Inside the Mind of the Chief Architect at Index Exchange | Big Ideas In App Architecture

[Music] welcome to the Big Ideas in app architecture podcast Josh how are you doing today I'm doing excellent yourself I'm doing all right it's like it's like a scramble as I was saying earlier right Monday you kind of have to go through a bunch of things that you thought you will do on Monday morning and then you realize that you left too much on a Friday so that kind of thing how about you oh I I have a conference that I'm going to be speaking at later on this week so uh I'm trying to get everything done before getting up incredibly early on Wednesday to start the the travel and the the prep for that so it's going to be a busy week for me when I was talking to Josh before and I was super excited with his background and just for everyone uh Josh has been a Chief Architect for about a decade Plus in my opinion right uh and when I went through your um you know LinkedIn I saw you've worked at FICO about 17 years as a Chief Architect working across multiple different projects you worked on you know their demand or decision making platform if I'm not wrong decision yeah decision management platform management platform there you go uh you did uh work on the AIML side of things as things were renovate innovating uh and then you've also now uh the Chief Architect working at index exchange which is an adtech business uh and I had no idea how that business worked until I had to start researching about it so it we want to get into all of that today so as we just begin you know uh tell us a little bit about your FICO experience and how did all all of that start actually yeah so interestingly enough um FICO more or less started for me in high school um and amazingly enough it was because of the internet um I came up through the Boulder Valley school district and the Boulder Valley School District was one of the very first school districts that was on the internet and as part of that they actually had a group of students who got together and sort of were system admins very early system admins for all of the other students on there because a lot of the teachers didn't yet understand the technology and uh that that uh uh connection um was provided by the Colorado internet Co-op which is one of the very first internet exchanges in the US and a company called exor way back when um was actually responsible for maintaining the Colorado internet Co-op and I ended up eventually going and working for them uh then basically the dotc stuff started spinning up and that company started doing more work inside of the marketing space right uh then the dot crash occurred and the company very quickly sort of uh like a lot of other do uh companies started to have to challenge and change their business model uh eventually we were acquired by a company called uh FICO which I I think a lot of people in the United States will be very familiar with them uh because of the FICO score uh which is a score that basically predicts your credit worthiness um but it also is you know a company that was involved in like uh fraud detection for credit cards it was involved in uh figuring out uh credit line increases it was responsible for doing targeted um advertising as well which is kind of how I I ended up coming into the ad adtech space a little bit although that was a little bit more sort of personalized oneto one uh marketing um and then eventually uh inside of that marketing space we built uh this idea of a platform we built the ability to really make the best offer the best next offer for a consumer based off of what knew about that consumer so that we were giving them more and more relevant offers um and that was pretty successful uh one day I was kind of sitting down I had a conversation with the the CTO of the company and said hey you were really successful in building a a a a platform for marketing why don't you come build a platform for financial services for everything else that we did and so the decision management platform was really an effort uh to build a uh a platform that would allow people to build Analytics and decisioning into their own products in a selfservice way and it's sort of the foundation of what FICO eventually built out in terms of their decision science uh capabilities right so one of the most interesting things is like I have obviously since I moved to America I've been very conscious about my FICO score because I don't and I've that's what I knew I knew about the company uh when you started at uh FICO was it this establish uh was it like a still a small shop uh because credit cards I think were still there but the whole process was was different right from where it is today well so so when you think about what the credit score actually is the credit score was an attempt to democratize access to credit so what that enabled people to do is basically show that by paying their bills on time being good with their money Etc they deserved credit and not necessarily just because of who they knew uh what color their skin was where they lived all of those factors and you know frankly it's been out there since the 70s right and so this is not new it's probably the most important model that affects your lives and not a lot of people always know about it and from that point of view it's really important it's very difficult for us to kind of understand like Society really did not have these algorithms and when you start saying uh the point around this is the most significant algorithm that I get used to more than my Netflix algorithm you know it's pretty pretty true because pretty much everything that I do on a day-to-day basis I am using some sort of a FICO score or Equifax um has scored that all of these scores kind of determine how I kind of live and base my life upon so going into this like when you started your career uh working at FICO um did you know about all of this or was this like an education that you kind of got once you got into the company oh no it was an absolute education and right the reality of it is is that all of us kind of interface with these systems in our day-to-day lives and we don't always necessarily understand them and so learning about them has been sort of really fundamental in terms of us becoming you know better um citizens in a real way understanding how these different uh systems interact with our lives in many ways it's the exact same discussion that we're having today about Ai and ml when you're starting to talk about sort of the what does it mean for us to live in a world with these large language models that are giving us you know answers back to our questions and all of the ethic considerations that come with the ability to do stable diffusion and deep fakes so that education journey I feel like maybe I was one of the first people alongo education Journey but I'm certainly not the last I think in general technology literacy and how these models work is becoming more and more important right so let's expand that a little bit I would love to like before we go to index exchange learn a little bit about how did you guys use Ai and ml obviously initially uh at you know FICO obviously you have a lot of data you're using that probably running regressions or classification models or it's a gradient boost and of a bunch of things that you did at the time could you expand on that a little bit and uh without giving us a secret source of what you did but kind of tell us a little bit more yeah and the reality of it is is it it never was just one thing I mean you start with the standard scorecard model that's obviously at the basis of the FICO score um originally at least but you're always adding more capabilities you're always adding more um insights into the model in some cases you're adding more data to to the models that create stronger and stronger signals and so a lot of what uh FICO did was to bring tools that allowed for greater and greater um use of data in order to drive insights that were actually meaningful um so when you think about the credit score that started with a scorecard but then you start thinking about fraud detection and then you start getting into outlier models and then you start thinking about all the different techniques and the reality of it is is it's not just the strength of the technique it's also for example some of the underlying characteristics of it so uh a good example here is you know we started taking a look at deeper neural networks and things like that and the question then becomes okay how do you balance some neural network Technologies uh with the ability to be explainable right how do you balance uh some of these Technologies with the need to protect and certain circumstances against abuse how do you deal with adversarial networks things like that as well and so it it isn't just that oh there was some new technique and that new technique opened a door it was there's a whole variety of different techniques that data scientists can use our job was really putting more of those tools into the hands of not only our own data scientists but data scientists and business users across the world right and what kind of Technologies were you guys using I mean obviously FICO had so much data obviously but you know 2007 2008 once you got in there was also the birth of the cloud and a bunch of other things were going on over there so how did that enable obviously data growth was a part of it we started becoming more uh digitally informed people start making more transactions online how did that affect uh the kind of systems you were building so the the real challenge there really wasn't data in many ways because frankly a lot of what we were doing was putting tools into our customers hands um and they had the data sets in inside of there so for example the FICO score FICO is responsible for developing the score but the ual score is run not by us but by other credit bureaus it wasn't run by us but by credit bureaus and whatnot um but in a lot of cases what we really want to do is again sort of putting the power of analytics into everybody's hands we really were focused on things that you wouldn't immediately think about in terms of decision science it was things like uh containerization it was things like leveraging Cloud earlier on it was things like serverless functions later we were one of the very first big adopters of open shift before it went to kubernetes we were one of the first big adopter of kubernetes we were one of the first big adopters of eks all of these different Technologies because what they allowed us to do is actually build this Cloud platform for supporting everybody in terms of what they were trying to do with their own decisioning processes right and so as a Chief Architect working and you're going through change obviously how did you go about U you know understanding what solution makes sense for what kind of use cases for you so it all begins with exactly that begins with the use cases it begins with from a product perspective what are we trying to do um anyone who kind of starts with the technology first um you know might be missing something if you're starting with technology from the point of view of hey what new thing does this fundamentally enable me to do that I've never done before then I think it's a really interesting effort but if it starts with okay I've got this database this dat and This Server platform you know I'm going to go do X Y and Z that's typically C before the horse so what we really want to find out is what is sort of the core use cases in our case the core use case that we wanted to enable is we wanted to take power and put it directly in the hands of business users and to do that what we really did is we said okay is there a world in which for example uh we could build a rules engine that would you could provision and write your own rules without having to have a human in the loop is there a world that where somebody could upload a model and have that model execute against their data set that they own not us and our data set right and make it so that they could drive insights out of it is there a world where we could sort of capture the outputs of various decisions uh in a way that it was immediately available via reporting without having to go through all of the steps that we're typically involved in sort of an analytics and data process so that's really what we kind of focused on in terms of Technology enablement 17 years working at a company obviously you've done a bunch of things uh before we jump into index exchange I'm really curious what was your favorite project to work at UH at FICO like something that you you were like this this product really gave me an insane amount of experience and love for technology I'm going to go back a little bit before the decisioning platform days to kind of answer this specific question uh we did a project for um Chase Bank and the US Navy where we put on board um clustered computer systems on board Navy ships to do online banking because ships go out to sea for long periods of time you actually still need to be able to do banking on ships at Sea and so what does it mean to take that kind of aspect of something that's built into our cultural fabric that we're so used to and actually make a standalone server that you could shove onto a ship and support all of the people at se so that they could continue to have a financial system to support the functions of the ship and whatnot when it was I see that was kind of a really incredible project I think it's still one of my favorite projects that I did in my career so you still have a server but it's not on the cloud but it's still working and serves pretty much all the transactions and does all these things that you have to do wow yep and this was a long time ago I mean this was like early 2000s so you know I haven't I haven't kept up to date to see what the latest capabilities are but when an aircraft carrier goes to C there's a thousand people right that you have to support and so you have a whole ecosystem you have a whole set of financial transactions you need to support when they come into Port you have a whole bunch of different things that you need to do in terms of being able to buy something at at at a um at a store more more or less on board ship while you're out of sea how do you support those different types of use cases all of these systems we take them for granted in sort of our daily lives right we're used to being able to go to Amazon and order something we're used to being able to stop by the ATM or stop by uh any place and just swipe our our credit card and yeah that's relatively new that's relatively modern so what are the systems that actually kind of fundamentally enable that behind the scenes that's an interesting question very cool I mean that's awesome well thanks for sharing let's let's just switch into uh something that I was really curious to know about like having worked 17 years at a company and done some incredible work and work that is influential to you know people like me and even you who are who's using all of this right like what inspired you to transition from working at FICO and then taking this new role as a Chief Architect at index exchange so there's a a set of fundamentals to architecture that I think are pretty Universal no matter where you go to um but your ability to use them differs right so when you're thinking about uh something in the financial services world you're you're living inside of one environment where you have corporations that are used to moving at a a particular speed you have um development practices that have grown up with an incredible amount of maturity over a long period of time you have a certain volume of data that you want to to deal with as I was working at FICO one of the things that I really wanted to exercise as a muscle that I didn't get a chance to exercise is really the scale muscle I wanted to be able to build really complex systems that were capable of executing at a much larger scale um than some of the things that we already were doing and so I'm not talking about you know hundreds of transactions a second I'm talking what do you have to do to handle trillions of transactions what do you have to do to to get up to that next level of scale so that was one piece the second piece is is I really wanted to kind of focus on looking at engineering cultures that were really dedicated to Excellence that was something that I had at FICO that I really appreciated and as I was kind of looking for the next thing I really wanted to find a place that was really focused around just having um a real sense of craftsmanship and what they were doing and combining those two things uh just an incredible craftsmanship and what they're building Plus in scale gives you the opportunity to take a business and really ramp it up to the next Lev level and that that was the the exact combination that I was looking for and that's I think what I found very much at index exchange wow that's amazing so for people who are less familiar with index exchange you know how would you describe what index exchange does and how it distinguishes itself in this competitive land landscape right obviously one of the things that I know about ads is I know about Google has their you know add Solutions you know there are certain competitors in that space are those competitors like Google ad manager your competitors as well so how would you describe it for everyone else index exchange is a global advertising exchange or Marketplace uh index exchange represents the sell side so and we're connected to the buy side we work for basically Publishers uh who might have web pages uh applications podcasts video feeds and we make it possible for them to connect to people who are looking to buy specific advertisements and place those on it we do this on demand we do this connection when a page is being rendered or when a podcast is being played or when a um uh when a video feed is going on and we take care of everything from detecting and fighting ad fraud uh to making sure that ads might be appropriate uh to making sure that you can deal with the scale of it um all of those sort of behind the scenes challenges and many more um we do that as a service as we're connecting the front end to the back end um right and this model is really kind of critical to the open internet it allows for Publishers to monetize themselves without having to necessarily operate at the same scale as some of the biggest players whether that's the same scale in terms of being able to handle all that traffic or whether that's the same scale as having all of the commercial relationships it basically opens up the marketplace uh and allows more people to get involved in that transaction tell me what is the challenge uh you know obviously it seems like for for you if you have to run real-time auctions on this real-time experience that you are creating for your attech business um for these companies what are the different challenges that you feel are U that you're encountering and trying to solve uh first and foremost volume right there's an incredible amount of just monetization that happens on the internet via these ads and it is just a profound amount of transactions coming in and in those circumstances you have to process those transactions in a very very low amount of time there's ad fraud and brand safety like I was talking about um there's emerging Technologies and platforms right we're seeing more of a transition towards video for example um there's ad quality and user experience right nobody wants to go to a page that's nothing but a whole bunch of advertisements right there's fragmentation and complexity um so not only is there the things that the industry is currently doing there's all of the new capabilities coming in and oh by the way there's also things like privacy sandbox where we're talking about changing a fundamental underpinning of how this has worked in the past uh then there's sustainability pieces there's an incredible amount of compute compute takes power power has an impact both in terms of you know monetary costs but also things like global warming uh all of these things are things that we have to kind of take into account and as we're architecting Solutions uh inside of this space and oh by the way you also need to do all of this in a way that is consistent with the environment that you're running in which includes regulation it includes uh just doing the right things by consumers as well so those are kind of the challenges that you know keep me up at night and and sort of factor into this particular problem space tell me a little bit about the first one that you said the the idea of the transaction and the volume of data that you have to handle to kind of build this experience how did you go about architecting that obviously there were certain things um already probably placed as a foundation before you came uh and what how is the infrastructure and the I would say the solution itself uh for you to handle this volume yeah and so a lot of this isn't new a lot of this I I talked a little bit about really want to go someplace where there was a culture of craftsmanship one of the things that really attracted me to index exchange was they have an incredible set of Architects already there and one of the things that they'd recently done was a full re architecture of the platform and when they were rearching the platform they were taking many many decades of experience and sort of writing a new system to support all of the different pieces that uh we wanted to do in the future as a company itself and so that sort of re architecture sort of laid the foundation for all of the things that we're doing now but how do we kind of support and build on that well from my perspective it really starts with establishing sort of technical Tech road maps for each of these individual areas and so we have a technical road map for what we want to do on the exchange we have a technical road map for what we want to do in performance we have a technical road map for what we want to do in managing our operational databases establishing sort of those road maps as North Stars or where we're going to go next allows us to have kind of the conversation earlier on about what do we need to do from an architecture point of view in order to support where we as an as a Corporation and as an industry eventually want to go to that makes it so that when we start to think about you know a year down the road a year down the line when we're thinking about what's the next set of challenges hopefully we've started to anticipate that or put in sort of the the general direction we're going in so that when we get into these use cases we've got the foundation in place and we can build them aggressively um the second piece is really uh we want to build a sense of craftsmanship across the board because by doing that what we're enabling is the ability to move faster in the future if we take care of building it right now not skipping the things that aren't necessarily the things that you always think about first thinking about you know how do we build quality and upfront how do we build in operations up front how do we build in all of these crosscutting concerns before we get into the individual systems when we get to the point where we have to operate at scale and scale can mean two different things here it can mean the scale of the transaction volumes and the latency that you've got but it can also mean scale in terms of the number of features that we're turning out and whatnot having those foundations in place make that faster and faster so that's another key thing that we're trying to do from an architecture perspective oh that's brilliant to know um you you talked about the re architecture you know so I'm I'm guessing are you guys running on the cloud um and did you have to make decisions because obviously you run this solution globally so you have to run it across multiple regions and you have to consider latency experiences for your users um tell me a little bit about how how that uh went about yeah so whenever it comes down to any sort of how do you deploy something you get really driven uh by what your business requirements are um so a lot of people will bring up the Netflix example and say oh well Netflix went onto the cloud and look how good it is well that's not exactly true Netflix built a system that allowed them to go out and put data feeds as close to the consumer as humanly possible that's not necessarily running in the cloud sometimes it may be sometimes it may not be right but what they really focused on is how do we do whatever we need to do in a way that makes the most sense for the problem that they're currently looking at right I think we very much take a similar thing we are running in some cases in our own on our own metal um what we're really focused on is how do we meet our customers needs the most effective way and then how do we do it in the most efficient possible manner right at the end of the day we are an an efficiency company right we are in the middle of these transactions connecting supply and demand right we have to do that providing the most value at the least cost possible because we have to take those savings and pass them on to our customers and so it's really not about having a dogmatic point of view about where you're running it's really much more about how can we best meet the needs for our our customers and in some cases that's also Cloud you know it's like if I'm sitting down and I'm doing some sort of uh task that's better suited for the cloud we have no problems going to the cloud for those T of problems but it is not a one siiz fits all solution you talked about database operations and you're talking about these scales and transactions and volume and this can be sometimes people scrolling and things like that a lot of data moving around on the database side of things what what kind of databases do you use uh you don't have to give us exact but what are your favorite databases to use there and there's so much religious wars around all of it so do you want to standardize on a single set of databases that makes a lot of sense from an operations point of view because supporting fragmented databases gets really expensive uh sometimes there's database uh technologies that uh can do something that other Technologies simply can't do so you know certainly um the calner versus rothing is you know a key Technical differentiator and I've over over my career I have been an early adopter of many databases trying to chase specific specific advantages um again it falls back into the what does your use case ultimately require because use case ultimately requires you to do you know something that's not easily access accessible um sometimes you have to look for different database options uh more and more I'm convinced that it it's not really the database that matters as much as what is the architecture around the database and the database is just part of it so if you're dealing with a heavy microservices environment then it really doesn't matter necessarily as much which databases you're using as long as you keep your data model segregated because it allows you to um choose the best database for the technology without having some of the operational overheads or some of the complexity overheads or The Accidental complexity overheads more specifically because the fact that you've got the database scope to something narrow on the other hand if you're using maybe a more traditional sort of data uh warehousing based approach you've got to be really careful about your database because you're shoving everything into the same database and you're building in a single point of failure for your entire company and so a lot of it f you know really depends on what it is that you're trying to do at the end of the day curiously you know I've been thinking a lot about gdpr and this whole idea of you know data security data privacy and okay let's let's say in the context of jna a lot of people are saying hey I don't want my data to be trained for you know all of this I don't want the model to learn how I'm getting used obviously that's that's the thread from which this draw but it also applies to what you guys do at index exchange because you have to kind of when you design uh you know your solution to consider data privacy and data regulation so how has this impacted how you make decisions around architecture when you're building this product out at index exchange one of the things that I like telling everyone is that architecture is not just about lines of code right um just like software development software engineering is not just about lines of code it's about everything else uh when you architect you don't just architect for how do I make system a talk to system B you have to architect it for the environment in which you're operating in and when I say environment in which you are operating in that doesn't just mean the servers you're running in it also means what's the regulatory environment that you're dealing with what is the cultural environment that you're running in what do you consider ethical right are you making sure that you stay within those bounds of what it is that you're actually trying to do so uh gdpr is a good example um there's a couple different ways that you can approach gdpr one of which is you can just kind of throw a a slap Dash process on top of it and say okay that's that's you know enough for what I'm actually trying to do or you can really think about what are we trying to do with gdpr what are sort of the core principles of gdpr and then what are the core principles of things like secure by Design private by Design all of those things and how do we really internalize that into the fundamental architecture right that we're trying to do and I I firmly believe that as Architects you have to take that second point of view you have to take the point of view of this is my operating environment and architecting for that is just as important as architecting a decision about which database you use or what language you're using or what your web services ultimately are what are the pitfalls then like I know I have had experiences where we design for everything and then we start thinking oh we had to think about gdpr so what are some of the pitfalls around not looking at the right things when you're designing that specifically what I would say is there's an idea of intentional design and an idea of emerging design right uh those tradeoffs more often than not have to do with sort of what I would say a misbalance between intentionality and emerges um most of the time when you're making architecture decisions you actually want to be making the architecture decision at the point in time in which you actually need it and it should be made it by the people who are actually implementing it or the business users that are actually close to that decision right um but the reality of it is sometimes you need long-term direction and you need to align that short-term decision into a long-term Direction and that's really where intentionality comes in and so um typically what I would say is when you see that problem it's because somebody said okay we're we need to support this and therefore we're going to rearchitecturing right right in reality I think what you do is you say we're going to make an architecture decision that we're going to support you know privacy by Design whatever and then you basically set that long-term Direction and then you allow the decisions as are coming out to be made but you make sure that those decisions are consistent with what your long-term vision is and that way you're not necessarily wasting effort um or designing Castles in the Sky is the other way I like to put it or I Tower architecture is another way to put it but on the other hand you've laid enough of a foundation on there so that when you are building the towers and the buildings and everything else that you need that you have a structure that allows you to move forward with that I wanted to go back to a thread that you were kind of mentioning about the volume you know how your systems have to be available all the time because the Real Time Auctions are happening you have to make decisions uh and your users have to make or customers have to make decisions so how do you design for scalable and you know scalability and resilience in your architecture like what kind of decisions go to kind of manage the high volumes as well as the velocity of your programmable ad transactions I was one of the very first big Believers loud Believers in containers and microservices and the reason I was a big believer in those was because they gave us the ability to have independent failure deploy independ independent failure domains and independent deployment domains right so for what we were trying to do which was really focused around self-service empowering the business user all of those things that made perfect sense because it gave us a way in which we could isolate resources protect those resources have a full life cycle for those resources Etc so the microservices model made sense um does that model necessarily make sense in every use case no it may not make sense to have something with 50 different microservices if you're out having to handle a real-time card swap or real-time auction scenario you want to look for different architecture patterns and so the way that you tackle these problems is really by understanding what it is that you're trying to accomplish and then letting the anal or letting the data in this case what the requirements are drive your decision right I.E which architectural patterns that you use and then it starts to become a little bit more San you know you say okay if I need to hit this then and I need to have these kind of failure characteristics it becomes easier to say okay where's my load balancers you know am I using a microservices architecture am I doing X Y or Z am I using you know python for my back end all of those type of decisions can get Scaled out based off of sort of your core principles but you better know what your core principles and your core use cases are every time I get someone on the podcast I'm selfishly so excited to just learn how they kind of look at problems and it's so amazing as to with your experience in the space how easy it is for you to kind of break down something so easily so it's just natural for you now and it's amazing yeah it's it's not always easy but it does come Scar Tissue helps with this particular area I'll put it that way for each that's kind of another thing though yeah with every decision you get right you're going to get X many decisions wrong right right and so the other thing that I would say is you know with all of this is collect data learn what's working learn what's not working and pivot quickly when you need to right uh failing cheaply is an asset at the end of the day so yeah I think and I failed I failed plenty it's fig out how to I mean I would be surprised to surprised to meet a Chief Architect who doesn't say well I built something and was part of something thing that did not fail at all you know you won't have that but I think what you made uh the point around executing uh with speed as well as failing cheaply is such an undervalued thought you spend hours and days and weeks and months into something and then you realize uhoh this is not the direction we want to go uh so it's so critical so I want to jump into that how do you help make that decision at index exchange or in your real life you know like do you have a principle like we'll look at this for two weeks or one month and then we'll scrape this or we'll go in this direction how how do you make that happen the principle of it is build your safety net right build the things that are absolutely key to you being able to run your business right do you have observability in place do you have quality in place you know build a checklist right come up with the things that are absolutely critical but then put in place kind of a life cycle that allows people to experiment quickly right start out by encouraging them to go do quick experiments of not only sort of technical things but maybe also business things hey what happens if we provide this feature to a customer do they pick it up those type of things yeah then have a solid plan on how to take your learnings from those Discovery phase things and throw everything else away because you don't want that stuff and start to build your software this is also part of the scaling thing right take the learnings you've re HED as much risk as you possibly can build out your first set of services don't try solve every single problem at at the very first moment don't try and build a TSH Mahal up front but figure out what the shack in the corner you need to build is and start building that piece be intentional about the decisions that you're making right do a review process however you want to go after it we use architecture decision records ADR processes things like that right um but do it in a very iterative fashion and then as you start to get to the point where you've got critical mass start thinking about what do I need to do to mature this what do I need to do to make it so that it is a A system that I'm not worried about in production uh what do I need to do to make it so that it will scale further and further as I get the base pieces in line and you know I think that that Core Business philosophy right try something see if you've got Market Market fit pivot if you don't right that entire mentality is not just a business concept it can very much be an architecture concept as well and should be an architecture Concept in what so you spoke about uh some of the things around you know mentoring and what your guidance is for Architects and Engineers you know uh what what are some other things that you can kind of share right now that Foster leadership and Innovation for people who are listening I think a big thing is finding the Champions the people who are really going to drive Excellence at your company at your nonprofit and your community and really doing everything you can to empower those people um one personal thing that I have is just trying to make sure that we are getting as much diversity both in terms of you know uh people of different ethnicities but also diversity of thought as possible into those decisionmaking processes as possible because that type of Engagement really encourages an environment where people can challenge ideas where people can come in and say hey I I saw you want to do it this way but if you also did it this this and this you could achieve something much greater software engineering is like the ultimate example of 1 plus one equals 10 right the parts that you put into it aren't lines of code the parts that you put into software engineering are processes and decisions and people and passion and whatever you can do to kind of encourage that the better off the product that you're going to get at the end ultimately is and you can always go and work on your quality you can always go and work on building a more inclusive environment you can always go and build all of these you know better systems but it's really pulling all those things together and structured decision-making and then having a passionate set of again I'll fall back on that term I keep using Craftsman to go execute it that's really what is going to drive your success over the long run 100% yeah and so as a Chief Architect at index exchange technology doesn't stop right like you are working on something and every damn day every damn hour there's a new thing coming out I've been following some of the really cool Open Source Products that you're supporting like the open C3 project and things like that uh what I'm curious about is that how do you keep up with tech Trends and how do you continue to learn about what's happening what's your formula uh so open C3 is a great example of a project that I love uh so open C3 is kind of this amazing uh open source project that's out there for literally doing management of satellites um I saw that what I absolutely love is I love seeing people who are passionate about their projects I love seeing people who get passionate about a specific bit of technology that they're interested in because when I see somebody who's passionate about something there's a reason that they're passionate for it and I don't always know the reason for it but simply following their passion and what they're engaged on will so often tell me about something that I knew nothing about and open my eyes to some new technology that's coming or some new technology Trend that's coming um or some new application of technology in culture and in in u the government that I wasn't aware of and so looking for people who are passionate and then seeing what they're passionate and trying to get a sense for why they're passionate about it more often than not leads me into technology that I wasn't aware of um so that's one thing um I think another thing is um Steve Jobs used to talk about the intersection of technology and Humanities interestingly enough I find that that's a really good way to keep track of the technologies that are really going to matter in the future so a lot of what we're seeing now with large language models and whatnot are actually driven by some very fundamental questions about you know how we think what how what it means for the internet to be searchable what it means to have sort of this distributed web and things like that so looking for new areas where you see that intersection of humanity with technology has always been important and then the other thing is whenever I see a technology Trend trying to figure out what the vectors and time are I.E if I can do this now and this tomorrow and I've got good visibility on it what does It ultimately look like uh I think that that is another sort of key piece or key enabler if you will for staying ahead of the technology curve but I I'll I'll be honest it it never stops it always gets always there's always something new every time I thought okay this this space is getting into a mature space and to some degree right we we work in a very mature space yeah right we use Linux Linux is derivative from Unix Unix has been around more than 50 years yeah um you know the microprocessors been around for a very long time they are all iterative improvements on each other what's really changing is new ways to apply those fundamental technology blocks to new problems oh yeah um so every now and then I'm like okay you know do I really have enough to get into this next curve of Technology but it it always comes back to that same pattern agree new technology applied to to long-standing human problems one of the most fascinating thoughts that i' I've had recently is that if you take a large language model uh and use a quantized model and put it into your pen drive and put it on your laptop and go start living on an island today you will still have the history of humanity without the internet on that large language model which is fascinating right like but then what do you do when it start telling you that uh you know Benjamin Franklin was a fan of absolute despots and things like that too you know so you have to start dealing with the fact that technology can lie to you ex what does that ultimately mean you know what is the ultimate outcome of those type of Curves as well and the reality of it is is with technology I I firmly believe technolog is neither good nor bad but what we do with it certainly can give it a moral character um yeah what does It ultimately mean that we've got sort of technology and these Trends and and what does that mean for the sort of the next five years and that I agree set of question I think the Innovation curve uh that I have seen in the last decade is like is it's showing me a exponential growth so I'm really fascinated like you and I I loved your perspective around bringing the idea of humanity around what we are building and looking at it from that prism and that because it shows you so many different views right uh all right so I know we're close to the end I wanted to ask this uh last question to you was what is the one lesson that you have learned in your career that you would like someone else to kind of know today so he doesn't make the same mistake don't be afraid of failure you know the reality of it is you're not going to get every question right you're not going to get every answer right um learn from it take the next steps be aggressive you know be passionate um but also when you made a bad decision realize you made a bad decision and go fix it uh take ownership be a Craftsman right of whatever it is that you want to build um that will get you an awful awful long way Josh it's been such a pleasure having you on the podcast it's been such an I would say a fascinating conversation looking into the mind of a Chief Architect you know I pretty sure I'm going to suggest that as the title for this podcast but where can no they'll probably come up with something better but the question I wanted to ask you is where can people find you uh is it LinkedIn do you have a Twitter uh where do you post stuff where can people follow uh Josh's activity yeah so I'm on Twitter uh I'm uh Josh prisman on Twitter I'm also on LinkedIn uh I am talking more and more about um privacy sandbox and some of those Technologies at industry events and yeah we follow you everybody listening in uh go in check out uh Josh on Twitter as well as his projects that he's working on uh fascinating thing that they are working as a company on is index exchange uh some really really brilliant things and a really curious about um do you also have an engineering blog or something for index exchange that people can yeah so there's a couple of great resources there's an engineering blog for index exchange there's also for people trying to understand the attech ecosystem a little bit better um we do a series of videos called index explains that's absolutely phenomenal um you'll see members of my architecture team have been doing some of those as well that's where you go and learn all right so once again thank you Josh for hopping on it's been an absolute pleasure and thank you everyone for listening in I'll catch you in the next one [Music]

Channel: CockroachDB

Views: 109

Keywords: Big Ideas In App Architecture, Chief Architect Index Exchange, What is Index Exchange, fico software architecture, ethical considerations AI, privacy-focused ad tech, cockroach labs, adtech, tech podcast, How does FICO's model work?, software architecture, enterprise architect, how to create a multi-region application, how to create a low-latency app, cockroach db, cockroachdb, emergent design, How to design for scalability and resilience, software design principle

Id: t-ei4w6Rc4E

Length: 44min 34sec (2674 seconds)

Published: Thu Apr 18 2024