Simon Margolis Director of Cloud Adoption at SADA on the Future of the Google Technology Ecosystem
Welcome to the tech deep dive podcast where we let our inner nerd come out and have fun getting into the weeds on all things tech. At Clark Sys, we believe tech should make your life better, searching Google is a waste of time, and the right vendor is often one you haven't heard of before. Hi. I'm Max Clark, and I'm talking with Simon Margolis, who's the director of cloud adoption for SADA Systems. Simon, thanks for joining.
Simon:Yeah. Absolutely. Happy to be here.
Max:Simon, we should start and talk a little bit about SADA Systems and what SADA Systems is. So can you give me a quick, like, 30 second ish overview of what SADA does for customers?
Simon:Yeah. So high level, we are the world's largest reseller of Google Cloud Platform. And so, that that includes both our resale business as well as our systems integration business. We provide professional services to our customers to aid their adoption and, maximize their use of Google Cloud Platform, as a whole.
Max:And you, personally, before you got into the cloud world, I I I noticed a note about a a very interesting company out here in in Pasadena, and and it looks like I'd I'd love to talk to you about that a little bit.
Simon:Yeah. Yeah. Yeah. I mean, so I I I still have to kinda pinch myself sometimes to to to believe that I really got to do this, but I I did before, I was at SADA and get to work at, JPL NASA JPL out in Pasadena, which was an amazing experience. Highly recommend it to anybody who has a chance to do that.
Simon:But, yeah, we did a lot of we did a lot of systems work there. That's kind of the thing that opened my eyes to cloud computing because we it was very early days of AWS back then when I was at the lab. And, of course, at NASA, they encourage experimentation, trial and error, playing around with new tech. But, yeah, that that's an experience I will, always cherish. A very cool place to work.
Max:I I mean, just just thinking about the nerd factor of it, it's just I'm I'm I'm already there. But, I mean, so NASA and JPL were were very progressive and and use of Linux and creation of physical hardware and massive storage servers and then, you know, different nascent cloud technologies and compute and farms and commodity infrastructure. So, I mean, you've you've quite literally seen a pretty big transition here in not a long long period of time of, you know, physical on premise to to cloud. And, I mean, that's that's a that's that's an interesting, you know, it's an interesting seat and view of the world.
Simon:Yeah. No doubt.
Max:Google Cloud is dominantly known for their G Suite, and and SADA does have a practice around G Suite. That's not what we're we're here to talk about today. But Google created the Google Cloud Platform, and and they have a a somewhat of a different slant on how they're approaching things from, Amazon, AWS, and from Microsoft Azure. You know, from a side view, from your world, how do you view the differences between these platforms and what GCP is bringing to an enterprise that is others? Yeah.
Max:That's a really good question. I think that's at the core of what, I'm trying to solve for at SADA.
Simon:You know, I think that both at SADA, we have a reputation for being a big G Suite partner, and I'm trying to, change that to make us known also as being a big GCP partner. And the other thing is that Google is seen as sort of being a, you know, I guess, for lack of a better term, a laggard in the cloud space. And a big thing that I'm, trying to change is that perception. And I I think a lot of the reason they've earned that perception is because of their approach to the market. And it is it is different than what AWS and what Azure did.
Simon:And, you know, I think there are benefits and disadvantages both to their approach. But, you know, the where I'm coming at this from, like, we just sort of talked about, right, is the fact that at the end of the day, I I consider myself a nerd, and I don't think that's a a negative term. Right? I I think that's something I'm I I wear proudly. And I think there's a lot of folks like me who are, you know, into the technology and want to use the best technology.
Simon:And I think, you know, I'll poorly paraphrase Eric Schmidt when I saw him at one of the very early Google Next Cloud Conferences. But he basically said, look, we build App Engine. Right? Platform as a service. You just upload your code and away you go.
Simon:Right? Like, Google deal with the hard stuff. What more do you people want? And, of course, you know, at the time, I was like, exactly. Right?
Simon:Of course. This this is this is all one would ever need. Right? But, you know, I think we nerds maybe didn't have an appreciation for where enterprises were in their adoption of these emerging technologies. And so, you know, what made sense to a a young engineer early in in his career, you know, maybe was not the thing that your fortune 500 organization was going to adopt.
Simon:And so I think that's at the core of the difference between how Google's gone to market with their cloud platform versus how maybe some of their competitors have.
Max:Google was definitely early with App Engine. I mean, nowadays, we talk about and we say serverless. Right? When when App Engine came out, serverless wasn't a term, You know, support for it wasn't really there. Google required a very specific data interaction, data layer that was, you know, abnormal for a lot of people used to working with, you know, SQL, you know, SQL Engines.
Max:And, they were definitely very early. But, I mean, the other side of it is Google and Google Cloud has given the world a lot of things that everybody knows, containerization and Kubernetes and and, you know, and machine learning pipelines. So are you seeing that as being, a big differentiator when people are looking at, you know, which cloud platform or cloud switches or multi cloud? Like, you you know, is that, you know, driving this conversation? Or or what brings people into Google Cloud today, and how does that that start?
Simon:Yes. I think I think you've got it on the head. I think there so so, you know, I've I've obviously I'm a little bit biased here in the sense that I I've tied my career to to the Google platform. So, that's probably worth mentioning. But I think that that is exactly it, is that they they were early in in their technology, and we're starting to see that paying off now in the sense that, you know, I remember the very first Google Cloud conference I went to where they were like, hey, there's this thing called Kubernetes, and we think it's gonna be this big deal.
Simon:And everyone was like, what? You know, it doesn't we're we're we're a VMware shop. What are you what are you talking about containers for? Doesn't make any sense. And so, they were way too early.
Simon:Now, absolutely, this is the reason that we're having conversations with clients about adopting the platform. And containerization is one such unique technology that Google has that is attractive to clients today. But you have to also remember that, you know, Google was google.com before it was anything else. And as a result, the data technology that Google has, that they needed to build in order to provide a google.com experience, is something they've continued to sort of keep their foot on the gas pedal with. And so as a result, I think they had a lot of technology in terms of data analytics and data warehousing that was far too advanced for most customers 5 years ago.
Simon:And because of the cost of storage going down, because of the accessibility to large volumes of storage, today, that's really not the case. It's not too early anymore. And so, yeah, we are definitely seeing those unique Google features being the things that are leading customers to, open open their minds to using Google Cloud Platform.
Max:So, I mean, beyond Kubernetes and containers, I mean, tell me more about this. I mean, what are the the dominant Google features in GCP that are driving customers to Google and that, you know, you're having the most conversations around.
Simon:A personal favorite of mine that I think is is a bit underrated is Google BigQuery. You know? And it it's another one of those early products. BigQuery has been around for a while, probably one of the older products in Google Cloud Platform. But I think it's like App Engine in the sense that it was way ahead of its time in terms of its power and ease of use.
Simon:But that's a technology that we find many customers taking advantage of across the spectrum. And this isn't just your Bay Area startup that's focused on data science that's using this stuff. Rather, we do have enterprises that are moving to using BigQuery where they otherwise maybe wouldn't be adopting any, you know, emerging technology just because it's so powerful, so cost effective. It's a great tool. There's other, maybe slightly, nerdier, components around the data engineering toolset that Google Cloud Platform has that are also things that are really leading some of our customers to adopt.
Simon:The thing about that is and that's why I love doing things like this where we can sort of talk about the various products that Google has is because I think a lot of customers out there, especially the enterprises, aren't aware sometimes of what may be out there in the Google ecosystem that may serve, their purpose. An example of this is we do have a lot of conversations with customers around BigQuery. However, we find that some customers, their needs are not met by BigQuery. It doesn't it leaves leaves them asking for more or or a differentiator or something like that. And, you know, in in the case that performance on BigQuery isn't what it needs to be, customers who want, you know, millisecond single digit millisecond response times, they may not know that Google has a product called Bigtable, which does that.
Simon:Right? It does deliver those single millisecond response times from reads and writes. You know, I think that there's there's gonna be another app engine, and there's going to be another BigQuery, as customers learn more and more about what the capabilities are of the platform.
Max:Let's talk about BigQuery specifically for a little bit. And this is something where, you know, a company that already has a MapReduce or Hadoop based workflow ends up looking at BigQuery at some point. You know, the the the the pipeline for that job and that analytics, you know, applies pretty well. Right? So but now BigQuery is very different in how it prices, and we have these things of slots.
Max:And that means how do slots apply, and why are people driving towards BigQuery so quickly and consuming this so fast?
Simon:Yeah. So I'll start with what you mentioned about slots. That's an it's an interesting feature to to what, BigQuery offers in the sense that you can sort of purchase reserved units of computing, so to speak. Right? That's the slot of computing and that's the thing that makes it a little bit easier to get predictability both in terms of performance and cost on BigQuery.
Simon:And of course, as more and more enterprise customers are adopting these types of differentiated technologies, those types of things become important in terms of predictability. But one of the coolest things in my opinion about BigQuery is actually, it goes against that in the sense that, well, just like with App Engine. Right? The the thing that is most attractive about App Engine is you sort of upload your code as a developer, and you kinda walk away. You wipe your hands, you walk away because Google handles the scale.
Simon:It handles, you managing the traffic, managing the back end, making sure that the customer experience is where it needs to be. I feel the same way about BigQuery. I feel that I would never consider myself a data scientist, but I'm able to do a lot with data that I otherwise wouldn't be able to do because BigQuery just makes it automatic. Right? It takes care of these things for me.
Simon:And so, I really enjoy the fact that I can write a couple queries against my dataset and say, okay, that gives me the information I want. And now I can go go from 1 or 2 queries to tens of thousands of queries a day, and I know that I don't need to do anything to make that happen. Right? It's gonna automagically scale up to, provide the performance that is required to answer those queries. And then the best part is it then scales back down when those queries aren't happening.
Simon:And so as a result, you know, unlike with traditional systems, but also unlike some cloud systems that are used for these similar workloads, I don't need to think about, nor do I need to pay for resources that I'm not using. And it's not one of those things where, hey, we're gonna scale down to the best of our abilities when you're no. The the Google doesn't price BigQuery like that. Google prices BigQuery based on how many bytes you process. And so if it takes a little longer or a little quicker to process your query, that's not your problem.
Simon:And you're not being charged for the length that it's taking or the time that it's taking, I should say, to you to process your query. You're just paying for that query. And it it it's really beneficial in the sense that I know what my costs are gonna be. I have a little bit more control over those costs. But, also, when you think about that pricing model, Google is incentivized to get my query executed as quickly as possible because I'm paying what I'm paying, whether it gets done quickly or not.
Simon:And the quicker they get my query done, the more compute capacity is available for other customers. And so, it's really this win win. And I feel like those types of technologies, App Engine, BigQuery, those are the big differentiators. Those are the things that maybe when you're talking to a CTO, you don't hear this. When you're talking to a developer, they say, wow.
Simon:Using BigQuery really changed how I work or changed how productive I am. And, that that's exciting for us.
Max:I've seen pretty radical pricing differences also for migrations into BigQuery. You know, applications that were sitting either on premise or in another cloud, you know, in some sort of MapReduce pipeline that went to BigQuery and, you know, lobbed, you know, 60% off their monthly bill for that. I mean, that's a pretty compelling argument outside of the tech of, like, why you should look at BigQuery and having, you know, fixed capacity in terms of slots. You know? In in a lot of cases, it was, well, we know how much how many queries we wanna run per day or per interval.
Max:And we're okay if some take a little bit longer to run or, you know, have to be queued out or whatever whatever the ramification was because it's efficient for us to know this is exactly what our bill is gonna be each month based on this component of our data science.
Simon:Yeah. Absolutely. And I think that's still true, and that's the cool thing about the way BigQuery pricing has been built out with both slots and the ability to not use slots is that you can kinda dial in what makes sense for your business, whether that whether that cost control takes precedent or whether the performance takes precedent or maybe a mixture of both. Right? That's what's being unlocked when when those workloads get shifted over to BigQuery.
Simon:And the thing that I don't I think gets missed a lot also which is super important in terms of time and cost savings as well is that this is much less work for our customers to use. You're not managing Hadoop. You're not managing not only are you not managing the software, you're not managing the physical layer beneath it. And so as a result, your developers can get that much more done in a day, which has a real serious impact on productivity.
Max:Another cool thing about BigQuery is it doesn't require you to use a proprietary BigQuery interface in order to manipulate it. I mean, there's it extends and there's other systems that you can interact and and connect to BigQuery. Talk about that a little bit.
Simon:Yeah. Absolutely. I mean, that that's one of the best parts. You know, we were all really excited when ANSI SQL was officially supported on the platform just because what we talk about in general when it comes to cloud computing, I think to many customers sounds too good to be true. BigQuery is a good example of that though, in the sense that we say, Hey, look, all this stuff's gonna be handled automatically.
Simon:It's gonna be cheaper dramatically than what you're doing today. It's also gonna be faster and always better. And the question is always, okay. What's the catch? Right?
Simon:So I gotta use some special tooling or something. And that's the cool part. The answer is no. You know, we have customers whose applications were using SQL to interface with their data warehouse, and they still use SQL to interface with BigQuery. You know, look, we're in the business of doing migration, so I'm not gonna stay I'm not gonna stand here and say it's there are very difficult migrations.
Simon:But look, yes, some are some are more difficult than others. When it comes to adopting something like BigQuery, though, it's extremely simple because, again, there's just that backward compatibility, so to speak, with whatever the customer may have been using before. And so unlike with some other emerging technologies, this doesn't require a dramatic refactoring of the application or of the infrastructure itself. We can simply plug in BigQuery where maybe another tool had been used previously, which is pretty neat.
Max:Right. And and things like, Tableau. Having a Tableau connector to BigQuery, I mean, gives a massive amount of flexibility and customization for somebody that's already con you know, using Tableau and understands Tableau and has reports written in Tableau of just switching where that data and where that processing actually exists from another place over to Google and leveraging Google's scale and efficiency and and, you know, underlying technology.
Simon:Yeah. It it's huge. I think the fact you can use Tableau is huge because so many customers are using it today. The fact that though you can also plug it into anything that speaks SQL is also huge because that means that your your all of your third party tooling, you know, can still work with with BigQuery as it might have worked with some legacy application. And I think, you know, Tableau is a great example, but I think this is what I think this is a big reason that we saw Google acquire Looker is to make that integration of visualization on top of these very powerful data platforms, something that's more accessible and easier to use.
Simon:You know, BigQuery is a good example of things that are compatible with lots of other services. Not all, Google tools are so compatible. And as a result, having something built into the platform like Looker, you know, is is a really big deal, and it really helps bring the power of all this data to the masses, so to speak.
Max:You touched on Bigtable a little bit earlier. Differences in selection between BigQuery and Bigtable. Why would end up in BigQuery, and why would somebody in Bigtable, or would they use both at the same time? And what's you know, how do you how do you make
Simon:Yeah. So I think both at the same time is totally something that that happens. And that that's I mean, not to digress too far, but that's one of the things that I like the most about the Google platform is that I think of it sort of like a set of Legos. And you can take lots of different components and build with each other and and you don't need to sort of make decisions about do I use this or that. You can use both.
Simon:Bigtable, for example, you can I'll speak in a moment about all the things that it's great at. But if you want to, you can take your Bigtable dataset and plug it in as an external table to BigQuery. And so, you know, for example, BigQuery is is not, I'm sorry, Bigtable is not a SQL compliant database. It's NoSQL. And so, we might have customers that say, Hey, you know, I like all that stuff that Bigtable can give, but I wanna run reports.
Simon:And I wanna use the power of BigQuery and I wanna use my SQL syntax to run those reports. So I kinda have to pick 1, don't I? And the answer is no. You can use Bigtable for all those cool things that it does, which are those really, really fast reads and writes, the NoSQL, the schema lists. You know, it makes it very, very fast.
Simon:But then I can also plug it into BigQuery and use all the things I like about BigQuery on top of the same dataset. And, you know, that I think that's the best of both worlds that a lot of our customers are after. But I do think of everything that Google has is sort of a spectrum. Yeah. You sort of have you can you can kind of, you know, pick how you want to administer your environment.
Simon:On the one end, you have tools like BigQuery or like App Engine that are extremely automated. You know, they they really define what a managed service is. Right? You don't do much of anything other than provide your data and then ask questions of your data. That's about it.
Simon:And then Bigtable is sort of a step on that spectrum towards less management in the sense that with Bigtable, I'm still responsible for managing the individual instances that that provide my Bigtable environment, which gives me more control, which oftentimes for our customers is exactly why they're selecting that technology. They want to control certain components of responsiveness and speed on their data. And so, you know, that that's the great example of where where you kinda get to pick the right tool for the specific requirements that each individual workload has. And, you know, in this case, you really get to do it without compromise.
Max:Google also has a tool called TensorFlow. What is TensorFlow, and how does that apply into an enterprise?
Simon:Yeah. So TensorFlow is really cool. Like I mentioned before, and I I will stand by. I I am I am the last thing, from a data scientist, and so I won't I won't pretend to be 1. But TensorFlow is effectively a platform.
Simon:It's a framework, really, that, you know, Google has put a lot of energy into, just like a lot of the other open source projects they're they're big contributors towards. And it's a framework for machine learning. And so that's sort of the logical next step. We talk about BigQuery. We talk about Bigtable.
Simon:There's a lot of ability to capture and analyze data, but then what do you do with that data? And so the TensorFlow framework really or the TensorFlow framework really helps with, helping organizations build models where they can harness not just the data that you're able to store and, and compute on within GCP, but also the massive computing power that GCP provides and use those things, in concert to build and train custom built models to answer unique questions for unique workloads. And, you know, it's worth mentioning, I think, to to take that abstract to make it a little bit more concrete, everyone's kinda familiar with Google's vision tools. Right? You know, if you go to google.com and you search for an image, they're pretty good at serving you an image.
Simon:And for those of you that have played around with it, google.com also has the ability for me to upload an image and search for similar images. And it's a really powerful TensorFlow based model that Google maintains that allows for that type of technology. You might see that if you use the consumer, Google Photos app on your phone also. Right? You see the same type of thing.
Simon:And so what TensorFlow basically does is it allows you to build a model like that for your your own use case.
Max:I think it's always fascinated to me with with machine learning. We talk about model development is how much data and computation goes in on the front end of the pipeline and how efficient it is on the backside of the pipeline. And there was a story a couple years ago, a fast food restaurant who is using machine learning and vision learning to detect how much chicken they had available in their hot baskets and make decisions on how much chicken should be cooked based on time of day and predicted load and traffic and all these sorts of things. And it was a machine learning based pipeline that they were training a model offline and then using, a little like raspberry pies or something in the actual franchise location itself to be making these decisions. And it's so that's it's an unusual, like, thought process, but it's a pretty specific example of how do you make a business more efficient from a resource allocation and capital and and time and efficiency and just, you know, all these things in a in a tool that, you know, 5 years ago wasn't available to the mass market.
Simon:Yep. Absolutely. And that's, again, just like with all the different tools we're talking about that Google has, there's a spectrum there. And so, you know, I'd say on the least managed, therefore, most complex side of things, you have the TensorFlow framework where, yes, from the ground up, you can build a model custom to your needs. Right?
Simon:So you want to count how much chicken is available, right? You probably need to build your own model that knows how to identify and count chicken and do all those cool things. But on the other extreme end of that spectrum are those pre built models. Like we just mentioned, there's the Vision API and the cool thing about that is that you're able to take advantage of all the things that Google's constantly adding as a result of their own work on, well, frankly, across all of Google. And so an example is, we work with a customer not all that long ago that wanted to build a custom model to identify how many people were in their business at any given time, count the individuals, but also get a feel for, customer satisfaction.
Simon:And so they were scratching their head. How do we even build a custom model that will identify those things? What it turns out is that, you know, the Google Vision API has a pre built capacity for identifying faces, and so they can easily count the number of people that that are there. And it also has a built in capacity for sentiment analysis. So it can look at a face and tell you, is this an angry person, a sad person, a happy person?
Simon:And so in the case of this client that we were working with, they found that it was much better to simply take advantage of what's available out of the box and it truly is out of the box. Right? They simply invoke an API and provide, you know, the imagery to the API and they get all this data back, as opposed to building something ground up. And I think those little things that, you know, in this case, saved thousands of development hours, you know, those things maybe aren't things that everybody knows about and doesn't they don't all know it exists. And so that that's what makes our job so fun is that we get to open people's eyes to a lot of this technology.
Max:You're the director of cloud adoption for SADA, who's a large Google partner. Right? I don't think a lot of people understand the relationship between Google and its partners and where SADA fits into all these things. So why would a company talk to a SADA versus talking to Google? Like, if I'm making this migration or I wanna use TensorFlow or I wanna figure out how to use BigQuery, you know, how does SADA fit into all that?
Max:And and, you know, how do you help?
Simon:Yeah. That's a that's a great question. That that's been, that that's been a that's been a key question since I started our GCP business over 7 years ago now, which is, I've heard of Google, but who is SADA Systems? Right? Yeah.
Simon:Most most folks don't even know how to pronounce our name. And, obviously, that's changed a lot over the years. Winning global awards from Google has really helped our brand recognition. But I think we play a fairly vital role in the the overall Google go to market as well as the adoption of of the platform in the sense that it is our job to help customers make use of all this really powerful technology. And, you know, the way we see it at least is that it's Google's job to make the best of breed technology, to make it, to support it, and to to ensure its its reliability and maintenance and operation.
Simon:And in in concert with that, we feel that it's our job to work with individual consumers or potential consumers of this technology to help them fit this tech into the problem spaces they need it to be in. And so I think that takes a lot of different forms for us. You know, we're able we we we provide support to our customers to help their applications that run on GCP keep running. You know, we provide consulting in the sense that, you know, kinda like what I just mentioned with that use case, the Vision API. Right?
Simon:You know, we help customers understand that, yes, Google makes lots of really cool tools. We help them figure out which one is applicable to their use case with the least amount of pain and, you know, the best end results. And then we also are responsible for the ongoing success of that customer. Right? We're we're there to make sure that, you know, it's fine if you migrate all your stuff to Google, and that's great.
Simon:We love to help customers do that. But we wanna make sure that our customers continue to have an ex a great experience, you know, with their use of the with the cloud. And and that goes beyond support. Right? I think, again, Google does support their products, and they're responsible for the actual product.
Simon:We wanna make sure that they continue to be used in the best way possible. So it's a really good, symbiotic relationship, I think.
Max:And it's safe to say that Google prefers SADA to be engaged with a customer deploying onto GCP at this point.
Simon:Yeah. I mean, I think that for the most part, the customers who partner in their adoption of the of the platform are the most successful both in the immediate, meaning they have the best migration, they have the best success at actually launching their project on the platform, but then they're also the most satisfied long term customers. And so for that reason, you know, Google loves to include partners like us because we're we're able to sort of share that responsibility for ensuring the outcome that the customer needs, long term.
Max:Hi. I'm Max Clark, and you're listening to the Tech Deep Dive podcast. At Clark Sys, we believe tech should make your life better, searching Google is a waste of time, and the right vendor is often one you haven't heard of before. With thousands of negotiated contracts, ClarkSys has helped hundreds of businesses source and implement the right tech at the right price. If you're looking for a new vendor and wanna have peace of mind knowing you've made the right decision, visit us at clarksys.com to schedule an intro call.
Max:Walk me through the the cloud journey, you know, for a customer. So I, you know, I I make a decision, either I'm evaluating a public cloud vendor at this point or I've I'm leaning towards Google. What would that engagement process with Asada Systems be, and and and how do you help that enterprise, you know, make that decision? This is where you should go, and these are the applications you should run, and this is how we make your your business do x, y, and z using these tools available.
Simon:Yeah. It takes it takes a lot of different forms. You know, I will say universally across our our customers, when you first engage with us, you know, we're not we're not the type of business that's going to, you know, nickel and dime our customers. And so as a result, we have these types of ideation sessions. We go through their workloads, we go through their requirements, and we understand their environment.
Simon:We do all that just upfront. Right? We're not we're not that's that's not a service we sell. We do that to better understand our customers, positioning in in what they're trying to accomplish. But then it varies a little bit because we do have customers that are already on a cloud platform.
Simon:Maybe it's Google, maybe it's something else, and they're trying to learn what they can do additionally. And for those customers that are already on GCP, oftentimes, we're having conversations with them about what more they can be doing to take advantage of what is already on the platform. We have customers that are not developing anything at the moment. Maybe they're a startup and they're looking for a new platform to start their journey on. And so as a result, we have different sort of approaches for each, you know, but but I think most common is the customer that's migrating to the platform.
Simon:In those cases, we have a number of tools at our disposal to assess their existing environment, both in terms of the operational efficiency as well as the actual costs of running from an OpEx perspective. In some cases, when we have customers that own their hardware, own their data center, we understand what those costs are. We also understand what the headaches are associated with that work. And then we're able to work with them to figure out what makes sense in terms of a move forward. Right?
Simon:Like, maybe maybe we go through that assessment. We say, look, you know, cloud maybe isn't for you. I must say that doesn't happen very often, but but it does happen. But more realistically, you know, we're able to then say, okay, these are maybe some target workloads that we think are good first movers to the cloud. Or if you're building net new, here are some things that we think are gonna be the best to prove the value of the cloud in terms of initial deployments.
Simon:And then, you know, in some cases, we'll work with our customer to actually perform that work with them by providing professional services. In other cases, we simply will be there as advisors to sort of hold our customers' hands through that process. And so, yeah, it takes it takes a lot of different forms depending on sort of what the customer needs and and where they are today.
Max:So another variation of that question might be, I'm already on another cloud platform, and I started using, you know, BigQuery and Bigtable. And I started a a process to go serverless, or I started down a containerization project. And, you know, this GCP thing has been over here on the corner, you know, with some of our our cloud spend. But, you know, now maybe it's a, should we look at this deeper because we're getting into more of the maybe it's Googley tools. Right?
Max:So if we're going containers and Google literally wrote Kubernetes, which is the dominant orchestration platform for containers, What is that conversation like and process for people, you know, evaluating a switch from something else into a GCP, you know, in a in a in a big way?
Simon:Yeah. So in that case, it's a little less, you automated. We don't have a tool that can say, hey, you should containerize your application. But we we we will work with that client and we'll understand both from a business perspective, what it is they want to accomplish, as well as from a technology perspective. An example of this would be, we we hear all the time, customers who exactly what you just said.
Simon:Right? They wanna come to Google because, you know, Google is sort of the granddaddy of containerization technology. They're hearing containers all over the place. They're hearing Kubernetes mentioned all over the place. They want in.
Simon:Right? And the first thing we do with those customers is we don't dive into their application code or understanding any of that stuff. We we wanna understand why from a business perspective they want to adopt that technology. What do they want to gain by doing that? And I think that's something that we take very seriously as sort of a core philosophy of how we approach customer problems because, you know, we could frankly probably make a lot of money by just saying, oh, you want to go containers?
Simon:Great. Let's let's spin you up a statement of work and start moving you to containers. Right? But it's not the best option for every customer. And even more to the point, sometimes what they want to accomplish can be accomplished in other ways that maybe are cheaper or easier.
Simon:And so that is always where we wanna start. We wanna know what it is the customer wants to accomplish. The other benefit of approaching it that way is that, like we've been talking about, there are a lot of cool technologies that Google has. And when a customer comes in and says, you know, I really want to become more efficient and I wanna get my developers to spend less time, deploying and testing, you know, maybe the answer isn't containerizing their application. Maybe the answer is retooling their DevOps pipeline.
Simon:And so that that's always where we're gonna start. We wanna understand what's at the core of what they're trying to accomplish.
Max:Also, kind of interestingly unique to Google. Right? Google Maps is an incredibly popular application on most people's cell phones or they're interacting with on the desktop, and this is something that's available to enterprises through GCP and something that SADA does a lot of work with, with with customers. What what are people using Google Maps for from an enterprise standpoint? How are you helping people with this?
Max:You know, what's what's kind of common that you that you're seeing today?
Simon:Yeah. So there's, there's a ton of functionality that comes out of the Google Maps ecosystem at Google. And we've had before we had a large GCP business, I think, actually, before we had any GCP business, we had a massive I believe, at one point, we were also the world's largest, reseller of Google Maps. And so there's been an awesome collaboration between the application side and the infrastructure side that happens on GCP and the mapping side of things. And the solutions that we're able to build for customers range really all over the place.
Simon:I mean, we have customers that are using, Google Maps for, you know, mapping front end for their application. Right? Think of food delivery or or these types of things that need to have a customer facing map involved in their application. They're able to use Google Maps for that. You know, we also have customers that are leveraging things.
Simon:I'm sure most everyone's familiar with Google Street View. Right? And we have many customers to take advantage of that because and not to go too far off course here, but just to sort of get everyone's wheel spinning a little bit. Think of what we just talked about that Vision API and its ability to identify lots of really interesting things in an image and then pair that with Google Street View. And you could sort of imagine all the cool things you could do.
Simon:For example, you could say, I need to provide directions to my place of business. I can say, okay, you're gonna make a left at that big oak tree because I can see that, and I'm by eye, I mean, the application can see that, with the technology. You know, we just this this this, this podcast is very well timed. We just today released our national response portal, which is Asada and Google collaboration, which is allowing for rich mapping data of, the the national response to COVID 19, where we can see well, actually, not just with the public, this is available to the public. The public can go in and see for every county, you know, cases, deaths, recoveries, where to find testing sites or places to get treatment or care.
Simon:And all of that's overlaid on a Google map, which is, of course, is location aware. So if I pull it up on my cell phone, it knows where I am, and that can give me lots of pertinent information. So, I still think, frankly, we're in the very early days of tapping into the power of what the collaboration within Google Cloud Platform and Google Maps can do. But we're already seeing customers, do some very, very cool stuff with those, 2 technologies.
Max:So Google has some pretty amazing speech technology, which includes, you know, some demonstrations that are awe inspiring and almost, you know, like, computers are here in Sentient, you know, to that level. How is that being rolled out right now? How are you guys involved with with development with enterprises? What kind of applications are you seeing driving this? I mean, there's the con the the the the one that comes up a lot is in the contact center space of people doing speech detection, language detection, and promptings.
Max:That way, the contact center can either interact with with the caller or maybe just prompt the agent for information that's being discussed so the agent doesn't have to search for it. But, you know, that's a pretty specific use case. I mean, what are you seeing enterprises taking advantage of this and how is that being leveraged?
Simon:Yeah. So there's been a lot of cool stuff in that space. I think it was a couple years ago at Google IO where they have the demonstration of sort of the the automated agent talking to a real human being and, you know, you really can't tell the difference. You can't tell you're talking to a computer. And so that was a cool demo, but what can we do with the technology in real life?
Simon:And, you know, we're just sort of getting to a point now where we have customers that are actually using this this toolset. The underlying technology is called Dialogflow. And it's a really powerful sort of automation AI tool that's offered or really it's a platform offered by Google that allows for building these types of things. It's been around for a little bit in the sense that you could have things like a chatbot on your website that would be able to interact with a consumer, but it's it's evolved a lot. And the umbrella now that it lives under is called CCAI or call center AI.
Simon:And it it's very similar to what you it's to what you just mentioned in the sense that it is the ability now to, at the simplest, augment and provide insights to a call center agent. And then at the most fully optimized, it can be the call center agent. Because not only do we have the speech to text where I can hear what you're saying and turn it into text, which can then be analyzed by, you know, an application. We also have the opposite where I can take text and I can speak it, with a computer. So when you start to put those things together and for all your listeners, I swear we didn't talk about this beforehand.
Simon:This is just good good, good timing, but, you know, we actually now have the ability to pair that technology with the mapping technology and the Street View technology. So I might say, hey. I can't find your business. I'm calling into your business. And by calling in, I'm actually connecting to a call center AI virtual agent who knows where I am, is able to see the street view data between where I am and where I need to be going, and can then read me visual cues for where I need to be going.
Simon:Again, turn left at that tree. And so, again, you know, this is very I think it's still very early in what we can do with these types of technologies, but we're already seeing things that I think even 5 years ago, I would have told you is pure science fiction happening in real life for real customers.
Max:You alluded to this a little bit at the beginning in terms of SADA's interaction and and integration with your customers. It's more than just advisory of, like, oh, you should use Google Cloud, and these are the things in Google Cloud you should use, like BigQuery versus Bigtable and why. I mean, how deep are you going into the actual technical implementation and projects with with with an enterprise?
Simon:Yeah. So I feel like my answer to a lot of this is the same, which is it kind of depends. And, you know, just like Google tools are offered on a spectrum of sophistication and control, We offer services in the same vein. And so, you know, at the least involved, we do. We provide consult just consulting and advising services where we are hands off, and we're we're simply guiding our client on where best to go.
Simon:This works really well for, you know, your digital natives or your startups, you know, where they they are very, very capable and they they grew up in this technology and they just need to be pointed in the right direction. But then on the other extreme end of that spectrum, we are fully building ground up environment for our customers. And in some cases, we're then managing that environment, that we've spun up. And, again, I say it's a spectrum for a reason because most of our customers fall somewhere in the middle of those two extremes, but we do offer services across the board. And so, you know, to bring examples to this, some of our customers, we will help them build an environment.
Simon:In some cases, we will build the environment. Sometimes we manage that environment, and other times, the customer then manages that environment after the fact. I will say in every case, unless the customer explicitly does not want us to, we always try to make sure to enable the client to be self sufficient because we don't we don't wanna be the gatekeepers. Right? We don't want our customers to be dependent on us.
Simon:We want them to work with us because we bring value, but not because they have no choice. And so even when we're fully managing our customer environments, we like to do sessions to make sure they understand how everything works, how to administer things themselves. And then still despite knowing all of that, make the choice to offload that work to a partner like us.
Max:SADA enjoys a relationship with Google that goes beyond just being a large partner and a large reseller. I mean, you have a common lineage in in executives and and management and technical teams. You know, for for the people listening, I mean, what what is that in you know, when we say that you guys are a big important partner to Google, what does that really mean?
Simon:Oh, I think, I mean, it means a few things. Yes. We do, you know, we do have a lot of folks on our team that, came from Google at one point in their careers. And, you know, I think we also we do a lot to to make sure our culture is a match, for Google as well. And so, you know, for example, I've been at SADA for almost 9 years, and I also feel very at home when I'm at a Google office, because they feel very similar to one another.
Simon:But I think, really, what it means to be, you know, sort of a a top tier partner with Google and to really have this type of relationship is that I think we're dependent on each other to a certain extent. You know, I think that the service, the implementation, the migration and adoption services that we provide, for our customers, I mean, we even have technical account managers that we assign to customers long term indefinitely to make sure they're successful. I think those things pair really well with the fact that we depend on Google to continue to innovate, to build the the best in breed technologies and tooling, and to make sure they always run, you know, as well as they possibly can. And, you know, I think we get to enjoy that Google will listen to us and we get to have conversations that are, you know, bidirectional about everything from go to markets through the detailed technical details of a product. And if I was one of our customers, I think the thing that I would say that I that is the best part of the SADA Google relationship is the fact that we are in a really unique place where and, you know, if you talk to Simon at JPL, you know, 9 years ago, I would never believe this to be a reality, but we have customers that are pushing the limits of of very sophisticated tools like BigQuery.
Simon:And we have the ability to take those customer needs or, additional features they're requesting and bring that to Google product management. And, you know, I don't mean to misrepresent that every time we make a suggestion to Google, it happens. It most certainly does not. But we do get to have a seat at the table and advocate for our customers' needs, wants, and and desires in a way that I don't think most other third parties get to do.
Max:As a as a closing last thought here, I mean, outside of hiring SADA, of course, for somebody that's evaluating and thinking about, you know, either migration to Google. So whether that's from on prem to Google or from another cloud to Google, what would your advice be in terms of, like, you know, pay attention to these things, think about these things, do these things for that to be a success?
Simon:Yeah. I mean, you know, shameless plug, we have Google Next, coming soon as the, the online iteration of the eGoogle Cloud Annual Conference. And it's it's where I point people when they wanna know more about the ecosystem because you have everything from very high level, you know, talks about the cloud strategy at Google all the way down to deeply technical details. And so it just so happens that's coming up in a few weeks and so that, you know, I think is probably a good place for a lot of folks to look. And if you're talking about Google specifically, I also point a lot of folks to the Google Cloud websites because there is a lot of really good content there both from a, you know, marketing perspective, right, to understand what the products are and what they're advertised as being able to do.
Simon:But then it's also extremely simple to just click go and start doing this stuff. And, you know, for those who are willing and have the time, I I can't think of a better way to learn what's out there than to roll your sleeves up and get in there and actually play around with the platform. And then I think, you know, the obvious answer is, give us a call. You know? Like I said, we're not here to nickel and dime customers.
Simon:You don't need to fear that if you call us up, you're gonna see an invoice at the end of it. That's not the kind of business we're running. You know, we we like to talk to our customers about these things and help educate them. I think that's a big part of our job, is doing that education. And so, yeah, shameless plug for us as well.
Max:Simon, thank you so much for your time. It's always a pleasure and very, very interesting always.
Simon:Yeah. Thank you so much for having me.
Max:Thanks for joining the Tech Deep Dive podcast. At Clarksons, we believe tech should make your life better. Searching Google is a waste of time, and the right vendor is often one you haven't heard of before. We can help you buy the right tech for your business. Visit us at clarksys.com to schedule an intro call.
