🛠️ Dagster: Step-by-Step Deployment Guide

This guide walks you through deploying Dagster with Docker — from a blank server to a working pipeline in about 15 minutes.

No prior Dagster experience needed. Just Docker and basic Python knowledge.

📋 Prerequisites

• 🔹 Docker — docker --version


• 🔹 RAM: 4GB minimum (8GB recommended)


• 🔹 CPU: 2 cores minimum


• 🔹 Storage: 5GB for Docker image + pipeline data


• 🔹 ⏱ Time: ~15 minutes

🚀 Step-by-Step

Step 1️⃣ — Pull the Image

docker pull dagster/dagster-cloud-agent:latest

The image is about 1.5GB. On a 100Mbps connection, about 3 minutes.

Step 2️⃣ — Write docker-compose.yml

version: '3.8'
services:
  dagster:
    image: dagster/dagster-cloud-agent:latest
    restart: unless-stopped
    ports:
      - "3000:3000"
    volumes:
      - ./dagster-home:/opt/dagster/dagster_home
      - ./pipelines:/opt/dagster/app
    environment:
      - DAGSTER_HOME=/opt/dagster/dagster_home

📖 Breaking it down:

• ports: 3000:3000 → The Dagster webserver UI runs on port 3000
• volumes: ./dagster-home → Stores pipeline metadata, runs, and logs
• volumes: ./pipelines → Mount your Python pipeline code here
• DAGSTER_HOME → Tells Dagster where to store its database and config

Step 3️⃣ — Start the Service

docker compose up -d
docker compose logs -f

After about 10 seconds, you should see Dagster's startup logs. Wait for the line: Dagster webserver is running on http://0.0.0.0:3000

Step 4️⃣ — Verify

curl http://localhost:3000

You should get an HTML response (the Dagster UI). Or just open http://localhost:3000 in your browser.

Step 5️⃣ — Your First Pipeline

Create a file at ./pipelines/my_pipeline.py:

from dagster import asset, MaterializeResult

@asset
def raw_data():
    return [1, 2, 3, 4, 5]

@asset
def processed_data(raw_data):
    return [x * 2 for x in raw_data]

Dagster auto-discovers assets in mounted folders. Reload the UI and you'll see two assets with a dependency arrow between them. Click "Materialize" to run the pipeline.

⚠️ Troubleshooting

🚫 Port already in use

Change the host port: "3001:3000" in docker-compose.yml. The container uses 3000 internally, your access port changes.

🐢 UI is slow on low-RAM machines

Dagster uses an in-memory SQLite database by default. For 2GB machines, reduce the asset cache size or switch to PostgreSQL (check the docs).

💥 Pipelines not showing in UI

Check that your ./pipelines volume mount is correct. Dagster auto-discovers *.py files but needs the dagster package installed in the container. If your code uses external dependencies, add a requirements.txt.

📊 Real Performance Numbers

🏁 Done!

You now have a running Dagster instance with your first pipeline. From here, add real assets, connect to databases, and schedule your production pipelines.

P.S. The Dagster UI has a "Launchpad" tab where you can run pipelines with custom config. Most teams discover this feature in month 2 and wonder why they spent month 1 clicking "Materialize" manually.