We’re excited and inspired by the level of enthusiasm behind the project, both from individual contributors and the broader community of users who are unsatisfied with SQL. We currently have an working version for the intrepid users.

We’re hoping we can build a beautiful language, integrations that are approachable & powerful, and a vibrant community. Many projects have reached the current stage and fallen, so this requires compounding on what we’ve done so far.

– PRQL Developers


The language is now fairly stable. While we’ll hit corner-cases, we expect we’ll only make small changes to the existing features, even as we continue adding features.

On this foundation we are planning to build advanced features like type checking, function currying, pivot/melt/wide_to_long/long_to_wide operations, operator overloading and a few more .


Currently the compiler is not sufficiently friendly, despite significant recent improvements. We’d like to make error messages better and sand off sharp corners.

Both bug reports of unfriendliness, and code contributions to improve them are welcome; there’s a friendliness label.

Standard library

Currently, the standard library is quite limited . It contains only basic arithmetic functions (AVERAGE, SUM) and lacks functions for string manipulation, date handling and many math functions. One challenge here is the variety of functionalities and syntax of target DBMSs; e.g. there’s no standard regex function. Improving our testing framework to include integration tests will help give us confidence here.

Alternative backends

Currently, PRQL only transpiles into SQL. It could be much more powerful (and in some cases performant) if we develop a data-frame-handling-library backend. To be more precise, we would want to apply PRQL’s AST to a in-memory dataframe of a performance-optimized library (such as Polars ).

This would allow data scientists, analysts and general Python developers to transform DataFrames with PRQL queries. One language for all data transformations!

PRQL as a tool

PyPrql is a step into direction of a general data handling program. Building on this, we want to build a tool that can read many data sources, offers syntax highlighting, auto-complete and type-inference using information from database’s schema.

We’ll likely continue pursuing this through integrations with other tools; combining the potential of PRQL with its openness and ecosystem.

If successful, we can have reproducible data transformations with an intuitive, interactive experience with fast feedback.


PRQL is focused at the language layer, which means we can easily integrate with existing tools & apps. This will be the primary way that people can start using PRQL day-to-day. Probably the most impactful initial integrations will be tools that engineers use to build data pipelines, like dbt-prql .

Not in focus

We should focus on solving a distinct problem really well. PRQL’s goal is to make reading and writing analytical queries easier, and so for the moment that means putting some things out of scope:

  • Building infrastructure outside of queries, like lineage. dbt is excellent at that! ( #13 ).
  • Writing DDL / index / schema manipulation / inserting data ( #16 ).