Develop and deploy your first Dagster pipeline

Welcome to Dagster+ Serverless! In this guide, we'll cover developing and testing your Dagster project locally, using branch deployments to safely test against production data, and finally, pushing your changes to production.

Dagster OSS and Dagster+ Hybrid users

To get started with Dagster+ Hybrid or Dagster OSS, see the Dagster+ Hybrid and OSS quickstart guide instead.

Prerequisites

To follow the steps in this guide, you will need to:

• Sign up for a Dagster+ Serverless account and create a Dagster project
• Clone your Dagster project to your local machine
• Ensure you have Python 3.9+ installed

Step 1: Install uv (Recommended)

note

If you will be using pip, skip to step 2.

If you will be using uv as your package manager, follow the steps below to install the Python package manager uv:

Mac

brew install uv

Windows

powershell -ExecutionPolicy ByPass -c 'irm https://astral.sh/uv/install.ps1 | iex'

Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Install project dependencies

uv

1. Change to the directory of your cloned project:

   cd <project-directory>

2. Activate the virtual environment:

   MacOS/Unix

   source .venv/bin/activate

   Windows

   .venv\Scripts\activate

3. Install required dependencies in the virtual environment:

   uv sync

pip

1. Change to the directory of your cloned project:

   cd <project-directory>

2. Create and activate a virtual environment:

   MacOS/Unix

   python -m venv .venv

   source .venv/bin/activate

   Windows

   python -m venv .venv

   .venv\Scripts\activate

3. Install your project as an editable package:

   pip install --editable .

Step 3: Run your pipeline

1. In the terminal, navigate to your project's root directory and run:

   dg dev

2. Open your web browser and navigate to http://localhost:3000, where you should see the Dagster UI:

   

3. In the top navigation, click the Assets tab, then click View lineage:

   

4. To run the pipeline, click Materialize all:

   

5. To view the run as it executes, click the Runs tab, then on the right side of the page, click View:

   

   To change how the run is displayed, you can use the view buttons in the top left corner of the page:

   

tip

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

dg launch --assets "*"

Step 4: Develop and test your pipeline

To develop your pipeline, you can:

• Update and create assets
• Automate your pipeline
• Add integrations

To test your pipeline, you can:

• Run dg check defs to check Dagster definitions
• Run dg dev to start the local webserver and run your pipeline in the UI
• Add asset checks
• Debug assets during execution with pdb

Step 5: Deploy to staging with branch deployments

To see how your changes will look in production without altering production data, you can use branch deployments. To get started, make your changes in a Git branch and create a pull request, then select your branch from the deployment dropdown in the Dagster+ Serverless UI:

Step 6: Deploy to production

Once you are satisfied with your changes, you can merge your branch into main and the changes will be deployed to your production Serverless deployment.

Next steps

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

• Follow the Dagster Basics Tutorial to learn how to build a more complex ETL pipeline.
• Create your own Dagster project and add assets to it.
• Check out our Python primer series for an in-depth tour of Python modules, packages and imports.