Public beta release
We're excited to announce that after a year of heads-down product development, a beta version of Patterns is open to the public to self-serve.
This is an important milestone on our mission to make data more accessible. One of the hardest parts about data science is knowing where to get started, what tools to learn, what business problems to study, etc.
I remember in 2014, when I left my job in finance, and set out on a journey to reskill myself to join a tech company, I learned SQL through Alan Beaulieu's Learning SQL, learned data science through Harvard's CS109 Data Science course, and eventually landed a job a Square.
During my whiteboard SQL interview there, I wrote my answers in a single run-on line becasue this was the only way to write SQL in a terminal:
select customer_id, date, sum(amount) as amount from payments group by customer_id, date
Lucky for me, my boss was understanding of my inexperience, and saw my commitment to learning technical subjects. To get this far I had to overcome setting up a local db, setting my bin/bash profile (I still don't know what I'm doing here), download and run python (never have the right version)... and this is just 1/10th of what's required to set up a bare bones analytics stack at a company. It. Should. Not. Be. This. Hard.
With Patterns, it's no longer this hard.
A new way to add nodes
We want using our product to be as enjoyable as playing a video game. To that end, we copied a common UX pattern in video games such as Command and Conquer for adding new items to a gameplay canvas. See below for show
Linear version history
While it's not quite git style version control (which we support by managing graphs via our devkit), a linear version history, and the ability to revert back to a prior version, is a powerful feature set for managing development on complex projects. After viewing the state of a prior project you can easily revert the current app back to that prior state, download the zip file, or clone the version into a entirely new app.
We use Posthog internally for a ton of different analytical and operational use cases. We support receiving events from Posthog via a Posthog App, integratef within their product. We also support extracting data from Posthog's API and have built a component within Patterns for this.