(upbeat music) >> Hello and welcome today's session for the AWS Startup Showcase, the next big thing in AI, Security and Life Sciences featuring Coralogix for the AI track. I'm your host, John Furrier with theCUBE. We're here we're joined by Ariel Assaraf, CEO of Coralogix. Ariel, great to see you calling in from remotely, videoing in from Tel Aviv. Thanks for coming on theCUBE. >> Thank you very much, John. Great to be here. >> So you guys are features a hot next thing, start next big thing startup. And one of the things that you guys do we've been covering for many years is, you're into the log analytics, from a data perspective, you guys decouple the analytics from the storage. This is a unique thing. Tell us about it. What's the story? >> Yeah. So what we've seen in the market is that probably because of the great job that a lot of the earlier generation products have done, more and more companies see the value in log data, what used to be like a couple rows, that you add, whenever you have something very important to say, became a standard to document all communication between different components, infrastructure, network, monitoring, and the application layer, of course. And what happens is that data grows extremely fast, all data grows fast, but log data grows even faster. What we always say is that for sure data grows faster than revenue. So as fast as a company grows, its data is going to outpace that. And so we found ourselves thinking, how can we help companies be able to still get the full coverage they want without cherry picking data or deciding exactly what they want to monitor and what they're taking risk with. But still give them the real time analysis that they need to make sure that they get the full insight suite for the entire data, wherever it comes from. And that's why we decided to decouple the analytics layer from storage. So instead of ingesting the data, then indexing and storing it, and then analyzing the stored data, we analyze everything, and then we only store it matters. So we go from the insights backwards. That allowed us to reduce the amount of data, reduce the digital exhaust that it creates, and also provide better insights. So the idea is that as this world of data scales, the need for real time streaming analytics is going to increase. >> So what's interesting is we've seen this decoupling with storage and compute be a great success formula and cloud scale, for instance, that's a known best practice. You're taking a little bit different. I love how you're coming backwards from it, you're working backwards from the insights, almost doing some intelligence on the front end of the data, probably sees a lot of storage costs. But I want to get specifically back to this real time. How do you do that? And how did you come up with this? What's the vision? How did you guys come up with the idea? What was the magic light bulb that went off for Coralogix? >> Yes, the Coralogix story is very interesting. Actually, it was no light bulb, it was a road of pain for years and years, we started by just you know, doing the same, maybe faster, a couple more features. And it didn't work out too well. The first few years, the company were not very successful. And we've grown tremendously in the past three years, almost 100X, since we've launched this, and it came from a pain. So once we started scaling, we saw that the side effects of accessing the storage for analytics, the latency it creates, the the dependency on schema, the price that it poses on our customers became unbearable. And then we started thinking, so okay, how do we get the same level of insights, because there's this perception in the world of storage. And now it started to happen in analytics, also, that talks about tiers. So you want to get a great experience, you pay a lot, you want to get a less than great experience, you pay less, it's a lower tier. And we decided that we're looking for a way to give the same level of real time analytics and the same level of insights. Only without the issue of dependencies, decoupling all the storage schema issues and latency. And we built our real time pipeline, we call it Streama. Streama is a Coralogix real time analysis platform that analyzes everything in real time, also the stateful thing. So stateless analytics in real time is something that's been done in the past and it always worked well. The issue is, how do you give a stateful insight on data that you analyze in real time without storing and I'll explain how can you tell that a certain issue happened that did not happen in the past three months if you did not store the past three months? Or how can you tell that behavior is abnormal if you did not store what's normal, you did not store to state. So we created what we call the state store that holds the state of the system, the state of data, were a snapshot on that state for the entire history. And then instead of our state being the storage, so you know, you asked me, how is this compared to last week? Instead of me going to the storage and compare last week, I go to the state store, and you know, like a record bag, I just scroll fast, I find out one piece of state. And I say, okay, this is how it looked like last week, compared to this week, it changed in ABC. And once we started doing that we on boarded more and more services to that model. And our customers came in and say, hey, you're doing everything in real time. We don't need more than that. Yeah, like a very small portion of data, we actually need to store and frequently search, how about you guys fit into our use cases, and not just sell on quota? And we decided to basically allow our customers to choose what is the use case that they have, and route the data through different use cases. And then each log records, each log record stops at the relevant stops in our data pipeline based on the use case. So just like you wouldn't walk into the supermarket, you fill in a bag, you go out, they weigh it and they say, you know, it's two kilograms, you pay this amount, because different products have different costs and different meaning to you. That same way, exactly, We analyze the data in real time. So we know the importance of data, and we allow you to route it based on your use case and pay a different amount per use case. >> So this is really interesting. So essentially, you guys, essentially capture insights and store those, you call them states, and then not have to go through the data. So it's like you're eliminating the old problem of, you know, going back to the index and recovering the data to get the insights, did we have that? So anyway, it's a round trip query, if you will, you guys are start saving all that data mining cost and time. >> We call it node zero side effects, that round trip that you that you described is exactly it, no side effects to an analysis that is done in real time. I don't need to get the latency from the storage, a bit of latency from the database that holds the model, a bit of latency from the cache, everything stays in memory, everything stays in stream. >> And so basically, it's like the definition of insanity, doing the same thing over and over again and expecting a different result. Here, that's kind of what that is, the old model of insight is go query the database and get something back, you're actually doing the real time filtering on the front end, capturing the insights, if you will, storing those and replicating that as use case. Is that right? >> Exactly. But then, you know, there's still the issue of customer saying, yeah, but I need that data. Someday, I need to really frequently search, I don't know, you know, the unknown unknowns, or some of the day I need for compliance, and I need an immutable record that stays in my compliance bucket forever. So we allowed customers, we have this some that screen, we call the TCO optimizer, that allows them to define those use cases. And they can always access the data by creating their remote storage from Coralogix, or carrying the hot data that is stored with Coralogix. So it's all about use cases. And it's all about how you consume the data because it doesn't make sense for me to pay the same amount or give the same amount of attention to a record that is completely useless. It's just there for the record or for a compliance audit, that may or may not happen in the future. And, you know, do the same with the most critical exception in my application log that has immediate business impact. >> What's really good too, is you can actually set some policy up if you want a certain use cases, okay, store that data. So it's not to say you don't want to store it, but you might want to store it on certain use cases. So I can see that. So I got to ask the question. So how does this differ from the competition? How do you guys compete? Take us through a use case of a customer? How do you guys go to the customer and you just say, hey, we got so much scar tissue from this, we learned the hard way, take it from us? How does it go? Take us through an example. >> So an interesting example of actually a company that is not the your typical early adopter, let's call it this way. A very advanced in technology and smart company, but a huge one, one of the largest telecommunications company in India. And they were actually cherry picking about 100 gigs of data per day, and sending it to one of the legacy providers which has a great solution that does give value. But they weren't even thinking about sending their entire data set because of cost because of scale, because of, you know, just a clutter. Whenever you search, you have to sift through millions of records that many of them are not that important. And we help them actually ask analyze their data and work with them to understand these guys had over a terabyte of data that had incredible insights, it was like a goldmine of insights. But now you just needed to prioritize it by their use case, and they went from 100 gig with the other legacy solution to a terabyte, at almost the same cost, with more advanced insights within one week, which isn't in that scale of an organization is something that is is out of the ordinary, took them four months to implement the other product. But now, when you go from the insights backwards, you understand your data before you have to store it, you understand the data before you have to analyze it, or before you have to manually sift through it. So if you ask about the difference, it's all about the architecture. We analyze and only then index instead of indexing and then analyzing. It sounds simple. But of course, when you look at this stateful analytics, it's a lot more, a lot more complex. >> Take me through your growth story, because first of all, I'll get back to the secret sauce in the same way. I want to get back to how you guys got here. (indistinct) you had this problem? You kind of broke through, you hit the magic formula, talking about the growth? Where's the growth coming from? And what's the real impact? What's the situation relative to the company's growth? >> Yeah, so we had a first rough three years that I kind of mentioned, and then I was not the CEO at the beginning, I'm one of the co founders. I'm more of the technical guy, was the product manager. And I became CEO after the company was kind of on the verge of closing at the end of 2017. And the CTO left the CEO left, the VP of R&D became the CTO, I became the CEO, we were five people with $200,000 in the bank that you know, you know that that's not a long runway. And we kind of changed attitudes. So we kind of, so we first we launched this product, and then we understood that we need to go bottoms up, you can go to enterprises and try to sell something that is out of the ordinary, or that changes how they're used to working or just, you know, sell something, (indistinct) five people will do under $1,000 in the bank. So we started going from bottoms up, and the earlier adopters. And it's still until today, you know, the the more advanced companies, the more advanced teams. This is our Gartner friend Coralogix, the preferred solution for Advanced, DevOps and Platform Teams. So they started adopting Coralogix, and then it grew to the larger organization, and they were actually pushing, there are champions within their organizations. And ever since. So until the beginning of 2018, we raised about $2 million and had sales or marginal. Today, we have over 1500, pink accounts, and we raised almost $100 million more. >> Wow, what a great pivot. That was great example of kind of getting the right wave here, cloud wave. You said in terms of customers, you had the DevOps kind of (indistinct) initially. And now you said expanded out to a lot more traditional enterprise, you can take me through the customer profile. >> Yeah, so I'd say it's still the core would be cloud native and (indistinct) companies. These are typical ones, we have very tight integration with AWS, all the services, all the integrations required, we know how to read and write back to the different services and analysis platforms in AWS. Also for Asia and GCP, but mostly AWS. And then we do have quite a few big enterprise accounts, actually, five of the largest 50 companies in the world use Coralogix today. And it grew from those DevOps and platform evangelists into the level of IT, execs and even (indistinct). So today, we have our security product that already sells to some of the biggest companies in the world, it's a different profile. And the idea for us is that, you know, once you solve that issue of too much data, too expensive, not proactive enough, too couple with the storage, you can actually expand that from observability logging metrics, now into tracing and then into security and maybe even to other fields, where the cost and the productivity are an issue for many companies. >> So let me ask you this question, then Ariel, if you don't mind. So if a customer has a need for Coralogix, is it because the data fall? Or they just got data kind of sprawled all over the place? Or is it that storage costs are going up on S3 or what's some of the signaling that you would see, that would be like, telling you, okay, okay, what's the opportunity to come in and either clean house or fix the mess or whatnot, Take us through what you see. What do you see is the trend? >> Yeah. So like the tip customer (indistinct) Coralogix will be someone using one of the legacy solution and growing very fast. That's the easiest way for us to know. >> What grows fast? The storage, the storage is growing fast? >> The company is growing fast. >> Okay. And you remember, the data grows faster than revenue. And we know that. So if I see a company that grew from, you know, 50 people to 500, in three years, specifically, if it's cloud native or internet company, I know that their data grew not 10X, but 100X. So I know that that company that might started with a legacy solution at like, you know, $1,000 a month, and they're happy with it. And you know, for $1,000 a month, if you don't have a lot of data, those legacy solutions, you know, they'll do the trick. But now I know that they're going to get asked to pay 50, 60, $70,000 a month. And this is exactly where we kick in. Because now, when it doesn't fit the economic model, when it doesn't fit the unit economics, and he started damaging the margins of those companies. Because remember, those internet and cloud companies, it's not costs are not the classic costs that you'll see in an enterprise, they're actually damaging your unit economics and the valuation of the business, the bigger deal. So now, when I see that type of organization, we come in and say, hey, better coverage, more advanced analytics, easier integration within your organization, we support all the common open source syntaxes, and dashboards, you can plug it into your entire environment, and the costs are going to be a quarter of whatever you're paying today. So once they see that they see, you know, the Dev friendliness of the product, the ease of scale, the stability of the product, it makes a lot more sense for them to engage in a PLC, because at the end of the day, if you don't prove value, you know, you can come with 90% discount, it doesn't do anything, not to prove the value to them. So it's a great door opener. But from then on, you know, it's a PLC like any other. >> Cloud is all about the PLC or pilot, as they say. So take me through the product, today, and what's next for the product, take us through the vision of the product and the product strategy. >> Yeah, so today, the product allows you to send any log data, metric data or security information, analyze it a million ways, we have one of the most extensive alerting mechanism to market, automatic anomaly detection, data flustering. And all the real law, you know, the real time pipeline, things that help companies make their data smarter, and more readable, parsing, enriching, getting external sources to enrich the data, and so on, so forth. Where we're stepping in now is actually to make the final step of decoupling the analytics from storage, what we call the datalist data platform in which no data will sit or reside within the Coralogix cloud, everything will be analyzed in real time, stored in a storage of choice of our customers, then we'll allow our customers to remotely query that incredible performance. So that'll bring our customers away, to have the first ever true SaaS experience for observability. Think about no quota plans, no retention, you send whatever you want, you pay only for what you send, you retain it, how long you want to retain it, and you get all the real time insights much, much faster than any other product that keeps it on a hot storage. So that'll be our next step to really make sure that, you know, we're kind of not reselling cloud storage, because a lot of the times when you are dependent on storage, and you know, we're a cloud company, like I mentioned, you got to keep your unit economics. So what do you do? You sell storage to the customer, you add your markup, and then you you charge for it. And this is exactly where we don't want to be. We want to sell the intelligence and the insights and the real time analysis that we know how to do and let the customers enjoy the, you know, the wealth of opportunities and choices their cloud providers offer for storage. >> That's great vision in a way, the hyper scalars early days showed that decoupling compute from storage, which I mentioned earlier, was a huge category creation. Here, you're doing it for data. We call hyper data scale, or like, maybe there's got to be a name for this. What do you see, about five years from now? Take us through the trajectory of the next five years, because certainly observability is not going away. I mean, it's data management, monitoring, real time, asynchronous, synchronous, linear, all the stuffs happening, what's the what's the five year vision? >> Now add security and observability, which is something we started preaching for, because no one can say I have observability to my environment when people you know, come in and out and steal data. That's no observability. But the thing is that because data grows exponentially, because it grows faster than revenue what we believe is that in five years, there's not going to be a choice, everyone are going to have to analyze the data in real time. Extract the insights and then decide whether to store it on a you know long term archive or not, or not store it at all. You still want to get the full coverage and insights. But you know, when you think about observability, unlike many other things, the more data you have many times, the less observability you get. So you think of log data unlike statistics, if my system was only in recording everything was only generating 10 records a day, I have full, incredible observability I know everything that I've done. what happens is that you pay more, you get less observability, and more uncertainty. So I think that you know, with time, we'll start seeing more and more real time streaming analytics, and a lot less storage based and index based solutions. >> You know, Ariel, I've always been saying to Dave Vellante on theCUBE, many times that there needs to be insights as to be the norm, not the exception, where, and then ultimately, it would be a database of insights. I mean, at the end of the day, the insights become more plentiful. You have the ability to actually store those insights, and refresh them and challenge them and model update them, verify them, either sunset them or add to them or you know, saying that's like, when you start getting more data into your organization, AI and machine learning prove that pattern recognition works. So why not grab those insights? >> And use them as your baseline to know what's important, and not have to start by putting everything in a bucket. >> So we're going to have new categories like insight, first, software (indistinct) >> Go from insights backwards, that'll be my tagline, if I have to, but I'm a terrible marketing (indistinct). >> Yeah, well, I mean, everyone's like cloud, first data, data is data driven, insight driven, what you're basically doing is you're moving into the world of insights driven analytics, really, as a way to kind of bring that forward. So congratulations. Great story. I love the pivot love how you guys entrepreneurially put it all together and had the problem your own problem and brought it out and to the to the rest of the world. And certainly DevOps in the cloud scale wave is just getting bigger and bigger and taking over the enterprise. So great stuff. Real quick while you're here. Give a quick plug for the company. What you guys are up to, stats, vitals, hiring, what's new, give the commercial. >> Yeah, so like mentioned over 1500 being customers growing incredibly in the past 24 months, hiring, almost doubling the company in the next few months. offices in Israel, East Center, West US, and UK and Mumbai. Looking for talented engineers to join the journey and build the next generation of data lists data platforms. >> Ariel Assaraf, CEO of Coralogix. Great to have you on theCUBE and thank you for participating in the AI track for our next big thing in the Startup Showcase. Thanks for coming on. >> Thank you very much John, really enjoyed it. >> Okay, I'm John Furrier with theCUBE. Thank you for watching the AWS Startup Showcase presented by theCUBE. (calm music)

(upbeat music) >> Well, hello everyone, John Walls here on theCUBE as we continue our CUBE conversations as part of the AWS Startup Showcase with Ariel Assaraf who is the CEO and co-founder of Coralogix based in Tel Aviv. And Ariel, thanks for joining us, especially under these trying circumstances I'm sure many people watching fully appreciate what's going on in Israel right now with the bombings that are happening on a perpetual basis. And I just hope you and family, friends and your coworkers are doing well and staying as safe as possible. >> Thank you very much, John. Yeah, this is a surreal period of time where we're in the office then occasionally going to the shelter for a couple minutes and then getting back to coding and planning. So yeah, thank you. >> Well, certainly take care and you're very much on our thoughts and in our hearts right now and we wish you all the well and safety. Let's talk about Coralogix though. This is obviously it's your baby and entering the wild world of data these days this exponential growth of data. You and I were talking about really the untapped potential of data a little bit early before the interview. So let's talk about maybe the genesis of Coralogix a little bit and why you came up with this concept and then the unique platform that you've now established to really help your clients make some sense of these vast reams of data that they have at their disposal. >> Yeah, I think that's a very interesting topic that a lot of companies are starting to now address each one with its own angle. We decided to go with the real-time streaming analytics approach. The problem starts with data growing exponentially like you mentioned, but it's not just growing exponentially it's growing faster than revenue. What happens is that, companies that are bound to the cost of data are getting to a point where their margins and our unit economics are being slaughtered by the amount of data that they need to analyze whether it's for BI or marketing, and certainly observability which is probably the largest data producer inside any organization. And what typically companies tend to do is to start cherry pick data. So they only collect relevant information or only collect areas or only collect specific servers or specific environments. And that causes that statistic you just mentioned from the MIT research, showing that 99.5 of the data remains untapped or unanalyzed. When we looked at it, we thought that, you want to monitor that data at a high level. You want to analyze it automatically or manually or visualize it with good performance. And so the approach that existed/exists in the market until today is to use storage tiers. But then you have to compromise the quality and the speed of analytics. And we chose instead of that, to unlike everyone that index and then analyze to ingest, analyze everything in real time, including the most stateful transformation and stateful analytics and only then store what matters that way giving broader coverage and allowing companies economically and also in terms of scale, to send everything get the full analytics layer that they need and basically improve both their businesses and their performance. >> Yeah, it sounds so sensible. It sounds so simple too. Right, we're just going to analyze data as it comes in real time, we'll make sense of it, we'll process that, we'll make it actionable and boom off we go. But obviously, as you know this is an extraordinarily complex series of operations occurring now especially in the microservices world, right? Because you have all these inputs and all these instances happening simultaneously in different environments. So untangle that for me a little bit in terms of microservices now, the complexity that that creates and your approach to that. >> Yeah. So two things that happen. One, there are more services in each company. Two, there are more versions uploaded to each service every day. So the world of CICB combined with the world of microservices creates a lot of uncertainty. On one hand that's great because you have less decoupling. You can go faster, you can be faster to market respond to the market faster. You can analyze data in specific units that allows you more flexibility and you can release a lot more. On the other hand, it gets much harder to triage, to figure out what specific microservice is causing a problem to monitor the communication between different microservices. And certainly to understand, what is the version that broke something? A lot of software problems come after upgrades or configuration changes. And these two factors together, they generate a lot of data that you need to start monitoring and analyze. Now, like you're saying, analyzing in real time that's been done in the past. That doesn't sound too complex but what happens is that the answer from real-time streaming was only applied to stateless things. Meaning, let me know when you see something. You see an event, send me an alert. You see a metric, send me an alert. What happens is that, it's still missing the longer term analytics. So it's some sort of an oxymoron to say, on one hand I'm doing real-time streaming on the other hand, I want to give you analytics that rely on a long-term state. Let me know if something happened more than it did last week. Let me know if something happened for the first time this month. Clustered a data based on a learning algorithm that learns the data continuously throughout the entire history of time. And this is Streama, the technology that we created that does the real-time streaming but also involves, components that store the state of the system at any given point in time. So while other solutions or other approaches use the storage as the state. So if I want to know what happened a week ago, I just go to the storage and see what's in there from last week. Now we hold the snapshot of state of everything relevant whether we automatically discovered or the customer defined it and make sure that our customers can go back in time and compare versions or compare matrix or compare graphs and see how specific versions affected specific microservices and how specific microservices affected their entire production systems. >> So where, or help me out here just in terms of cost efficiency, then now, if you kind of, you're not eliminating storage obviously, but you're kind of shifting responsibilities here or shifting process a little bit, right? And making it a little more accessible on a real-time basis. What kind of cost efficiencies do you get out of that, in terms of not having to go to storage for everything and dig everything out from a week or two weeks or a month ago? >> Yeah, that's a great question. So it affects multiple areas there. First of all, storage is one of the areas where you can least optimize because it is what it is besides compression that's been invented years ago and we're pretty much maxed out there. There's not a lot of waste to really save on storage. So what companies do they try to put it on lower tier storage, but then you lose performance. What we do is we bound ourselves only to CPU and CPU, when you do analytics, you can improve and optimize to the max and get to a point where you auto-scale analyze all data in real time, get better results and you can continuously improve your code and your microservices in a way that makes them more efficient. We're talking about roughly 70, 75% of savings when we compare that to the closest solution in our space. But it's more than that actually. We believe that at the end of the day the storage approach is not going to be a feasible because storage doesn't scale great, like any, you know, any CPU that you increase, you get better performance, you get faster performance, you get more power. But when you increase storage size when you store more data for a longer term you actually lose performance. It's actually slower, it's more cluttered. And so what happens is that companies that need long-term analytics one, they have to use the storage. They can do it in real time, but two, they also have to have that storage stored for a very long period of time. So it exponentially grows. And we believe that we'll come to an era because data grows exponentially and many of our users are engineers that understand exponential growth. It's going to get to a point where it's almost impossible to write all the data to the disk and then companies are going to need to compromise again. So we feel that the market is going to a place where you'd like to get the analytics taken out of the data and only relevant information for the analytics being stored because the matrix and the logs and the traces they are a means to an end. They're not the purpose for which we are actually generating and storing them. >> Right. And that's what your clients are all about too. Right? Get me the need, you know, get me the gold, the data, you know, that I actually need and help me separate the wheat from the chaff here. What about AWS? How did they come into play? Or what about your relationship with them and how has that developed and currently where does that sit? >> Yes. So we actually moved to AWS about two years ago and moved our entire production and built it on the AWS infrastructure. Our infrastructure is entirely on Kubernetes. We're using Terraform and we have our own CI/CD tool that we actually released as an open source. And we scaled on AWS massively and started seeing the opportunities with most of our customers being on AWS. So we partnered with AWS partnerships teams. We went through the competencies the well-architected, the accelerate program. And now the relationship is at a level where our sales teams are working closely together with the AWS account managers to spot opportunities where AWS customers need an additional layer of analytics or better cloud security or cost reduction. And we're working together to find them that solution. Now to make it easier and more seamless for AWS customers to use us, we are onboarded to the AWS marketplace. So we're under the unified agreement of AWS and we can be paid through the AWS bill. So now Coralogix can be seen as an AWS service that you're using you don't have to use another vendor and you can get additional insights and lowered costs and 24/7 support that we provide. So that's how we partner with AWS. And of course, a lot of joint marketing and content activity. So we're running a webinar together with AWS teams at a general, not about us. In general, how can we give back to the community? How do you scale? For instance, we ran a webinar on how do you scale Kafka? Which is certainly not our domain, but definitely an issue that we had to handle and had to scale and it's a pain point for many AWS customers. So we're trying to give back, we're a lot from AWS and we are partnering with them to solve problems together. >> So what's it done for you then at Coralogix? So you said it's been a two year relationship so it's matured obviously and you've worked out something very nice. You're leveraging each other's strengths, you know, in a very smart and tactical way. So what does it mean to you though Coralogix and ultimately, what do you think it means to your end user, your client base when you bring the kind of this combined power into their needs? >> Yeah. So for us working together with AWS means that they help us where a startup is lacking the most strength. So startups, they can be extremely fast they can develop cutting edge technologies they can bring new approaches and products to the market. But when you start working with the larger organizations the most hardest part of a POC because the engineering teams see the value immediately is the procurement is the legal parts is getting there opening the door and showing them the value proposition that you have and working together with AWS allows us to first of all, meet these customers, understand their needs and then being able to route through the AWS marketplace. And of course, to make it easier for them. We created like 20 different plugins to all AWS services so they can seamlessly connect all their data. Cause you remember one of the things that we wanted to get to is people not having to cherry pick logs. We're not having to cherry pick matrix. So now they can connect their entire environment and get full cloud observability and security within minutes and do it in an economic way. >> Wait, you're talking about all these capabilities and providing the client base and obviously this is a field that we're talking about data and what you're doing with it that's growing so rapidly. What does it mean to you like inside your office there in terms of, do you have enough space for people? I assume your growth trajectory is pretty impressive right now. >> Yes. This is, it's something that we are trying to learn now. This is a third office in three years and we're now outgrowing this one and going to the next one. So we grew from about 10 people, two years ago when we moved to AWS to over a hundred people now and continuing to hire in East, center, West US and in Israel and in London and in India. And the company is going to double itself within the next few months. So it's definitely, you know, a challenge now with COVID era also, but thank God, you know here in Israel, we're kind of past that. And it seems like the US is going to be past that in the next few months. So we're going to get back and start hiring and growing the teams. >> Well, it sounds impressive. And congratulations on that particular aspect of your business. I know it's always fun to bring on new people. It's all a very positive sign. So congratulations on that front. Thank you for the time today. And most importantly, again, we do wish you a great health and wellness and safety given that all that's going on right now and our hopes and prayers are that it ends as quickly as possible and you can return back to business as usual there. >> Thank you very much, John. I appreciate your time. >> Thank you sir. >> You bet. My pleasure. Once again, we're talking about Coralogix here, on theCUBE Conversation as part of the AWS Startup Showcase with Ariel Assaraf, who is the CEO and co-founder. I'm John Walls. Thanks for joining us here on theCUBE. (upbeat music)