How To Evolve An Open Source Project Into A Business by FangJin Yang, CEO@Imply - Robin.ly Talk

welcome to robin e and entrepreneurship talk and robert is brief interaction with albany governor's online content platform i mean to create by the understanding of leadership partnership and the AI insights for researchers ideas so you know to better prepare for a next generation of leaders and atropine ors so today we are glad to have founder CEO of empire funding young and welcome Wendy yeah yeah this periphery internship or imply implies a data analytics company and founded in 2015 so they are focusing on high performance analytics on large-scale event streams so founding is also a co-author of a party tweet which was a open source state story store decide to quickly the rest a massive courting of a human today so before he was some a dinner lead and metal markets which was acquired by snapchat in 2017 and fun Jane received his math degrees from you know what Waterloo University and the I in computer computer engineering right yeah so yeah thank you verse a brief introduction of the what do you guys do and who are your client yeah yeah sure absolutely so imply is what is known as an open core company and open core companies typically have an open source project at the heart of our product and then there's sort of an enterprising end-to-end solution that we sell to customers so for people that may not be familiar of open source is this idea of basically free software so implies case were really built around an open source technology called Apache druid which is a next-generation database designed to ingest and analyze like very large volumes of digitally generated data and what we do at imply is we build a complete Ellucian around this courtroom project that's designed for large companies enterprises to be able to use okay so since you are involved in lots of PR technology revolutions in this big theater marquee and what do you think about the cable cuts through some of the key milestones so in the past maybe five or ten years so the way I see the evolution of data analytics is you know the first generation of technologies on data warehouses so if you look at what companies were using many decades ago data warehouses by Oracle Tara Veda HP many other vendors were very very popular and I think over the course of the last 10 years that started to change a little bit because people started realizing data is getting more complex data is now growing in volume and combined with the rise of both the Hadoop ecosystem which is another populism yes as well as public cloud vendors analytics has shifted where I think now there's a central storage location and different types of kind of engines to cordilla central storage location so this is what's known as kind of a data Lake architecture it's separating where data is stored with different systems that can get value out of the data this is how Amazon runs its and all the next services this is how Google brought all of its analytic services and this is what people do that deep because of stuff I think where analytics is now shifting towards is actually moving towards more real-time dynamic workflows so a lot of businesses today are investing in the digital aspects of their business putting more of their more more more and more of their business online and what that means is instead of you know working with kind of static files you all now have continuous streams of information and I believe analytics is migrating to be able to ingest and also analyze basically continuous flows of information and this is the world that imply belongs to we're trying to build a new type of database basically for this world okay so as you know you know since 2716 a 2015 maybe 16 so he is getting hot and hot so how to shoot data react to those rights of a I am loon right I could give you some example yeah so it's interesting so AI machine learning that the concepts have been around for a very long time they get around for decades yeah I think really it's in the investment circles they've been getting more attention and in the media they started getting more attention as a result I think it's an open question of you know our company is actually going to be successful with with our AI solutions okay but another the less I do think AI and machine learning they do have value in for the whole data ecosystem yeah the way I see kind of AI and ml broken down is there's the algorithms piece and then there's the compute piece if there's a lot of data that you have to work with and then there are systems that have to do that raw like compute in order to produce answers of this data and like the rock compute feeds data into more intelligent algorithms that basically can help make decisions help make recommendations and so on and so forth okay so you know what I see what we do at imply with a lot of other systems is to build a foundation that like compute layer that higher level AI technologies can leverage for a lot of their their applications for law they're like number crunching okay do you have someone AI clients and like how do you work with those those companies like there is a bunch of attorneys inside and working with you guys right how do we work with with that ya know we have one of our clients which is a major financial bank they're combining technology with kind of their homegrown AI system yeah and the idea is you know they're trying to better identify like identical customers across different product lines and then potentially understand what other products we might be interested in okay and in that case they're using our technology really for like more low level compute for no very fast number crunching for getting in data immediately and being able to crunch numbers on that data and then they build their own high level abstractions and algorithm data okay is it mostly like structured sharky the data or unstructured it uh it's it's actually a mixture I would say probably the best way to categorize is is semi structured okay because unstructured data could be like a video file could be an audio file yeah I mean very difficult to actually process and deal with you know most of the time what we deal with is more semi structured data where there's a more fluid schema but there's like an ocean of keys and values okay so you mention about the event stream yeah solo can you briefly induce what is the event stream yeah so event stream it's it's an industry term really used to describe kind of the output of a digital business yeah and I think there's kind of three main categories of event streams a user generated data okay so if I'm a user and I'm using a web page I'm using some sort of mobile product or some sort of digital product and my actions oh how I engage with that product produces data that's that's useful but event streams can also encompass like the metrics generated by applications right so maybe KPI so the applications their performance etc etc we could also be the low-level infrastructure data so server metrics server logs basically the output as people are interacting with different types of products so really what event streams are are like discrete events so a continuous flow of events that's describing some sort of action occurring okay you look for markets for four years and how did you join a treet project and why you decide to to study your own business yeah yeah so when I first move out to Silicon Valley's in 2009 yeah I join a very large corporation which was Cisco yeah and I was doing some research and development work actually for them but I realized very quickly that I that the big company really wasn't for me and a lot of there was a lot of structure which was nice you know there were a lot of support which is nice but I was really fascinated by like startups okay so I started looking basically for the smallest company I could find that was doing like in testing work so I was one of the first I think five employees this tiny tiny company called like Metamarkets yeah and I joined really because during the interview process I met like another very like strong engineer uh who well you know he was the kind of the VP of engineering at that time he brought me in and he was like hey I just started this like her new project called druid I think we do some cool stuff here so I joined that project right away it was really just the two of us when we started building okay initially for some use cases that we saw in our markets at the startup but then we started realizing like oh actually like these use cases they have applications in a lot of different places and well when we decide to open source of technologies actually a lot of like major companies came to us that's not like hey we actually have like similar use cases to what you guys are doing and over time you know more and more companies started coming to us and then at a certain point I realized like oh there's actually market potential here there's a market gap there's this technology solving and even that doesn't exist right now I think there's the opportunity to start a company and like I always want to start a company because I was very fascinated by startups yura of focusing like a Empire is focused on real-time analytics uh as we know there are many other real-time data streaming - such as Frank Storm Kafka and how does treat different sheet from those projects yeah definitely more so this is kind of a technical answer yes but really when you look at data architectures and it's very rare to see a single technology solve everything that's required for a particular use case and what's much more common is people tend to build data stacks they combine different technologies together and then each of those technologies is specialized to be good at like certain things okay so for example in a more modern streaming analytics I'd say there's three main components there's one piece which is responsible for getting data from somewhere and transfer it somewhere else so this is what's known as a message bus and this is what Apache cough that does it's very very good at basically delivering data from one place to another another very critical component of analytics as a whole is cleaning data transforming data and processing data because when you have raw data it's very very difficult to use raw data as is you oftentimes have to transform it in many different ways so this is known in the industry as extract transform load which is also the short name of its ETL and the piece that does ETL is what's known as a stream processor so Apache flink actually storm spark streaming they're actually almost entirely designed to do the tl4 swim Canada and now the last piece is how do you get answers from this data how do you store this data may be a long term yeah and how do you get answers from it and this is typically what databases do and this is what true it does okay so druid is not competitive to neither Kafka nor flame or storm but it's very complimentary so a snap would be you put data into Kafka you transform it in something like flink and then you deliver that transform data and to Drew it for further queries okay and that's an end to ends back so you guys have this entire card so which is some other service for it okay all right so I know it obvious I have a has many restrictions yeah so how do you handle them self yeah so imply is packaged software and what that means is you can actually install it on premise you can install it really in kind of any Linux based environment but one of our top products is implied Cloud which is a managed service for AWS and what we mean by a managed service is we help a client deploy our software into their AWS account and so for the client they actually fully own their data they can control that the hardware that the data runs on and what we help with is making operations and deployment much easier okay so in that case you know a you know we haven't seen many restrictions of AWS model has actually worked pretty well and its value bad for the client because they don't it's you know they get the benefits of not having to manage the software there's a lot of automation behind the scenes so it's easy to operate and then they also get the benefit that they own the data so we're not you know looking at their data we're not owning their data they own that okay so it's a model that other companies such as data breaks which is a company behind of happy spark and people at others have adopted to great effect okay god I saw so you previously mentioned so you started this company in 2015 and then the you I think I remember you risked out your sit around from Khosla Ventures yeah right right so and then lately anything as last year your wrist and up around from a grease and harvest right so two of them are both of them are great in a raid on investors right so how did you find them how did you convince time right so I think the city Ron from Christopher Khosla Ventures two million dollars and so you know it's not easy and but Iran for 13 million dollars even is the UN is even harder right so yeah so many markets itself the startup I was at Mamoru's bought my snapchat was funded by Khosla Ventures oh okay and at that time when we decided like we are ready to start our own company we actually talked to our employers first and you know we had a conversation of how this is gonna be done and they introduced us basically to cosa ventures okay it's actually you know we had talked we had taught some other investors as well but it was actually a very quick process for the seed at that point you know drew was started to get pretty widely adopted there were a lot of major companies using it and talking about it and there was enough buzz around it that I could if I appreciate company there okay and after we found found it the company you know the the first two years are always like the hardest when you're just have to figure everything out but as we got better at building our product as a product became more mature I should say and then as we've got better at selling it we were actually growing pretty fast in sales and we're thinking about kind of doing an a whether or not makes sense and news coincidental at that time that increase in Horowitz actually reached out to us asked us to grab coffee we started talking one thing led to another next thing we know that were talking to a lot more people and okay that that's what became kind of the a okay God so uh so you actually like you have three co-founders I mean only to others and to others and so besides you is any other people from many market yeah yeah so both my co-founders are from that company as well okay all work together for a very long time okay I trust them a lot okay and we know we work the wall very well together but it's you know also very beneficial that they're like amazing world-class engineers okay one of my co-founders is now completely leading druid I mean the architecture direction of it and the other one you know he was one of the creators of d3.js which is one of the most popular visualization libraries in the world okay you know he's published several books top courses and it's like 20 you know world-class engineer so I've always been doing working with them and and what I started helping you just couldn't think of anyone else I want to work with okay so you were you started your career as a linear yeah so then later you saw this opportunity we founded his company and you became the founder CEO right so how did you transfer my dear background to be a more of an entrepreneur or or maybe business person yeah so I was I think from very young I was interested actually in like startups even when I was a university I was trying to learn more about how do these startups work how did he get started you know what's venture capital because yeah he a decade ago there was not this information there was no YC there was no theis not these incubator so your how to figuring out everything on her own and you know I started my career as an engineer but in the back of my mind you know I was always like how do I learn more about startups so I started at Cisco and I realized I'm not gonna be learning everything I want to know at like such a major organization that's what prompted me basically to find the smallest company I could I could find and like once I found that company I realized like oh actually like starting a company is extremely difficult like not only do you have to be a good engineer you also have to have a very good head for business so I spent a lot of my the earlier as my career focused on trying to be a good engineer trying to build a good system that would add value to different companies and get them helping you use it and then once I started a company transition very quickly until i learning about what's known as go to market strategy like how do you take a technology and product actually position the market and then begin to sell it and from that it's you know part of it is spending time with people trying to find mentors that can help you part of it a lot of my own research I probably read like 50 60 different books on the concept to go to market strategy everything from sales to marketing everything in between and as you go through the journey you know it's it's a very sink-or-swim environment you either figure it out your company dies yeah so you hope you figure it out but as you go through the journey you realize like all there are skills that I'm good at and can create value for going to market strategy and their skills unless good at and for the skills unless good at I need to find like other people that can like supplement my skills so from that you start building out your executive team and members of it so it was a I think like the honestly the way to learn it is just a new way it's like anything else you can read a million blogs but the end of the day you just do it yeah I'm a critic I I know it's a it's a tough journey from 10 or tree to product to market right so yes new mention of the could market strategy like what is your takeaway and what is your insights about for a technology company also what would be the pass yeah so I'll say this so broadly there's two types of companies right there's business to consumer and this is the business to companies yeah implies the latter like we sell to other businesses yeah and but more like either way of selling there's very complex strategies of how do you like how does this company actually make money yeah and when I first started my company I didn't like realize a lot of this I was more naive and I thought that if you have very strong technology like people will figure it out and then they'll start to adopt it which is which is not the case because no matter what you are doing especially in the business-to-business space there is competition and then it becomes how do you convince you know companies to adopt your technology over the complication because some of the competition they have relationships that last like decades with the customer you're trying to get into you know they have teams that are like thousands of times larger than yours you're you're a tiny thing like three people and like a dog and like that the tiny office basically try to figure out like how make and it's one of the world's largest companies to give us money yeah and then you realize like okay the technology is almost like secondary to like how do you talk about the technology in terms of the problems it's also the value that it creates and then from that you start learning no specific use cases you start learning on specific problems and you start building more and more of your go-to-market strategy it's not an easy journey by all means but it's you know as I mentioned you know figure it out a company die so you obviously spend a lot of time and effort to try and figure it out okay you did you actually a stop stop from some reference clients first like a beaut really successful stories so we were I think fortunate because we had the open source which was the free software and there were many people that use like the free software and then you know for us that's a starting point to say like well like you know like look at Netflix using this looking like yeah I'm using this look at like these major technology companies are using this and this is some of the some of their use cases that they publicly talked about they were also fortunate that the technology is very very popular in China so there's like books written about it or yeah just like that so we could always talk about some of the Chinese companies like Baba 10 cents of the world and how they're using it that helped us get started okay are they talking about China so like like us at China who are you you have lots of customers and though there are world beat typical applications yeah so today we have customers in a couple of different countries primarily in the US but we have an expanding presence now in Europe Middle East and then we have a couple clients in China and Asia Pacific as well another primarily its us-based oh there's we have a little bit more of a horizontal approach to our technology management use cases we've seen we've seen the technology become very popular for digital advertising and digital marketing data so as as people are viewing as clicking on ads the data is getting generated from that is being analyzed often with our systems we see a lot of adoption for user experience as well like understanding how users engage and interactive products and this helps companies improve on their products faster it helps companies AP test their products and do rapid iteration on their development we started seeing use cases with more network security and also Network flow data as well so that kind of the other the bottom of the staff with more infrastructure level data so we're still learning about these cases there's been more uses coming up for supply chain and manufacturing analytics which we're pretty excited about because I think there's a really interesting work that we can do in that area okay so uh so in China like do you actually host your service song like some China based at cause platform like ah Rd cloud others right so today we're not doing that so today most time when we work with a Chinese company it's tend to be very large Chinese enterprise that we have we can have a closer engagement and closer relationship with in the future we may you know there might be opportunities to partner with with one of the cloud providers I think all the major cloud providers are using Drude today yes I think there's there's potential collaboration but I think we're too early stage as a company to really explore like broad international expansion okay so what it's in your three years it's very intense our trip and oh yeah and what is your your biggest challenge or maybe maybe even mystic right so many challenges and so many different mistakes I don't know happy one point to the biggest one I would say that some of the stuff I struggled with early on or not some of the things I struggle with today and some when I first started I think I was more sensitive to rejection that I am today and rejection comes in many different forms right like investors telling you you're your company's not gonna succeed its potential clients telling you like we don't see the value out of your product its candidates telling you like we don't work at this company because probably go nowhere so it costs a lot of different forms and every in the beginning it's like every time you're actually like I feel bad but after you see enough rejection it's like oh whatever it's just Tuesday just stop caring about it so like in the beginning I think I dealt with rejection a little bit harder than I do today like today is just whatever it doesn't matter it's like every days you get rejected you move on I would say the mistakes that the things I wish I had done better when I first started were that I wish we had focused even more like we knew that focus was very important and a start-up but there was a lot of like random one out there because you get distracted so easily the startup there's a lot of like kind of random little things that we did here and there that I would have indeed resolved yeah a limiter is now looking back like all those who probably like waste of time and we should have like me even like crazier in our focus but it's harder you know it's is it's a distinct process right it's it's like you do lots for practice experiments right so you can yeah you do but it's like sometimes you also do like you can't eat too many you also need like trustor your beliefs and your creation okay okay okay so yeah I mean and then go on and on about mistakes but like overall the journey is not easy and I think every startup makes makes concept okay okay so I think a lot of our audience might be an interesting ending you know started their own business yeah I stood up and what would be your advice uh right so I would say honestly I think that startups are very very difficult and they are very very difficult you hear about all the time but it's like one of those things until you do it you don't realize like how difficult it is and I think people before they start really should understand their motivations of why they're starting this company okay and I hear like actually a couple of different motivations and some are bad and some I think are better so for example when I talk to people and they say like I want to start a company I ask them why do you want to start and they're like I want to be rich or I asked them you know why do I start a company they say oh I want to be my own boss but often times like that's not the case uh-uh-uh-uh-uh-uh startup because when you do a start-up like you know the cash is very very important you're not making a lot of money you might potentially in the future but during the most difficult days of a startup and there's some really really hard days and I do not believe if your motivation is like money you all like survive those days like you will probably give up and I know many many people I decided it's like it's not worth it it's too hard it's custom motivation is not strong enough or people say like oh like I want to start a company because I'll be my own boss that's not the case because if anything you report to everybody now like if one in your place quitting like now you like you dropping everything trying to make them stay for this some person company's not happy to try to make them hack here you have your board to report to like everybody you put to everybody yeah the one with the most number bosses so I also think that's not a strong enough motivation and I think the motivation that gets you through the most difficult days has to be something deeper it's a lot sometimes for like here it's a true belief that you believe that you are you're creating something that has not existed and it needs to exist okay it's it's usually I think for a founder founder of deeper reason of why you keep going right because there's a lot of other things you can do to to make more money or to have more without kind of the pressure and the stress of heinessen in the founder seat I think you know what do you mean like persistence these were in person versus endurance I think is I think the greatest startups have endured they saw they just kept going okay and yeah so I would say like the biggest takeaway that I think the most important attribute for a founder is endurance okay I got it okay so then where do you see yourself and then apply in the future three to five years yeah founders were optimistic so I tend to be optimistic and you know implies been I think tripling or more than tripling every single year in terms of revenues so okay I think we're on a very strong growth path we want to obviously continue to grow the company building it to be as great as possible and we'll see we'll see I think know the startup world it's a roller coaster so you never know what's going to happen but please right now I'm very optimistic in the future okay good luck for that journey thank you very much yeah fontina this is Robin e at fedora sheep talk and thank you for twenty four he's sharing of agreed insights and also stories thank you yeah thanks a lot thank you

Channel: Crossminds

Views: 150

Keywords: big data, open source, druid, data analysis, entrepreneur

Id: EOeSYYDGFHk

Length: 28min 0sec (1680 seconds)

Published: Fri Apr 12 2019