Build your first Dagster pipeline

Welcome to Dagster! In this guide, we'll cover:

Setting up a basic Dagster project
Creating a single Dagster asset that encapsulates the entire Extract, Transform, and Load (ETL) process
Using Dagster's UI to monitor and execute your pipeline

Prerequisites

Python 3.9+
If using uv as your package manager, you will need to install uv (Recommended).
If using pip as your package manager, you will need to install the create-dagster CLI with Homebrew, curl, or pip.

For detailed instructions, see the Installation guide.

Step 1: Scaffold a new Dagster project

Open your terminal and scaffold a new Dagster project:
```
uvx -U create-dagster project dagster-quickstart
```
Respond y to the prompt to run uv sync after scaffolding
Change to the dagster-quickstart directory:
```
cd dagster-quickstart
```
Activate the virtual environment:
- MacOS/Unix
- Windows
source .venv/bin/activate
.venv\Scripts\activate
Install the required dependencies in the virtual environment:
```
uv pip install pandas
```

Open your terminal and scaffold a new Dagster project:
```
create-dagster project dagster-quickstart
```
Change to the dagster-quickstart directory:
```
cd dagster-quickstart
```

Create and activate a virtual environment:

MacOS/Unix
Windows

python -m venv .venv

source .venv/bin/activate

python -m venv .venv

.venv\Scripts\activate

Install the required dependencies:
```
pip install pandas
```
Install your project as an editable package:
```
pip install --editable .
```

Your new Dagster project should have the following structure:

.
└── dagster-quickstart
   ├── pyproject.toml
   ├── src
   │   └── dagster_quickstart
   │       ├── __init__.py
   │       ├── definitions.py
   │       └── defs
   │           └── __init__.py
   ├── tests
   │   └── __init__.py
   └── uv.lock

.
└── dagster-quickstart
   ├── pyproject.toml
   ├── src
   │   └── dagster_quickstart
   │       ├── __init__.py
   │       ├── definitions.py
   │       └── defs
   │           └── __init__.py
   └── tests
      └── __init__.py

Step 2: Scaffold an assets file

Use the dg scaffold defs command to generate an assets file on the command line:

dg scaffold defs dagster.asset assets.py

This will add a new file assets.py to the defs directory:

src
└── dagster_quickstart
   ├── __init__.py
   └── defs
      ├── __init__.py
      └── assets.py

Step 3: Add data

Next, create a sample_data.csv file. This file will act as the data source for your Dagster pipeline:

mkdir src/dagster_quickstart/defs/data && touch src/dagster_quickstart/defs/data/sample_data.csv

In your preferred editor, copy the following data into this file:

src/dagster_quickstart/defs/data/sample_data.csv
id,name,age,city
1,Alice,28,New York
2,Bob,35,San Francisco
3,Charlie,42,Chicago
4,Diana,31,Los Angeles

Step 4: Define the asset

To define the assets for the ETL pipeline, open src/dagster_quickstart/defs/assets.py file in your preferred editor and copy in the following code:

src/dagster_quickstart/defs/assets.py
import pandas as pd

import dagster as dg

sample_data_file = "src/dagster_quickstart/defs/data/sample_data.csv"
processed_data_file = "src/dagster_quickstart/defs/data/processed_data.csv"


@dg.asset
def processed_data():
    ## Read data from the CSV
    df = pd.read_csv(sample_data_file)

    ## Add an age_group column based on the value of age
    df["age_group"] = pd.cut(
        df["age"], bins=[0, 30, 40, 100], labels=["Young", "Middle", "Senior"]
    )

    ## Save processed data
    df.to_csv(processed_data_file, index=False)
    return "Data loaded successfully"

At this point, you can list the Dagster definitions in your project with dg list defs. You should see the asset you just created:

dg list defs

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Section ┃ Definitions                                               ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Assets  │ ┏━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┓ │
│         │ ┃ Key            ┃ Group   ┃ Deps ┃ Kinds ┃ Description ┃ │
│         │ ┡━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━┩ │
│         │ │ processed_data │ default │      │       │             │ │
│         │ └────────────────┴─────────┴──────┴───────┴─────────────┘ │
└─────────┴───────────────────────────────────────────────────────────┘

You can also load and validate your Dagster definitions with dg check defs:

dg check defs

All components validated successfully.
All definitions loaded successfully.

Step 5: Run the pipeline

In the terminal, navigate to your project's root directory and run:
```
dg dev
```
Open your web browser and navigate to http://localhost:3000, where you should see the Dagster UI:
In the top navigation, click Assets > View lineage.
Click Materialize to run the pipeline.
In the popup, click View. This will open the Run details page, allowing you to view the run as it executes.

Use the view buttons in near the top left corner of the page to change how the run is displayed. You can also click the asset to view logs and metadata.

tip

You can also run the pipeline by using the dg launch --assets command and passing an asset selection:

dg launch --assets "*"

Step 6: Verify the results

In your terminal, run:

cat src/dagster_quickstart/defs/data/processed_data.csv

You should see the transformed data, including the new age_group column:

id,name,age,city,age_group
1,Alice,28,New York,Young
2,Bob,35,San Francisco,Middle
3,Charlie,42,Chicago,Senior
4,Diana,31,Los Angeles,Middle

Next steps

Congratulations! You've just built and run your first pipeline with Dagster. Next, you can:

Continue with the ETL pipeline tutorial to learn how to build a more complex ETL pipeline
Create your own Dagster project and add assets to it

Step 1: Scaffold a new Dagster project​

Step 2: Scaffold an assets file​

Step 3: Add data​

Step 4: Define the asset​

Step 5: Run the pipeline​

Step 6: Verify the results​

Next steps​