at the project root. The `--skip-profile-setup` flag skips the interactive profile wizard; you will configure the Snowflake profile manually below.\n\n### Configure the dbt Profile\n\nCreate `analytics/profiles.yml`:\n\n```yaml\nanalytics:\n  target: dev\n  outputs:\n    dev:\n      type: snowflake\n      account: \"{{ env_var('SNOWFLAKE_ACCOUNT') }}\"\n      user: \"{{ env_var('SNOWFLAKE_USER') }}\"\n      password: \"{{ env_var('SNOWFLAKE_PASSWORD') }}\"\n      role: DAGSTER_ROLE\n      warehouse: TAXI_WH\n      database: TAXI_DATA\n      schema: DBT\n      threads: 4\n```\n\nUsing `env_var()` keeps credentials out of version control.\n\n### Configure `dbt_project.yml`\n\nReplace the generated content of `analytics/dbt_project.yml`:\n\n```yaml\nname: analytics\nversion: \"1.0.0\"\nconfig-version: 2\nprofile: analytics\n\nmodel-paths:    [\"models\"]\nanalysis-paths: [\"analyses\"]\ntest-paths:     [\"tests\"]\nseed-paths:     [\"seeds\"]\nmacro-paths:    [\"macros\"]\nsnapshot-paths: [\"snapshots\"]\n\nmodels:\n  analytics:\n    staging:\n      +materialized: view\n      +schema: DBT\n    marts:\n      +materialized: table\n      +schema: DBT\n```\n\nStaging models are materialized as views (fast to rebuild, no storage cost), while marts are materialized as tables (fast to query for downstream BI tools).\n\n### Create the dbt Model Files\n\nReplace the generated `models/example/` directory and create the following structure:\n\n```\nanalytics/models/\n├── sources/\n│   └── raw_taxi.yml\n├── staging/\n│   ├── stg_taxi_trips.sql\n│   └── staging.yml\n└── marts/\n    ├── trip_metrics.sql\n    └── marts.yml\n```\n\n**`analytics/models/sources/raw_taxi.yml`** — declares the Snowflake raw table as a dbt source. The `meta` tag connects it to the `taxi_trips` Dagster asset:\n\n```yaml\nversion: 2\n\nsources:\n  - name: raw\n    database: TAXI_DATA\n    schema: RAW\n    tables:\n      - name: taxi_trips\n        description: \"Raw NYC yellow taxi trip data loaded from S3/CloudFront by Dagster\"\n        meta:\n          dagster:\n            asset_key: [\"taxi_trips\"]\n```\n\n**`analytics/models/staging/stg_taxi_trips.sql`** — cleans and renames the raw parquet columns:\n\n```sql\nwith source as (\n    select * from {{ source('raw', 'taxi_trips') }}\n),\n\nrenamed as (\n    select\n        VendorID                                        as vendor_id,\n        tpep_pickup_datetime                            as pickup_at,\n        tpep_dropoff_datetime                           as dropoff_at,\n        passenger_count,\n        trip_distance,\n        PULocationID                                    as pickup_location_id,\n        DOLocationID                                    as dropoff_location_id,\n        payment_type,\n        fare_amount,\n        tip_amount,\n        total_amount,\n        datediff(\n            'minute',\n            tpep_pickup_datetime,\n            tpep_dropoff_datetime\n        )                                               as trip_duration_minutes\n    from source\n    where trip_distance \u003E 0\n      and total_amount  \u003E 0\n      and tpep_pickup_datetime \u003E= '2023-01-01'\n      and tpep_pickup_datetime \u003C  '2023-02-01'\n)\n\nselect * from renamed\n```\n\n**`analytics/models/staging/staging.yml`** — dbt documentation and data quality tests:\n\n```yaml\nversion: 2\n\nmodels:\n  - name: stg_taxi_trips\n    description: \"Cleaned and renamed staging model for NYC yellow taxi trips\"\n    config:\n      tags: [\"staging\"]\n    columns:\n      - name: pickup_at\n        description: \"Trip pickup timestamp\"\n        tests:\n          - not_null\n      - name: trip_distance\n        description: \"Trip distance in miles\"\n        tests:\n          - not_null\n      - name: total_amount\n        description: \"Total fare charged to the passenger\"\n        tests:\n          - not_null\n```\n\n**`analytics/models/marts/trip_metrics.sql`** — aggregated business-level mart with hourly trip statistics by pickup location:\n\n```sql\nwith trips as (\n    select * from {{ ref('stg_taxi_trips') }}\n),\n\nmetrics as (\n    select\n        date_trunc('hour', pickup_at)       as pickup_hour,\n        pickup_location_id,\n        count(*)                            as trip_count,\n        avg(trip_distance)                  as avg_trip_distance,\n        avg(trip_duration_minutes)          as avg_trip_duration_minutes,\n        avg(total_amount)                   as avg_total_amount,\n        sum(total_amount)                   as total_revenue,\n        avg(tip_amount)                     as avg_tip_amount\n    from trips\n    group by 1, 2\n)\n\nselect * from metrics\n```\n\n**`analytics/models/marts/marts.yml`:**\n\n```yaml\nversion: 2\n\nmodels:\n  - name: trip_metrics\n    description: \"Hourly trip metrics by pickup location — materialized as a Snowflake table\"\n    config:\n      tags: [\"marts\"]\n    columns:\n      - name: pickup_hour\n        description: \"Hour bucket of trip pickup time\"\n        tests:\n          - not_null\n      - name: trip_count\n        description: \"Number of trips in this hour and pickup location\"\n        tests:\n          - not_null\n```\n\n\u003C!-- ------------------------ --\u003E\n## Configure the dbt Component\n\nA single `dg scaffold` command generates a YAML configuration file for the dbt project. Dagster reads this YAML, runs `dbt parse` to build the manifest, and automatically creates one Dagster asset for every dbt model and source in the project.\n\n### Scaffold the Component\n\nFrom inside `dagster-snowflake-tutorial/`:\n\n```bash\ndg scaffold defs dagster_dbt.DbtProjectComponent dbt_transforms --project-path analytics\n```\n\nThis creates `src/dagster_snowflake_tutorial/defs/dbt_transforms/defs.yaml`.\n\n```yaml\ntype: dagster_dbt.DbtProjectComponent\n\nattributes:\n  project: \"{{ context.project_root }}/analytics\"\n```\n\n### Verify the Component Loads\n\n```bash\ndg check defs\n```\n\nIf dbt parses successfully, all definitions are valid. Any syntax errors will surface here before you start the Dagster server.\n\nThe dbt project, represented as Dagster assets, can be viewed in the Dagster UI.\n\n```bash\ndg dev\n```\n\nOpen `http://localhost:3000` to view your asset graph.\n\n![Dagster Asset Graph 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/graph_1.png?v=db387812)\n\nMore information can be viewed about each individual asset.\n\n![Asset information](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/asset.png?v=db387812)\n\nThis includes the dbt tests associated with each dbt model, represented as [asset checks](https://docs.dagster.io/guides/test/asset-checks).\n\n![Asset Checks](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/asset_check.png?v=db387812)\n\n\u003C!-- ------------------------ --\u003E\n## Write the Ingestion Asset\n\nThe dbt component handles transformations, but raw data still needs to arrive in Snowflake before we can execute our pipeline. This section adds a Dagster Python asset that creates the raw `TAXI_TRIPS` table and loads a month of NYC taxi parquet data from a public CloudFront URL using Snowflake's native `COPY INTO`.\n\n### Create the Asset File\n\nScaffold the ingestion asset:\n\n```bash\ndg scaffold defs dagster.asset assets/ingestion.py\n```\n\nReplace the contents of the generated file with:\n\n```python\nimport dagster as dg\nfrom dagster_snowflake import SnowflakeResource\n\n\n@dg.asset(\n    group_name=\"raw\",\n    description=\"Load January 2023 NYC yellow taxi trips from S3 into Snowflake\",\n    kinds={\"snowflake\", \"python\"},\n)\ndef taxi_trips(snowflake: SnowflakeResource) -\u003E dg.MaterializeResult:\n    with snowflake.get_connection() as conn:\n        cursor = conn.cursor()\n\n        cursor.execute(\"\"\"\n            CREATE TABLE IF NOT EXISTS TAXI_DATA.RAW.TAXI_TRIPS (\n                VendorID              INTEGER,\n                tpep_pickup_datetime  TIMESTAMP,\n                tpep_dropoff_datetime TIMESTAMP,\n                passenger_count       FLOAT,\n                trip_distance         FLOAT,\n                RatecodeID            FLOAT,\n                store_and_fwd_flag    STRING,\n                PULocationID          INTEGER,\n                DOLocationID          INTEGER,\n                payment_type          INTEGER,\n                fare_amount           FLOAT,\n                extra                 FLOAT,\n                mta_tax               FLOAT,\n                tip_amount            FLOAT,\n                tolls_amount          FLOAT,\n                improvement_surcharge FLOAT,\n                total_amount          FLOAT,\n                congestion_surcharge  FLOAT,\n                airport_fee           FLOAT\n            )\n        \"\"\")\n\n        cursor.execute(\"TRUNCATE TABLE TAXI_DATA.RAW.TAXI_TRIPS\")\n\n        cursor.execute(\"\"\"\n            COPY INTO TAXI_DATA.RAW.TAXI_TRIPS\n            FROM 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet'\n            FILE_FORMAT = (\n                TYPE                = PARQUET\n                SNAPPY_COMPRESSION  = TRUE\n            )\n            MATCH_BY_COLUMN_NAME = CASE_INSENSITIVE\n            ON_ERROR             = ABORT_STATEMENT\n        \"\"\")\n\n        cursor.execute(\"SELECT COUNT(*) FROM TAXI_DATA.RAW.TAXI_TRIPS\")\n        rows_loaded = cursor.fetchone()[0]\n\n    return dg.MaterializeResult(\n        metadata={\n            \"rows_loaded\": dg.MetadataValue.int(rows_loaded),\n            \"source_url\": dg.MetadataValue.url(\n                \"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet\"\n            ),\n        }\n    )\n```\n\nA few things worth noting:\n\n- **`CREATE TABLE IF NOT EXISTS`** — the asset owns the schema of its output. The table is created on first run and left in place on subsequent runs.\n- **`TRUNCATE` before `COPY INTO`** — ensures the asset is idempotent. Re-materializing it always produces the same result.\n- **`MaterializeResult` with metadata** — the row count and source URL are recorded in the Dagster UI asset details panel after every run, giving you an audit trail without any custom logging.\n- **`snowflake: SnowflakeResource`** — Dagster injects this resource at runtime. You declare the dependency; Dagster supplies it.\n\n### Configure the SnowflakeResource\n\nScaffold a resource to make the `SnowflakeResource` available to all assets in the project.\n\n```bash\ndg scaffold defs dagster.resources resources.py\n```\n\nReplace the code at `src/dagster_snowflake_tutorial/defs/resources.py` with:\n\n```python\nimport dagster as dg\nfrom dagster_snowflake import SnowflakeResource\n\ndefs = dg.Definitions(\n    resources={\n        \"snowflake\": SnowflakeResource(\n            account=dg.EnvVar(\"SNOWFLAKE_ACCOUNT\"),\n            user=dg.EnvVar(\"SNOWFLAKE_USER\"),\n            password=dg.EnvVar(\"SNOWFLAKE_PASSWORD\"),\n            warehouse=\"TAXI_WH\",\n            database=\"TAXI_DATA\",\n            schema=\"RAW\",\n        )\n    }\n)\n```\n\n`dg.EnvVar` reads environment variables at runtime rather than import time.\n\n### View the Graph\n\nIf you have closed the UI, launch it again with:\n\n```bash\ndg dev\n```\n\nThe asset graph at `http://localhost:3000` now shows the ingestion asset alongside the dbt assets:\n\n![Dagster Asset Graph 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/graph_2.png?v=db387812)\n\n\u003C!-- ------------------------ --\u003E\n## Add a Daily Schedule\n\nDagster's schedule system triggers asset materializations on a cron expression. Create a daily schedule that refreshes the entire pipeline.\n\n```bash\ndg scaffold defs dagster.schedule schedules.py\n```\n\nReplace the contents of `src/dagster_snowflake_tutorial/defs/schedules.py` with a job and schedule:\n\n```python\nimport dagster as dg\n\ntaxi_pipeline_job = dg.define_asset_job(\n    name=\"taxi_pipeline_daily\",\n    selection=dg.AssetSelection.all(),\n    description=\"Full refresh: ingest from S3, build staging view, build marts table\",\n)\n\ndaily_schedule = dg.ScheduleDefinition(\n    name=\"taxi_pipeline_daily_schedule\",\n    job=taxi_pipeline_job,\n    cron_schedule=\"0 6 * * *\",\n)\n```\n\nRestart `dg dev` and navigate to **Automation**. The `taxi_pipeline_daily_schedule` will be listed with its next scheduled run time.\n\n![Schedule](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-dagster-dbt-snowflake/schedule.png?v=db387812)\n\n\u003C!-- ------------------------ --\u003E\n## Materialize the Pipeline\n\nWith the asset graph fully wired up, run the pipeline end-to-end. Dagster materializes assets in dependency order: first `taxi_trips` (which creates and loads the Snowflake table), then `stg_taxi_trips` (which runs `dbt build` for the staging model), then `trip_metrics` (which runs `dbt build` for the marts model).\n\n### Materialize from the UI\n\nIn the Dagster asset graph view:\n\n1. Click **Materialize all** in the top-right corner\n2. Watch the run progress — each node turns green as it completes\n\nClick on any asset node to open the asset detail panel and see the run logs and metadata for that specific asset.\n\n### Verify in Snowflake\n\nSwitch to your Snowflake worksheet and run the following queries to confirm data flowed through every layer:\n\n```sql\n-- Confirm raw data loaded (~3 million rows for January 2023)\nSELECT COUNT(*) FROM TAXI_DATA.RAW.TAXI_TRIPS;\n\n-- Inspect the staging view (cleaned column names, filtered rows)\nSELECT * FROM TAXI_DATA.DBT.STG_TAXI_TRIPS LIMIT 10;\n\n-- Query the final mart\nSELECT\n    pickup_hour,\n    SUM(trip_count)        AS total_trips,\n    AVG(avg_total_amount)  AS avg_fare,\n    SUM(total_revenue)     AS total_revenue\nFROM TAXI_DATA.DBT.TRIP_METRICS\nGROUP BY 1\nORDER BY 1\nLIMIT 24;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nYou have built a production-ready data engineering pipeline using Dagster, dbt, and Snowflake. You started with raw parquet files on S3 and ended with a clean, tested, documented analytics mart in Snowflake with full lineage visible in the Dagster UI at every step.\n\n### What You've Covered\n- How to use `create-dagster` and the `dg` CLI to scaffold a Dagster project with auto-discovery of definitions\n- How to configure a `DbtProjectComponent` with a YAML file to connect your dbt project to Dagster\n- How to write a Dagster Python asset that creates a Snowflake table and idempotently loads data via `COPY INTO`, with row count metadata surfaced in the UI\n- How to add a daily schedule with a single `ScheduleDefinition`\n- How to materialize the full pipeline from the Dagster UI and verify results in Snowflake\n\n### Additional Resources:\n- [dagster-dbt integration reference](https://docs.dagster.io/integrations/libraries/dbt)\n- [dagster-snowflake integration reference](https://docs.dagster.io/integrations/libraries/snowflake)\n- [NYC TLC Trip Record Data](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page)\n- [Dagster community Slack](https://dagster.io/slack)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-50205eab4c","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-2ece8b390e",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-8b6f5b579b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-8de2aec553","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-9def0935ee",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-6ab180c76f",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-f3734fd495","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-c46a7222c0","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/data-engineering-with-dagster-dbt-snowflake",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESet 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