The easiest way to build the Streamlit app you actually want

A Streamlit UI is only a part of the story. You need to deploy it, add auth, automate tasks, and get feedback too. We’ve built the online workspace Databutton to make it all super-easy.
March 30, 2023

Don’t you just love Streamlit? I certainly do. As an aging Data Scientist, I remember a time when I was fumbling with html, css, and Flask to get anything in front of people. With Streamlit, my days of hating html/css and trying to learn javascript came to an end. No doubt, hands down:

  • Streamlit is the absolutely easiest way to write UI in Python.

However, despite all its glory, writing a Streamlit UI is only a part of the story. You still need to:

  • Host the app somewhere your users can find it
  • Add auth so you can choose who your users are
  • Write backend jobs that fetches data, does processing, model training, or similar and write data back somewhere or sends our notifications
  • Collaborate and get feedback from non-technical people
  • Make sure the whole thing stays up and gets improved

Now, it’s not that using something like Flask to write a backend, or deploying on Heroku, or figuring out how to setup auth is that difficult on its own. It is more that it leads you to become an infrastructure engineer building and maintaining a stack, or waiting for someone else to do it. In my experience, that leads to data apps that never leaves the laptop, nor do they get improved or maintained, and most don’t even get built.

After years of seeing and feeling this on a daily basis, we decided to start a company and create Databutton to make it truly easy to build analysis powered tools.

What is Databutton?

Databutton is an all online workspace where you can iterate right in your browser and see updates live with hot-reloading. When you are happy with your app, you can publish and share it with the click of a button. Thus,

  • No more wrestling with a local dev setup and python environments.
  • Stop spending time on deployment, auth, and keeping it up.
  • Pull collaborators right into the space where you are working.

And yes, the basics are in order. You can use any Python package you want, code is versioned, and there is a secrets store to manage your secrets.

When working with live data, you typically don’t want your users to sit in the app and wait while you fetch tons of evens and do processing on them. Instead you want that processing to happen continuously and be ready by the time your users need the results.

In Databutton, you can write and schedule python jobs just as easily as writing Streamlit apps.

Being able to schedule code is also a powerful way to reach your users and provide insight. In practise, people rarely sit and watch your app all day long, or even daily. Through writing Jobs in Databutton however, you can instead send out notifications on email, Slack, Discord, or wherever else your users are. That enables you to get people attention and pull them into the app when they should.

Databutton is currently in free closed beta and we would love it if you check it out and provide candid feedback!

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