0.2.0
🎉 🎉 After several months of building, PRQL is ready to use! 🎉 🎉
How we got here:
At the end of January, we published a proposal of a better language for data transformation: PRQL. The reception was better than I could have hoped for — we were no. 2 on HackerNews for a day, and gained 2.5K GitHub stars over the next few days.
But man cannot live on GitHub Stars alone — we had to do the work to build it. So over the next several months, during many evenings & weekends, a growing group of us gradually built the compiler, evolved the language, and wrote some integrations.
We want to double-down on the community and its roots in open source — it's incredible that a few of us from all over the globe have collaborated on a project without ever having met. We decided early-on that PRQL would always be open-source and would never have a commercial product (despite lots of outside interest to fund a seed round!). Because languages are so deep in the stack, and the data stack has so many players, the best chance of building a great language is to build an open language.
We still have a long way to go. While PRQL is usable, it has lots of missing features, and an incredible amount of unfulfilled potential, including a language server, cohesion with databases, and type inference. Over the coming weeks, we'd like to grow the number of intrepid users experimenting PRQL in their projects, prioritize features that will unblock them, and then start fulfilling PRQL's potential by working through our roadmap.
The best way to experience PRQL is to try it. Check out our website and the Playground. Start using PRQL for your own projects in dbt, Jupyter notebooks, and Prefect workflows.
Keep in touch with PRQL by following the project on Twitter, joining us on Discord, starring the repo.
Contribute to the project — we're a really friendly community, whether you're a recent SQL user or an advanced rust programmer. We need bug reports, documentation tweaks & feature requests — just as much as we need compiler improvements written in rust.
I especially want to give Aljaž Mur Eržen (@aljazerzen) the credit he deserves, who has contributed the majority of the difficult work of building out the compiler. Much credit also goes to Charlie Sanders (@charlie-sanders), one of PRQL's earliest supporters and the author of PyPrql, and Ryan Patterson-Cross (@rbpatt2019), who built the Jupyter integration among other Python contributions.
Other contributors who deserve a special mention include: @roG0d, @snth, @kwigley
Thank you, and we look forward to your feedback!
