5 BigQuery Features That Changed How I Write SQL

Modern BigQuery SQL Features for Senior Engineers

Happy Sunday! I hope you’ve all been well! I haven’t been keeping up with The Data Letter as much as I should have these past few weeks because I’ve been busy building and launching the beta for Asaura AI. For those of you who are new here, Asaura AI is a tool designed to help people who struggle with the high energy cost of starting a task. ADHD brains. Neurodivergent brains. Just plain tired brains. It’s built to give you a simple entry point so you can stop negotiating with yourself and just move.

The beta is finally ready for macOS and Linux, and I’d love for you to test the architecture. If you’ve ever felt paralyzed by a project that felt too big, your feedback is exactly what I need to refine the system. You can try it out and share your experience through our survey at the links below.

I’ve been documenting the build over a 100 Days of Building AI video marathon. You can check that out on Substack notes or LinkedIn. Today is Day 91.

I’ve also been writing technical deep dives about the Asaura AI build on my other Substack, Between Thinking and Doing. Feel free to subscribe to that as well, or connect with me on LinkedIn.

Now that the main build is stable, I’ve got the space to get back into the rhythm of writing here every week.

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Today’s article covers BigQuery features I've found myself teaching constantly over the past year. I share at least three of these with every data team I meet. These features solve problems people have worked around for years.

Part of what makes them so useful is that SQL has a structural quirk that we all know too well: you write clauses in a fixed order that doesn’t match the order they actually execute. BigQuery’s GoogleSQL dialect has been shipping fixes for this. The five features below are the ones I keep coming back to.