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Quick Start

This guide walks you through deploying your first workflow on Datatailr in under 5 minutes.

Prerequisites

  • Access to a Datatailr instance (ask your administrator for the URL)
  • A user account (your administrator will provide your credentials)

Running locally?

You can run Datatailr on your own machine to try it out before deploying to the cloud. In that case, open https://localhost.datatailr.com and log in with username admin and password admin.

Step 1: Open a Workspace

A workspace is your development environment in Datatailr — a container with an IDE, Python, and the Datatailr SDK pre-installed.

  1. Log in to the Datatailr dashboard.
  2. Click the Workspaces icon in the left sidebar.
  3. Click Connect on your default workspace and choose VS Code or JupyterLab.

Your workspace will start and open in a new browser tab.

Step 2: Initialize a Demo Project

Open the terminal inside your workspace and run:

datatailr init-demo

This creates a demo project with example workflows, apps, and services, and opens it in a new IDE window.

Step 3: Deploy a Workflow

From the demo project directory, deploy the data pipeline example:

python -m examples data_pipeline

This builds a container image with your code, uploads it to Datatailr, and schedules it for execution.

Step 4: Check the Results

Go back to the Datatailr dashboard — your workflow will appear in the list of compute processes. You can:

  • Watch its status change from Building → Ready → Running → Completed
  • Click on it to view logs and output

You can also check the status from the terminal:

dt job ls

What Just Happened?

When you deployed the workflow, Datatailr:

  1. Packaged your code and dependencies into a Docker container image
  2. Stored the image in its internal registry
  3. Scheduled the workflow for execution
  4. Allocated compute resources and ran the workflow
  5. Collected logs and results

To learn more about how this works, continue to Compute Processes.

Try More Examples

The demo project includes several other examples you can deploy:

python -m examples app          # A Streamlit dashboard
python -m examples service      # A Flask REST API
python -m examples excel        # An Excel add-in
python -m examples all          # Deploy everything

Next Steps