Time to get blogging again. After a long run with Data in Beta, it's nice to have a fresh start. WordPress was feeling clunky, and over time the title took on unintended connotations. So I’m starting over—lighter, cleaner, and more grounded here on GitHub Pages.
The ideas won’t be any more “finished” than before, but it feels like a good time to shed some baggage and keep moving.
I'll still be blogging mostly about thoughts that come up in research and development with my team.
If you're into programming, computation, data management, or distributed systems,
you might find things here to interest you over time.
Research Roots
To set some context for what you'll find on this blog, here's a bit about where I’m coming from—intellectually and professionally.
I was trained as a database researcher back in my salad days. Out of college, I interned with the storied database group at IBM Almaden—the same team who brought us System R, which begat R*, which begat Starburst, the project I worked on.
I then did my MS with the amazing Postgres team at Berkeley, and continued working on Postgres with them as I did a PhD with the famed Wisconsin database mafia.
In retrospect, I was very fortunate to do a tour of duty with each of the most influential database groups of the time. I learned a ton.
During that training I met some outsized personalities and grew a thicker skin, which has undoubtedly had both positive and negative impacts on my professional life. That said, all my mentors were incredibly kind and supportive to me personally, and I'll always be paying forward their influences—especially Meichun Hsu, Hamid Pirahesh, Mike Stonebraker, and Jeff Naughton.
The Benefits of a Database Upbringing
Database research was—and still is—my home research community. It's a great space: a cross-cutting area of computing that has, from its beginnings, spanned academia and industry, theory and practice.
Data management provides a context to work on pretty much every computing topic imaginable. But database folks see the world of computing a bit differently: our primary focus is on the data that moves around, rather than the silicon resources of a computer. This often frees us up to take a broader view.
There's a meme in the "Systems" community: for any given topic, someone says “I think database people already solved that problem.”
And y’know … it's not wrong! 🙂
DB folks were among the first in software to tackle service-oriented computing at scale, with correctness and fault tolerance guarantees, and an eye toward serving a wide range of users—not just hobbyists and hackers.
The goalposts have shifted since the 1970s, of course, and sometimes being early to a technology can be a liability in the business world. But much less so in research!
It's kind of amazing how prescient the DB folks were in the 1970s and 1980s (before my time!) about the problems worth solving in computer science. And it's not just the applied folks—there's also a ton of database theory work that keeps coming back in new contexts.
Cross-Pollination
Over the years, I’ve had the good fortune to collaborate with friends from all corners of computing: experts in distributed systems, programming languages, HCI, AI, networking, and theory.
I've always liked working with people who can teach me new things, and I enjoy having a broad portfolio of topics to keep me curious.
Cross-area collaboration pulls you away from the center of your home field—and on the whole, I’ve been glad about that. Many of the most interesting places are away from the center.
Outside the Box
Topic areas aside, I generally prefer to work on problems that most folks are not working on.
Hot topics drive scientists to race for discovery. Lots of people like racing—especially because the fastest racer gets a big medal! But in most cases, if the winner had tripped along the way, someone else would have replaced them with no appreciable difference in outcome.
I find that highly demotivating, particularly in a field where the main goal is innovation.
I don’t like to race. I’d rather explore and invent.
Coming Up
In the next post, I’ll dig into some of the research that’s grown out of this perspective—ranging from language design and distributed consistency to data visualization, AI-based systems and beyond.