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Facebook Alumni Startup - Real-Time Data Analytics Platform

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Read how a company called in Toronto recently released a new version of its big data analytics platform for behavioral discovery and analysis and essentially in Toronto can analyze trillions of raw Tom savings data points in just one to two seconds. And there's no need to work with the data scientists to extract or transform that day. An ordinary business. Users can ask question after question getting answers about their customer behavior. But in real time, this is where things get interesting because the platform was built by co founder and CTO Bobby Johnson who also headed up development of analytics tool at Facebook, including scuba many, many moons ago, so book elope and hold on tight. So I can beam your ears all the way back to the US. So we can speak with CTO. Bobby Johnson who's going to tell us all about inter on an big reveal recently about the world's most advanced platform for behavioral discovery and analysis. So massive warm. Welcome to the show, Bobby Kennedy tell the list as a little more about who you are. And what you do. Yeah. Sure. So my background is a used to work at Facebook. I was the head of infrastructure there from a few million users up to a billion. So did a lot of work on scaling there. And then a few years ago left to start in Toronto, which his company I'm working on now where we do behavioral discovery. And analytics, and what we mean by that is sort of taking what we call vendetta. So this is you know, anything that happens by the time stamps, so this could be a click or a swipe on website. It could be called a call center. Could be a transaction often. It's a mix of those things, and we pull that all together. And we're able to let you look at how people are behaving or how devices are caving. That data so things like what people do before they turn where people getting stuck in this on boarding flow. So things that have to do sequences of advanced rebel pull in is data's often. Very large were able to pull it in to the vet at massive scale. And then make those kinds of really viable business questions accessible to everybody. Not just data scientists not just people code. There's a visual interface where you can go explore through and find out, you know, how people are interacting with your product or service. Wow. What a great history of Atlanta's. There's not many paper, I would imagine. But could say they've scaled full gold from sculling one million to a billion us as lot. So going to ask let me tell me more about you'll bike story foam leading the development of analytics tools Facebook, including scuba, of course, which is Facebook's internal data analysis platform to go onto you'll tell him out and Theron, and the kind of problems that you sat too so feel clients. Yes. So it was I mean, we obviously learned a lot about. Out just for the mechanics of how you deal with data at the scale. How you make it fast? How you how you make it manageable? But the other thing that was really interesting that we learned along the way was is seeing how people actually use this data to solve light rail business problems in the thing that was neat about Facebook. Is that we really did have almost everybody in the company was able to to use data base their decisions on that. And so we got to see over the years, you know, how you know, mapping. And so when you have a business question, it usually starts out pretty vague. There's this thing I don't understand. I wanna get this number to go up. Right. Understand why this isn't working as well as it should in. So getting to see how you know going from those kind of questions into this is a specific sad of analytic questions like NASCAR or get my answer. And so we had you know by the time face, but we had more than half, the employees were active users of of Anna. Politic's tools. We learned a lot about how you make those things accessible so people can start with their rod questions narrow down to finding an answer to something that they can actually do something about, you know, I have a problem in this particular stage of of this process, or, you know, this group of people is working. So I need to change something about how I now I, you know, either, you know, message that something on our product. So it was a lot of new Chevaliers sitting sitting with people who add real pressing questions figuring out how could make the tool actually serve them. Not in Toronto also recently released a new version of its big data analytics platform for behavioral discovery and analysis. So can you tell me more about how it can analyze trillions of rule toll? I'm saving day points. It just one to two seconds. And also how this actually no need to work with the dia- Santus to extract or Trump's full not data it. So the thing about event data behavior is this data is really big. And it's one of the things feel a lot of a lot. Big data is really time series data because you know, rather than having, you know, if you have ten million users on your app, ten million things isn't a big number. But if those people are clicking thousand times a day that ends up being a really big number in. So it's. You know, real important we're able to ingest that all events in the you across it. Because the other thing he find with behavior is that you don't know beforehand. What's going to be interesting to you can't just sort of say, oh, I'm going to summarize this, you know, if I can count, you know, how many times people clicked on mobile app, but that's not useful. What I want to know is that when they went through these three steps it turned

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