Today we’re introducing our serverless Python backend (and our very belated announcement for our Python blocks). It’s fast, highly available, yet extremely lightweight. We can safely say: it’s going to be a game-changer for you. 🙂
When I was a data scientist at Airbnb, I’d occasionally use Jupyter for long-form analyses. While I tend to be a SQL-first kind of guy, there are certainly some things that are more easily done in Python. So I’d interleave code with narrative markdown cells, and ship analyses straight in Jupyter.
But there were two big problems with this workflow:
So we decided to tackle these head on.
When we started Hyperquery, we wanted to build the best damn SQL notebook in the world. But over the last couple years, “What about Python?” came often enough that we knew we’d have to dive into the Jupyter world one day. Still, we didn’t want introduction of Python to degrade the delightful user experience we’d agonized over from the get-go — the core of what makes Hyperquery Hyperquery. We were worried that the issues that plague our competitors would come to haunt us. We didn’t want to have to give up our elegant, WYSIWYG editing experience, adding unnecessary friction to the creation process. We didn’t want to have to subsidize the cost of Python by forcing SQL-only shops to foot a heavier bill. We needed infrastructure to remain cost-effective for us and for our users, at scale. We wanted a sustainable, usable solution, not just a throw-money-at-the-problem solution padded by venture capital.
Today, we’re launching our solution, and we think you’re going to like it. We’ve innovated in two big ways here:
Stay tuned for more product updates!
To learn more about Hyperquery, visit hyperquery.ai.