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so the big question uh for our guests today um Adi Folger from Redis Labs and we've also got

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Cassie Zimmerman from Redis Labs on as well big question is about what's different about data as

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you modernize thinking kind of modernizing monoliths to microservices uh but what's what's

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different as you kind of get into the data layer uh obviously like all the the sort of the 12 factor

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principles don't really apply and so you know what do you do and what what are the challenges

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that are different what are the goals like the kind of why are you doing it at all yeah that's

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a great place to start drumming um I think really uh it's going to come down to a few different

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things um usually it's going to be some kind of response time driven or some kind of user experience

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driven initiative either uh you have some kind of competition that's pushing a faster website load

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time or just a better user experience like I I had one experience at my previous job where

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it was all about kind of the all-encompassing user experience and that was how we were losing it they

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were just they were just beating us out on the on that whole multi-dimensional thing um so ultimately

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you kind of have to get to a few different things right you need to evaluate how expensive the data

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accessing process is both in the context of time as well as in context of dollars um and then if you

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want to do some of the more personalization or some of the more advanced things that we're doing with

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data science today you need to have a more sophisticated data architecture um than what most companies have

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have historically had um and I think let's let's kind of just start out with with the the easier one which

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is uh things need to load faster right so I worked for a CTO who was part of his uh his evaluation process was

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the website had to load in a second or less um and that can be a really big challenge for a lot of a lot

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of companies because um most people that are kind of getting into the industry today aren't really

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we haven't really had the experience with performance minded uh development it's been a lot more like web

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web development which is usually a lot more lenient when it comes to these sorts of things um and then

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this is where kind of the the reddest part of the story comes in right so we we we have a slide that we use

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all the time that shows like when you talk to the database it's going to cost you 100 milliseconds

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um the response time for the web layer is you know 50 milliseconds um and all of that time adds up

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um but using redis for caching or as the operational data store um is how we've had several really really large

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customers move beyond um this challenge right and then on the the personalization and the kind of data

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warehousing analytics side um being that front-end high high ball highly volatile buffer um has helped drive a lot of

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other uh a lot of other initiatives as well yeah i i want to add one thing on that audi just taking a step

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back and looking at you know where the enterprises come from cloud native development i know we were

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on a podcast with you guys or audi was on a podcast with you guys months ago or maybe a year ago at this

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point i'm losing my track of time but um we talked about hey we talked a lot about cloud native application

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development but we're not so much talking about what's happening with the database there and i think

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kind of from day one it was very much state from stateless kind of don't have your database running

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in with your application keep it external in the monolith and what we've experienced and audi can get

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into the specific customer examples of this is people are realizing that you need to think about your

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database and what you're doing with your data from day one in order to have that instantaneous user experience

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um whether you're in the financial industry or the retail industry or you know insurance whatever it is

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but being able to say hey we're not just going to keep our database in the back end in the database or

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excuse me in the um in the basement somewhere but we need something more flexible that can react in real

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time and a lot of that comes from tools like redis that can be implemented as you know a cache for your

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microservices architecture um or you know other no sql more flexible databases like mongo db or um you

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know my sql and things of that sort so kind of taking you know application development and cloud native

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uh you know features and functionality or you know i guess that's the right word um but also thinking

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about what tools you need from the database perspective in order to get you there so that

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that your applications are running the way that they should so there's a couple couple questions

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so if we look at things like auto you mentioned solving for faster load times is something that's

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different about uh how you deal with data as you modernize so are you saying that folks have

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had that kind of problems solved before like as a in the in a monolith world they sort of had that solved

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but by modernizing they reintroduce it like why is this something that's different for modernizing as

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opposed to just hasn't making sure you have faster load times been a problem for as long as like

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internet commerce has been making yeah like that that's been actually i mean for your business well

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i i think i think there's a there's two parts to that answer one is um

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certain really big websites the the facebooks the googles of the world have kind of set the standard

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for this performance um and modern broadband internet is still kind of relatively new if we think

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about it in the grand scheme of things like yes we've had we've had internet we've had fast internet

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for a while but um the whole notion of it has to be there now right now now now that's that's a little

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bit more kind of you know post 2010 um and i think why is because of a more customer user centric focus

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um that's kind of been coming in and increasing in the industry and this is of course you know what

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you guys the pivotal do all the time right is is how do we think about things from the customer mindset

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um and and the customers don't want to wait and so when you have competition or you have a standard out

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there of hey this is expected to load this quickly and i shouldn't have to wait for my data or for my

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experience whatever that might be um it it just it they they've they've moved the goal or they they've

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set the the bar higher for everyone else i i that i think that kind of gets at what what you're getting

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at right but i i would like to add i i second what you said audie and i think that user experience now

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in this digital world is like the most important interaction that your customers are going to have

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with your business so it's not just you know your static website that your customers are going to

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but it's your online banking that you're using from your mobile device and it's tracking your shipping

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codes from your mobile device to see where your package is wherever in the world and this needs to happen

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instantaneously and that's a total shift in what businesses had to do even 10 years ago right so

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the more that we move to digital the the faster that we expect it to do and the more competitive it is

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with the the enterprise landscape to to get the customer experience there so are we modernizing because we

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need a better experience and better customer experience and then that has data implications or are we

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modernizing and then that has performance implications that we have to go fix i really kind of want you both to

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blurt out like an answer at the same time and it's not the first one in my opinion i know what were you

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going to say i'll let you go first chocolate no vanilla yeah no the i i don't think i don't think

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people really go about hey we need to modernize for the sake of modernization um it's it's really

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it's really usually driven by like i said competition or or customer demand um sure kathy what what do you

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what do you think i would say the same thing so i would say um the reason that performance has become

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so important isn't necessarily because um you know people want to just try new technology and and re-architects

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the way that they build applications but um the the internet giants today right uber airbnb twitter

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all of these you know very successful companies that were born in the cloud have set these expectations

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for users to be able to you know have real-time experiences use it from anywhere um have global

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access all of these different requirements that i think has gone into what cloud native means and that's

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just because of the changing user requirements out there in the world and that's why you see

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you know fortune 500 fortune 1000 companies having to say we need to increase the performance and

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improve the user experience for our customers in order to retain them um so you know i think part of it

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is you want to make our business better every day and you do want to be at the forefront but in general as

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trends move you're going to need to re-architect the way that your database systems and the rest of your

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applications are running because of the demands from the market and i think too um there's another

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component that's more than just straight performance it's also uptime right so in the same vein you don't

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necessarily uh you you need the website to load fast but if it goes down it can't be down for more than

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five minutes right and i think that piece is is actually more intrinsically tied to the cloud native part of

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things um because that's not just redis or databases that's the entire application that's being able to

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do multi-cloud that's being able to do hybrid architectures right and this is this is of course

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what what tcf it helps enable people to do and so um the fact that we can be bundled in with that concept

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that we can be a part of that platform um i think it's helped these really big customers that we're

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talking about um be able to to get over that hurdle and not only solve the performance problem and have

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a better user experience but also be able to solve the ha problem and the scaling problem and you know all

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all of the other things that modern software has to be able to endure in order to be successful

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and that's not going to come with one tool or one product right you're going to need

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a platform you're going to need the infrastructure you're going to need databases that are supporting

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that um and a whole lot more so it's important to look at the full picture which we can talk about more

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um so you mentioned something also earlier uh i might be mangling this but i paraphrasing you

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mentioned something about you need to evaluate the essentially how you value your data and how you value

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access to the data can you expand expand on that a little bit what do you mean by that i heard some

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statistics recently that we're creating like uh you know some some ridiculously large amount of data

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every day um and and more and more importantly especially if you look at kind of the the bigger

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conversation that we have going on in the us and the rest of the world about uh how how much our data

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uh is is valuable right so how we have all these big companies who are making a lot of money off of

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things that people are willing to give off for free so um let's let's kind of take that concept and

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look at it through an enterprise lens right you can you can generate all the data in the world but if

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you don't know how to use it if you don't know how to manage it if you don't know how to serve it

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um it's really just bits sitting on a hard drive somewhere right um and i think there's a there's a few

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different dimensions to that um one is how does it fit into the nature of your products right so um

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in in the the company that i that was acquired by mbcu that i worked for um a big part of that

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was the database of all the movie information that we had gathered and all of the the like low level

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tagging and um kind of the the level of granularity that we were able to provide to that um was a big

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part of the value um and that that was sitting in a sql database and we figured out how to how to

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leverage it um to ultimately drive things like seo and and and ultimately back to that user experience

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that is kind of the the key theme here um but we're kind of the next phase of that thought is um the

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larger translitical conversation which is what we were having you know when i was on y'all's podcast last

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year um around how do we get data out of the production system how do we use things like

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machine learning to provide insight um and then ultimately make that data more valuable and i don't

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think genuinely um that most most companies are really there yet i think they've kind of over the

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course of the last few years kind of gotten a hint or a sniff that that's that's a thing that they need

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to do um i don't think most of them know how to do it um but but for example kind of the the tracking

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the the tracking example that cassie cited before that's a that's a place where it works really really

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well right so like if i can get a text message on a phone within seconds that my package is delivered

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that's improving my life right um and and that's understanding the value of that little piece of

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data to that user and that's just a very small example of what i think we should what we will see

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uh more and more of going forward as companies continue to modernize yeah i want to give one more

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example aside from you know the the shipping customer that we're talking about um but another way that

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we're kind of driving you know data driven applications is in the financial industry with pivotal so fraud

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detection we've seen come up a lot as a use case in the financial vertical and that's i mean think

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about it you're in europe you get a text message from your credit card provider that says hey we've

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identified fraudulent or abnormal activity on your credit card and sometimes that's really irritating to

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us if it's not fraudulent but if it is then you're going to be pretty upset if somebody's in frankfurt

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spending all of your money um so having that real-time response and being able to say hey cassie is not

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in germany right now she's in california um we need to immediately alert her that something is going

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wrong that's the type of application that needs to literally be able to process in sub millisecond response

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times so and to just go off of that to add a very awesome fine point um that's one of those performance

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driven situations as well if you have to wait a minute for your credit card to be processed it's

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not going to be a good user experience so that's why three out of the four credit card companies use

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us to do that getting back just for a minute to the valuing your data i mean do you think we need to take

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this to the point where we're actually putting a dollar value on data you know looking at it like a

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like we would an accounting issue i mean it seems a little squishy like yeah i can see it would be

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valuable yeah have you know that insight to that user but i mean you have to balance that on how

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much it's going to cost to do that and resources and time and energy and all that kind of thing

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yeah also potentially a liability if you want to go down that that's exactly where my head

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headwinds are made yeah um i i think it's a very good question and a very good point um and this is

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kind of why i think it's been very difficult for companies to to to move forward in this case um

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and and you know it it has a certain amount of value right you can there's an easy way to frame

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it if all of that data that was released publicly tomorrow how how damaging could that be or or how

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much value would your company have lost had that happened um and if if you don't have at least a

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ballpark figure in your head if you think about it and that kind of most catastrophic scenario then

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you then you don't really understand the value of your data um but more broadly this really kind of

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falls into the product management um and positioning kind of perspective every product out there that's

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being really successful today that i see has some component of leveraging the data that they're

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that either they get from the user or that they're getting from you know third parties and providing to

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the user uh and if you can't if you don't have an answer for these questions and if you don't understand

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like what you can do with your data then and in the long run you're just going to be left behind

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okay so what are some of the you mentioned that like most enterprises just aren't very good at this

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um and you know the like the the credit card fraud detection i mean totally get like it's very

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valuable and it's it's very um performance sensitive but in many ways it's like it's it's an existing

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use case right like credit card companies have always been in the business of trying to minimize their

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you know fraud exposure because they incur a lot of that yeah but so if we step away from that kind

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of example what are you seeing with the folks that you're working with in these modernization efforts

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where they're they're doing kind of the right things around how they then incorporate that data

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back into the products that they're building i the immediate thought comes to mind if we talk about

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kind of the newer stuff is going to be in the iot space um and and there's some really interesting

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things that we're doing um around providing tools for edge computing um and and ultimately kind of

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where i want to go with this and it's not specific to iot it's going to be um it's going to be the case for

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everyone um or at least any anyone who wants to do some of these more personalization or data driven

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sort of things um it it's going to be around ai ml and and being able to serve insight quickly back to

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where you're getting the data from so in the case of iot it's being able to serve that on the edge on a

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raspberry pi and this is something that we're we have tools that we're we have that are going to be

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released um here pretty soon um probably around the time this podcast actually comes out so that might

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be a cool thing uh but yeah anyway uh being able to serve these these ai models on the edge um being

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able to get the insights back to the user in real time i think that's the next phase of that um

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and and and we don't have a whole lot of people who really are kind of there yet

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um so being able to in the short term or bridging that gap is is just starting to understand

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what kinds of insights could you get out of your data right so for example um going back to kind

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of that movie data uh that i was talking about being able to do recommendations with that being able

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to do something similar to like what you see on all of the video on demand services of hey uh you like

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this movie so therefore you might like this movie how how could you do something like that with your

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customer data right and and maybe that fits in your business world and maybe it doesn't um but that

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those are the kinds of questions and and the things that like data scientists are really kind of getting

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paid all those great dollars to be able to figure out yeah i think that's a great point audi and i to your

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point domain in the financial vertical fraud detection will always be a top use case right but as we're

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getting into these entirely new industries and working with these cases that we've never seen before

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the business values that they're going to get out of that are still kind of unknown right they don't

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have personal profiles for all of their users and um you know what cassie does on a day-to-day basis on

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her credit card and how to identify fraud with an iot company um you're going to be tracking millions

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and millions of devices globally um and and using that for a totally different outcome so i don't know

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if we're there yet and we've been able to see that in a real life scenario but we're at least having

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really really interesting conversations when it comes to the iot iot space um and really even other

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use cases in the financial vertical as well and retail when it comes to recommendations um well i guess

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just to like tie kind of to tie this all back to the modernization efforts right that was the the

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question at hand um and so you know cassie you had some really interesting points about

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you know geodistribution uh both from a governance and compliance perspective but this is also something

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that you know folks as they modernize are also taking into consideration is you know how are we

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we're now moving away from a monolith we're having much more distributed applications we can potentially

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run this across multiple clouds we can run it in you know multiple geographies and serve users

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uh globally in a more um you know you didn't folks can get a more low latency experience if

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they're they're actually accessing a um instance of the application running much closer to them

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so um that kind of you know thinking about that the modernization work to go into that is that

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something that you're seeing a lot of folks as they modernize um they're starting to think about

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that that geographic distribution yeah i mean i would i would argue that those geodistributed architectures

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are coming up more and more in our customer conversations and not just on the whiteboard

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when we're at their offices but in production as well um and i'm sure that audi will have some great

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points to make on this but i think the work that little in that space is especially interesting because

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pcf is providing kind of that horizontal layer so that you can run hybrid cloud and you can

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have different foundations globally but aside from that you also need a database that's going to be

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able to cross replicate to be able to communicate uh across the world um in the same way that i guess

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cf kind of is and and redis enterprise and redis can provide that layer of data to run redis clusters

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across the world and be able to replicate in either you know an active passive or an active active type

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of replication model um and those requirements i think are coming up more and more with customers as

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they are expanding their global footprint from a data center perspective and understanding the value of

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hybrid cloud and diversifying what their infrastructure looks like and data you know obviously will fall into

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that point at some point when you're looking at how you need to how you need to globally distribute your

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your databases as well the concept of data governance across international borders is probably is the

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hardest part of all of this right so we have we have customers who can only host data in germany or data in

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france and and there's a lot of requirements around that and certain data can lead the country and

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certain certain data can't um and as well um with with our technology as well as some of our other databases

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out there um the whole kind of standard model of active passive is is starting to go the way of the dodo

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right like active active when you have a platform when you have multiple cloud providers or your own

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multiple data centers is a lot cleaner of an architecture and if you have the tools to be able

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to build it um then you're just going to make your life easier in the long run yeah and i and on

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kind of snowball off of that when you're looking at the developer experience to build those types of

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applications you need the tools that are i would say easier to implement than than others and i i think

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since redis is already so well known in the developer community we've spent years engineering this really

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robust architecture to be able to run your redis clusters globally so that the interface from a

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developer perspective looks the same so when they're writing their redis applications and writing code

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they don't have to worry about the ins and outs of a database in the back end and how to globally

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distribute your redis clusters um that's where our software comes in and obviously pivotal tells that

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story you know very well the developers should be doing what they what they do best which is uh writing

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good software and writing good code um and i think what we've the work that we've been doing at redis

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labs falls really good into that story of fast time to market and ease of use from a development

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standpoint when we're talking about how do we get to these large-scale global applications

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so the other question that i had that cropped up was you know talking about the the kind of hybrid

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cloud and multi-cloud is um also just you know there's a lot of there's kind of this interesting

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quandary i think sometimes folks are in comes up as they modernize it can come up with you know building

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net new you know applications as well but wanting to take advantage of native uh services on the cloud

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providers and you know there's a lot of interesting you know machine learning services as well as just kind

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of raw basic whether it's caches or sequel or what have you um and on the one hand like there's some

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great innovation happening um in those services on the other hand there's a tremendous amount of

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like stickiness to now having your data sitting with that particular cloud provider and if it's in like

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uh you know a a sort of non-standard version of uh whatever type of database or or cash or um data

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warehouse or what have you now you know that's a sort of uh a bit of a commitment to that cloud provider so

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how do you see folks navigating that in terms of well i want you know i'd love to have my data

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in the cloud i want it close to where i'm running my workloads which is i want those to run in the cloud

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um but i'm i'm a little bit concerned about really marrying this data to a native cloud service

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or like i want to be able to mix and match right like how do you see people navigating that so um once

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upon a time i was part of a cloud forklift project and it was i was we're on it for like two years or

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something like that and and the one thing that kept us from really like doing a real cloud forklift

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was uh our sql server uh we couldn't figure out how to get it up into the cloud and and we didn't even

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have the problem of like multi-cloud or anything like that um so so i think data as a whole has a lot of

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gravity um and and that's not exclusive to to cloud services right like i think that it takes a lot of

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time and energy to to build out a database right um whether it's redis whether it's sql or or whomever

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else um and and once you get it you don't really want to touch it right you wanted to it took a lot of

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time and effort to get to that point in the first place let's just leave it as it is um but to to kind

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of get more to your core point there i think in the earlier days of cloud forklifting and and the

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migration of the cloud of the bigger companies um we were seeing a lot more of just kind of hey let's

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just use that vendor service and and it'll be easy um but nowadays especially the more sophisticated

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companies realize they can't do that and they can't really do vendor lock into an individual cloud um

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the nice thing about redis even if you are using one of the cloud provided versions and order or like our

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uh cloud managed service for example is that the protocol aspect of it is very portable

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um so we we've helped a lot of different companies migrate from one cloud to the other from on-prem

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into into cloud and and every every everywhere in between um so it it's not as big of a problem with

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say like ours but if you look at um you know for example like dynamo right dynamo is very easy to get

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started with um but then you kind of end up where you you're very locked into it and it ends up being

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very costly very quickly um and and now you're stuck in that one cloud right so i think if anything

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i don't have an answer for it right i think if anything i have a cautionary tale of be careful

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wherever you put your data that's probably where it's going to go and i and i'll do do my best to try

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to help you figure out what's the best way to distribute it and this is where um you know again

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kind of the the platform story of pcf and that that horizontal dimension um is is really important um

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but yeah i i cannot recommend the opposite enough uh to people in all of my conversations with you know

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the various big companies the world i get to talk to yeah i i would echo that as well and when we're

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working with our customers who have already decided that they will take a hybrid approach

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i think a lot of them recognize that buying a particular data service or other services from

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one particular cloud provider just won't make sense for the architecture that they've already decided on

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because they understand the advantages of going hybrid cloud and multi-cloud um and luckily today

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customers have so many options when it comes to the ecosystem and cloud providers and platform players

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and you know services like redis uh have built up these ecosystems to work together and i think that's

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one of the really cool things that pivotal and redis labs does very well is making sure that wherever

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the customer wants to run we can enable them to do so and we're not going to lock them into running

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you know on x y or z cloud or platform it's it's what they want to do and you know people like audi and

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other solutions architects at our company can help guide them for what we think is the best option there

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um but having that conversation with the customer is key to understand what they're trying to get to

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what their requirements are and if if the outcome of that is going with you know a cloud service provider then

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that's fine but if your requirements really do require a data service that can

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cross multiple clouds and be globally distributed then you know you should probably have a different

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conversation um good i'm also having good healthy conversations with um you know with their teams

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about like what are we really trying to get out of this and so that we're not just taking the easy route uh

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or something that you know was the we've already been doing this route uh so that they can start to

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start to identify like what they should be doing um so thanks so much for for joining and great to have

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you and lots of great interesting stuff continuing to happen in the data world um so talk to you next time

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thanks for having us