Discussing Bloomberg’s Cloud Native Journey with Andrey Rybka

This conversation covers:

• How Bloomberg is demystifying bond trading and pricing, and bringing transparency to financial markets through their various digital offerings.
• Andrey’s role as CTO of compute architecture at Bloomberg, where he oversees research implementation of new compute related technologies to support kind of our business and engineering objectives.
• Why factors like speed and reliability are integral to Bloomberg’s operations, and how they impact Bloomberg’s operations . Andrey also talks about how they impact his approach to technology, and why they use cloud-native technology.
• How Andrey and his team use containers to scale and ensure reliability.
• Why portability is important to Bloomberg’s applications.
• Bloomberg’s journey to cloud-native. 
• Some of the open-source services that Andrey and his team are using at Bloomberg.
• Unexpected challenges that Andrey has encountered at Bloomberg.
• Primary business value that Bloomberg has experienced from their cloud-native transition.

Links

• Bloomberg
• Bloomberg GitHub
• Follow Andrey on Twitter
• Connect with Andrey on LinkedIn

Transcript
Emily: Hi everyone. I’m Emily Omier, your host, and my day job is helping companies position themselves in the cloud-native ecosystem so that their product’s value is obvious to end-users. I started this podcast because organizations embark on the cloud naive journey for business reasons, but in general, the industry doesn’t talk about them. Instead, we talk a lot about technical reasons. I’m hoping that with this podcast, we focus more on the business goals and business motivations that lead organizations to adopt cloud-native and Kubernetes. I hope you’ll join me.

Emily: Welcome to The Business of Cloud Native, I'm your host Emily Omier. And today I'm chatting with Andrey Rybka from Bloomberg, thank you so much for joining us, Andrey.

Andrey: Thank you for your invitation.

Emily: Course. So, first of all, can you tell us a little bit about yourself and about Bloomberg?

Andrey: Sure. So, I lead the secure computer architecture team, as the name suggests, in the CTO office. And our mission is to help with research implementation of new compute-related technologies to support our business and engineering objectives. But more specifically, we work on ways to faster provision, manage, and elastically scale compute infrastructure, as well as support rapid application development and delivery. And we also work on developing and articulating company’s compute strategic direction, which includes the compute storage middleware, and application technologists, and we also help us product owners for the specific offerings that we have in-house. 

And as far as Bloomberg, so Bloomberg was founded in 1981 and it's got very large presence: about 325,000 Bloomberg subscribers in about 170 countries, about 20,000 employees, and more news reporters than The New York Times, Washington Post, and Chicago Tribune combined. And we have about 6000 plus software engineers, so pretty large team of very talented people, and we have quite a lot of data scientists and some specialized technologists. And some impressive, I guess, points is we run one of the largest private networks in the world, and we move about a hundred and twenty billion pieces of data from financial markets each day, with a peak of more than 10 million messages a second. We generate about 2 million news stories—and they're published every day—and then news content, we consuming from about 125,000 sources. And the platform allows and supports about 1 million messages, chats handled every day. So, it's very large and high-performance kind of deployment.

Emily: And can you tell me just a little bit more about the types of applications that Bloomberg is working on or that Bloomberg offers? Maybe not everybody is familiar with why people subscribe to Bloomberg, what the main value is. And I'm also curious how the different applications fit into that.

Andrey: The core product is Bloomberg Terminal, which is Software as a Service offering that is delivering diverse array of information of news and analytics to facilitate financial decision-making. And Bloomberg has been doing a lot of things that make financial markets quite a bit more transparent. The original platform helped to demystify a lot of bond trading and pricing. So, the Bloomberg Terminal is the core product, but there's a lot of products that are focused on the trading solutions, there is enterprise data distribution for market data and such, and there is a lot of verticals such as Bloomberg Media: that's bloomberg.com, TV, and radio, and news articles that are consumer-facing. 

But also there is Bloomberg Law, which is offering for the attorneys, and there is other verticals like New Energy Finance, which helps with all the green energy and information that helps a lot to do with helping with climate change. And then there's Bloomberg Government, which is focused on, specifically, research around government-specific data feeds. And so in general, you've got finance, government, law, and new energy as the key solutions.

Emily: And how important is speed?

Andrey: It is extremely important because, well, first of all, obviously, for traders, although we're not in high-frequency game, we definitely want to deliver the news as fast as possible. We want to deliver actionable financial information as fast as possible, so definitely it is a major factor, but also not the only factor because there's other considerations like reliability and quality of service as well.

Emily: And then how does this translate to your approach to new technology in general? And then also, why did you think cloud-native might be a good technology to look into and to adopt?

Andrey: So, I guess if we define cloud-native, a little because I think there's different definitions; many people think of containers immediately. But I think that we need to think of outside of not just, I guess, containers, but I guess the container orchestration and scaling elastically, up and down. And those, I guess, primitives. So, when we originally started on our cloud-native journey, we had this problem of we were treating our machines as pets if you know the paradigm of pets versus cattle where pet is something that you care for, and there’s, like, literally the name for it, you take it to the vet if it gets sick. And when you use think of herd of cattle, there's many of them, and you can replace, and you have quite a lot of understanding of scalability with the herd versus pets. 

So, we started moving towards that direction because we wanted to have more uniform infrastructure, more heterogeneous. And we started with VMs. So, we didn't necessarily jump to containers. And then we started thinking like, “Is VMs the right abstraction?” And for some workloads it is, but then in some cases, we started thinking, “Well, maybe we need something more lightweight.” 

So, that's how we started looking at containers because ...