TigerGraph Developer Spotlights allow you to get to know people using TigerGraph around the world to create powerful connected data solutions answering countless use cases.
Meet Max Latey
Learn about how Max went from being a physicist to becoming the Founder and CEO of Pinboard Consulting and how graph technology has changed how he now thinks about data!
Tell us a bit about your background and what you were doing prior to Pinboard?
Initially, I was a physicist. I started out doing a degree in physics at Strathclyde University in Scotland. I absolutely loved it. I did a placement at a nuclear fusion research site, JET (Joint European Torus), the central research facility of the European Fusion Programme. I loved learning for the first couple of weeks and then realized by the end of my placement that physics as a career was just way too slow for me. The pace of research, work, and life was just too slow. Even though I loved the degree and I loved learning about physics, I realized the research lifestyle in physics as a professional scientist was not going to be for me, so I moved over to IT. It was still technical and fun but just at a much faster pace.
How did your career progress from IT to becoming the Founder and CEO of Pinboard Consulting?
When I transitioned to IT, I started in Data Analytics, and from there, I moved into more technical roles. I then went into Business Operation Management and then consulting, which led to test management and quality assurance. It got to a point where I missed the math, analytics, and the technical side. Data Science had exploded at this point, and it caught my eye. I liked the idea of machine learning, AI, computer science, math, probability, and stats combined, so I started a part-time Master’s Degree in Data Analytics. It was then that I discovered graph theory properly, and I thought, “Wow, this is amazing!” I started doing Statnet in R and then discovered graph databases, including TigerGraph. That is when I realized that this could be a thing, and there could be a graph database wave. From there, I started Pinboard Consulting, recruited people I had met during my Master’s, and we’ve been up and running since January 2022. Since then, we’ve been working with TigerGraph, working with clients. We are currently helping out a client in the supply chain space and looking forward to helping many others.
What interested you in TigerGraph, and what has it been like working with the platform?
The main thing that stood out for me about TigerGraph was the maturity and stability of the system itself. The fact that you could have multi-server, real-time replication, distributed algorithms that can dynamically split your queries up into logical pieces and run them in parallel and return all of the results. The sophistication of the engine and the way you interact with it was amazing.
In terms of analytics you can do, I would say it’s easier to do things like NetworkX and Statnet because they have been through so many rounds of iteration improving things like their data science algorithms, clustering, centrality, and transitivity. They have built-in functions that do it incredibly simply. TigerGraph has implementations in GSQL, but they are not as straightforward to use. I think there are pros and cons to both sides. The fact is, however, that when you want to do things at scale, you can’t do it on a laptop or desktop with Python and NetworkX. There’s a very low ceiling that you just can’t go above, whereas with TigerGraph you can now take these amazing techniques and apply them at an enterprise level. You put those two things together, and really TigerGraph is the only one that can do that in the graph database space. You can try to do it in NoSQL, you can get part-way there, but you can’t do a true graph. TigerGraph was the only true graph representation of data where you could also do it at scale and still have access to the amazing graph theory techniques.
What has been your favorite graph-based project that you have worked on so far and why?
I would say my dissertation project, which I am still working on. It’s around trying to predict things about human social behavior by using random graph theory. We try to pull out basic information by matching real graphs with random graphs. So if you make a random graph and that random graph is indistinguishable from a real graph, you’ve learned something from the factors you’ve used as your parameters to make the random graph. Looking at the distributions formed in random graphs vs. real graphs is something that I find completely fascinating.
What has surprised you most about working with graph technology?
It’s made me realize how unnatural most data storage technology is. If, like me, you started using tech properly in the early 90s, then you grew up, in terms of career, using tech from then until now, your brain thinks that data is stored in rectangles, everything looks like a spreadsheet. Databases look like spreadsheets because data is stored in rows and columns. Graph made me realize that this dominant idea that data is stored in rows and columns is unnatural. It’s a very artificial way to represent data, and that’s not really how data is represented in the real world. Storing data in nodes with attributes and edges with attributes is a far more natural way of storing data, processing data, and querying data.
What’s been the most influential resource for your career?
The people around me. In terms of resources, there are the people around me, and then there is clear blue sky, then there is everything else. Any problems I face, anything that is difficult, anything I need help with, I ask the people around me. Whether it’s my bosses, employees, or teammates, there is nothing like being able to turn to the person next to me and ask, “How would you do this?”
If you were going to write a book, what would it be about?
Pictorial composition. I would love to write the definitive book on composition, which is how to compose objects in a 2D frame, like a photograph or a painting, to convey what you want to convey.
Anything else you want to share about yourself?
I love reading. Books are the condensed wisdom of 50,000 years of human history. You can’t beat that. In regards to my development as a human being, books have helped make me who I am today!
Any books that you would recommend?
Proofs and Refutations: The Logic of Mathematical Discovery by Imre Lakatos. It looks intimidating but it’s actually a great read! And has very little math in it.
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