PRQL

Talk Structure

• Introduction, Background and Aim
  • SQL from the 1970s (like Star Wars)
  • PRQL is concise, with abstractions such as variables & functions
  • PRQL is database agnostic, compiling to many dialects of SQL
  • PRQL is ergonomic for data exploration — for example, commenting out a filter, or a column in a list, maintains a valid query
  • PRQL is simple, and easy to understand, with a small number of powerful concepts
  • PRQL allows for powerful autocomplete, type-checking, and helpful error messages (in progress)
• Examples and Integrations
  • Python
  • R
  • Shell
• Call to Action
  • High impact / stars per contributor
  • Bug reports, example queries, documentation
  • New features and compiler enhancements

Introduction

Hello Normconf! Thank you for coming to my talk. I'm excited to talk to you about PRQL - a new way of working with databases that makes your life as a data explorer much easier.

Now, you may be thinking, "What is PRQL? And shouldn't I just be focusing on SQL, which is ubiquitous and universally supported?" Well, you're right. SQL has been around for a long time, and it's served us well. But PRQL takes the best parts of SQL and adds some new features that make it even better.

The first thing to note about PRQL is that it compiles to SQL, so you can use it any place where you would use SQL. But, unlike SQL, PRQL is database agnostic. That means it can compile to many different dialects of SQL, so you can use it with any database.

One of the key things that sets PRQL apart is its conciseness. It has abstractions like variables and functions that allow you to write queries more efficiently.

But what really sets PRQL apart is its ergonomics. It's designed to be easy to use and understand, with a small number of powerful concepts. And it has some really cool features, like the ability to comment out parts of a query without breaking it. This makes it ideal for data exploration, and it's a huge time-saver.

Finally, PRQL is under active development, and we're working on adding autocomplete, type-checking, and helpful error messages to make it even more user-friendly.

So if you're tired of wrestling with SQL and want to make your data exploration easier, come and learn more about PRQL. I promise you won't be disappointed.

Call to action

Now that you've seen what PRQL has to offer, Why not take the next step and learn more about it? The best way to do that is to visit our website at prql-lang.org. There, you'll find all the information you need to get started, including documentation, a PRQL Playground, and more.

But don't just stop there - we're always looking for contributors to help us make PRQL even better. And with one of the highest number of stars per contributor of any project on Github, you can really make a big impact. Whether you're a seasoned developer or just starting out, we welcome contributions of all kinds, including bug reports, example queries, documentation enhancements, and more.

And if you're really ambitious, you can even work on new features or enhancements to the compiler. Trust me, if you do that, you'll be a data hero in no time! So don't hesitate - visit our website and join our community today. Together, we can make working with data easier and more enjoyable than ever before.