(upbeat music) >> Hello friends and welcome back to theCUBE's Live coverage of AWS re:Invent 2022 from the Venetian Expo in Vegas, baby. This show is absolutely packed. Lisa Martin with Dave Vellante, Dave this is day two, but really full day one of our wall to wall coverage on theCUBE. We've had great conversations the last half day this morning already, we've been talking with a lot of companies, a lot of Amazonians and some Amazonians that have left and gone on to interesting more things, which is what we're going to talk about next. >> Well, I'm excited about this segment because it's a really interesting space. You've got a search company who's gotten into observability and security and through our ETR partner our research, we do quarterly research and Elastic off the charts. Obviously they're the public company, so you can see how well they're doing. But the spending momentum on this platform is very, very strong and it has been consistently for quite some time. So really excited to learn more. >> The voice of the customer speaking loudly, from Elastic, its Chief Product Officer joins us, Ken Exner. Ken, welcome to the program. Hi, thank you, good to be here. >> Dave Vellante: Hey Ken. >> So a lot of us know about Elastic from Elastic Search but it's so much more than that these days. Talk about Elastic, what's going on now? What's the current product strategy? What's your vision? >> Yeah. So people know Elastic from the ELK Stack, you know Elastic Search, Logstash, Kibana. Very, very popular open source projects. They've been used by millions of developers for years and years. But one of the things that we started noticing over the years is that people were using it for all kinds of different use cases beyond just traditional search. So people started using Elastic Search to search through operational data, search through logs, search through all kinds of other types of data just to find different answers. And what we started realizing is the customers were taking us into different spaces. They took us into log analytics they started building log management solutions. And we said, cool, we can actually help these customers by providing solutions that already do this for them. So it took us into observability, they took us into security, and we started building solutions for security and observability based on what customers were starting to do with the platform. So customers can still use the platform for any number of different use cases for how do you get answers added data or they can use our pre-built packaged solutions for observability and security. >> So you were a longtime Amazonian. >> I was. I was. >> Talk a little bit about some of the things that you did there and what attracted you to Elastic? 'Cause it's only been a couple months, right? >> I've been here three months, I think three months as of yesterday. And I was at AWS for 16 years. So I was there a long, long time. I was there pretty much from the beginning. I was hired as one of the first product managers in AWS. Adam Selipsky hired me. And it was a great run. I had a ton of fun, I learned a lot. But you know, after 16 years I was kind of itching to do something new and it was going to take something special because I had a great gig and enjoyed the team at AWS. But I saw in Elastic sort of a great foundational technology they had a lot of momentum, a huge community behind it. I saw the business opportunity where they were going. I saw, you know the business opportunity of observability and security. These are massive industries with tons of business problems. Customers are excited about trying to get more answers out of data about their operational environment. And I saw, you know, that customers were struggling with their operating environments and things were becoming increasingly complicated. We used to talk in AWS about, you know how customers want to move from monolithic applications to monoliths, but one of the secrets was that things were increasingly complicated. Suddenly people had all these different microservices they had all these different managed services and their operating environment got complicated became this constellation of different systems, all emitting data. So companies like Elastic were helping people find answers in that data, find the problems with their systems so helping tame that complexity. So I saw that opportunity and I said I want to jump on that. Great foundational technology, good community and building solutions that actually helped solve real problems. >> Right. >> So, before you joined you probably looked back, and said, let think about the market, what's happening in the market space. What were the big trends that you saw that sort of informed your decision? >> Well, just sort of the mountain of data that was sort of emerging. Adam Selipsky in his talk this morning began by talking about how data is just multiplying constant. And I saw this, I saw how much data businesses were drowning in. Operational data, security data. You know, if you're trying to secure your business you have all these different endpoints you have all these different devices, you have different systems that you need to monitor all tons of data. And companies like Elastic were helping companies sort of manage that complexity, helping them find answers in that. So, when you're trying to track intruders or trying to track you know, malicious activity, there's a ton of different systems you need to pay attention to. And you know, there's a bunch of data. It's different devices, laptops and phone devices and stuff that you need to pay attention to. And you find correlations across that to figure out what is going on in your network, what is going on in your business. And that was exciting to me. This is a company sort of tackling one of the hardest problems which is helping you understand your operating environment, helping you understand and secure your business. >> So everybody's getting into observability. >> Yep. >> Right, it's a very crowded space right now. First of all, you know it's like overnight it just became the hottest thing going. VCs were throwing money at it. Why was that and how were you guys different? >> Well, we began by focusing on log analytics because that was the core of what we were doing. But customers started using it beyond log analytics and started using it for APM and started using it for performance data. And what we realized is that we could do all this for customers. So we ended up, sort of overnight over the course of three years building that a complete observe observability suite. So you can do APM, you can do profiling, you can do tracing, sort of distributed tracing, you can do synthetic monitoring everything you want to do, wheel user wondering. >> Metrics? >> All of it, metrics, all of it. And you can use the same system for this. So this was sort of a powerful concept, not only is it the best in leading log system, it also provides everything you need for complete observability. And because it's based on this open platform you can extend it to a number of different scenarios. So this is important, a lot of the different observability companies provide you something that's sort of packaged and as long as you're trying to do what it wants to support, it's great. But with Elastic, you have this flexible data architecture that you can use for anything. So companies use it to monitor assembly lines, they use it to monitor dish networks, for example use it to not only manage their fleet of servers they also use it to manage all their devices. So 25 million desktop devices. So, you know, observability systems like that that can do a number of different scenarios, I think that's a powerful thing. It's not just about how do you manage your servers how do you manage the things that are simple. It's how do you manage anything? How do you get observability into anything. >> Multiple use cases. >> Sorry, when you say complete, okay you talked about all the different APM, log analytics tracing, metrics, and also end-to-end. >> Ken Exner: End-to-end, yeah. >> Could you talk about that component of complete? >> So, if you're trying to find an issue like you have some metric that goes into alarm. You want to have a metric system that has alarming. Once that metric goes in alarm you're going to want to dig into your log. So you're going to want it to take you to the area of your logs that has that issue. Once you gets to there, you're going to want to find the trace ID that takes you to your traces and looks at sort of profiling, distributed tracing information. So a system that can do all of that end-to-end is a powerful solution. So it not only helps you track things end-to-end across the different signals that you're monitoring, but it actually helps you remediate more quickly. And the other thing that Elastic does that is unique is a lot of ML in this. So not only helping you find the information but surfacing things before you even know of them. So anomaly detection for example, helps you know about something before you even realize that there was an issue. So you should pay attention to this because it's anomalous. So a lot of systems help you find something if you know what to look for. But we're trying to help you not only find the things that you know to look for, but help you find the things that you didn't even think to know about. >> And it's fair to say one of your differentiators is you're open, open source. I mean, maybe talk about the ELK stack a little bit and how that plays. >> Yeah, well, so the great thing about this is we've extended that openness to both security and to observability. An example of this on the security side is all the detection rules that you use for looking for intrusion all the detection rules are open source and there's an entire community around this. So if you wanted to create a detection rule you can publish an open source, there's a bunch in GitHub you can benefit from what the community is doing as well. So in the world of security you want to be supported by the entire community, everyone looking for the same kind of issues. And there's an entire community around Elastic that is helping support these detection rules. So that approach, you know wanting to focus on community is differentiating for us. Not just, we got you covered as long you use things from us you can use it from the entire community. >> Well there implies the name Elastic. >> Yeah >> Talk a little bit about the influence that the customer has in the product roadmap and the direction. You've talked a little bit in the beginning about customers were leading us in different directions. It sounds very Amazonian in terms of following the customers where they go. >> It does, it actually does, it was one of the things that resonated for me personally is the journey that Elastic took to observability and security was customer led. So, we started looking at what customers were doing and realized that they were taking us into log analytics they were taking us into APM, they were taking us into these different solutions, and yeah, it is an Amazonian thing, so it resonated for me personally. And they're going to continue taking us in new places. Like we love seeing all the novel things that customers do with the platform and it's sort of one of the hallmarks of a great platform is you can have all kinds of novel things that, novel use cases for how people use your platform and we'll continue to see things and we may get taken into other solutions as well as we start seeing things emerge, like common patterns. But for now we're really excited about security and observability. >> So what do you see, so security's a big space, right? >> Yep. >> You see the optiv taxonomy and it makes your eyes bleed 'cause there's so many tools in there. Where do you fit in that taxonomy? How do you see and think about the security space and the opportunity for your customers? >> Yeah, so we began with logs in the security space as well. So SIEM, which is intrusion detection is based on aggregating a bunch of logs and helping you do threat hunting on those logs. So looking for patterns of malicious behavior or intrusion. So we started there and we did both detections as well as just ad hoc threat hunting. But then we started expanding into endpoint protection. So if we were going to have agents on all these different devices they were gathering logs, what if we also started providing remediation. So if you had malicious activity that was happening on one of the servers, don't just grab the information quarantine it, isolate it. So that took us into sort of endpoint protection or XDR. And then beyond that, we recently got into cloud security as well. So similar to observability, we started with logs but expanded to a full suite so that you can do everything. You can have both endpoint protection, you can have cloud security, all of it from one solution. >> Security is a very crowded market as well. What's your superpower? >> Ken Exner: What's our super power? >> Yeah. >> I think it, a lot of it is just the openness. It's the open platform, there's the community around it. People know and love the, the Elastic Search ELK stack and use it, we go into businesses all the time and they're familiar, their security engineers are using our product for searching through logs. So they're familiar with the product already and the community behind it. So they were excited about being able to use detection rules from other businesses and stay on top of that and be part of that community. The transparency of that is important to the customers. So if you're trying to be the most secure place, the most secure business, you want to basically invest in a community that's going to support that and not be alone in that. >> Right, absolutely, so much that rides on that. Favorite customer example that you think really articulates the value of Elastic, its openness, its transparency. >> Well, there's a customer Dish Media Dish Networks that's going to present here at re:Invent tomorrow at 1:45 at Mandalay Bay. I'm excited about their example because they use it to manage, I think it's 10 billion records a day across 25 million devices. So it illustrates the scale that we can support for managing observability for a company but also just sort of the unique use cases. We can use this for set top boxes for all their customers and they can track the performance that those customers are having. It's a unique case that a lot of vendors couldn't support but we can support because of the openness of the platform, the open data architecture that we have. So I think it illustrates the scale that we support, the elasticity, but also the openness of the data platform. >> Awesome and folks can catch that tomorrow, 1:45 PM at the Mandalay Bay. Last question for you, Ken, is you have a bumper sticker. >> Ken Exner: A bumper sticker? >> A bumper sticker you're going to put it on your fancy sexy new car and it's about elastic, what does it say? >> Helping you get answers out of data. So yeah. >> Love it, love it. Brilliant. >> Ken Exner: Thank you. >> Short and sweet. Ken, it's been a pleasure. >> It's been a pleasure being here, thank you. >> Thank you so much for sharing your journey with us as an Amazonian now into Elastic what Elastic is doing from a product perspective. We will keep our eyes peeled as Dave was saying. >> Ken Exner: Fantastic. >> The data show is really strong spending momentum so well done. >> Thank you very much, good to meet you. >> Our pleasure. For our guest and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

[Thomas Hazel] - Hello, this is Thomas Hazel, founder CTO here at ChaosSearch. And tonight I'm going to demonstrate a new feature we are offering this quarter called JSON Flex. If you're familiar with JSON datasets, they're wonderful ways to represent information. You know, they're multidimensional, they have ability to set up arrays as attributes but those arrays are really problematic when you need to expand them or flatten them to do any type of elastic search or relational access, particularly when you're trying to do aggregations. And so the common process is to exclude those arrays or pick and choose that information. But with this new Chaos Flex capability, our system uniquely can index that data horizontally in a very small and efficient representation. And then with our Chaos Refinery, expand each attribute as you wish vertically, so you can do all the basic and natural constructs you would have done if you had, you know, a more straightforward, two dimensional, three dimensional type representation. So without further ado, I'mma get into this presentation of JSON Flex. Now, in this case, I've already set up the service to point to a particular S3 account that has CloudTrail data, one that is pretty problematic when it comes down to flattening data. And again, if you know CloudTrail, one row can become 10,000 as data gets flattened. So without further ado, let me jump right in. When you first log into the ChaosSearch service, you'll see a tab called 'Storage'. This is the S3 account, and I have variety of buckets. I have the refinery, it's a data refinery. This is where we create views or lenses into these index streams that you can do analysis that publishes it in elastic API as an index pattern or relational table in SQL Now a particular bucket I have here is a whole bunch of demonstration datasets that we have to show off our capabilities and our offering. In this bucket, I have CloudTrail data and I'm going to create what we call a 'object group'. An object group is a entry point, a filter of which files I want to index that data. Now, it can be statically there or a live streaming. These object groups had the ability to say, what type of data do you want to index on? Now through our wizard, you can type in, you know, prefix in this case, I want to type in CloudTrail, and you see here, I have a whole bunch of CloudTrail. I'mma choose one file to make it quick and easy. But this particular CloudTrail data will expand and we can show the capability of this horizontal to vertical expansion. So I walked through the wizard, as you can see here, we discovered JSON, it's a gzip file. Leave flattening unlimited 'cause we want to be able to expand infinitely. But this case, instead of doing default virtual, I'm going to horizontally represent this information. And this uniquely compresses the data in a way that can be stored efficiently on disc but then expanded in our data refinery on Pond Query or search requests. So I'mma create this object group. Now I'm going to call this, you know, 'JSON Flex test' and I could set up live indexing, SQS pops up but I'mma skip that and skip Retention and just create it. Once this object group is created, you kind of think of it as a virtual bucket, 'cause it does filter the data as you can see here. When I look at the view, I just see CloudTrail, but within the console, I can say start indexing. Now this is static data there could be a live stream and we set up workers to index this data. Whether it's one file, a million files or one terabyte, or one petabyte, we index the data. We discover all the schema, and as you see here, we discovered 104 columns. Now what's interesting is that we represent this expansion in a horizontal way. You know, if you know CloudTrail records zero, record one, record two. This can expand pretty dramatically if you fully flatten it but this case we horizontally representing it as the index. So when I go into the data refinery, I can create a view. Now, if you know the data refinery of ChaosSearch, you can bring multiple data streams together. You can do transformations virtually, you can do correlations, but in this case, I'm just going to take this one particular index stream, we call 'JSON Flex' and walk through a wizard, we try to simplify everything and select a particular attribute to expand. Now, again, we represent this in one row but if you had arrays and do all the permutations, it could go one to 100 to 10,000. We had one JSON audit that went from one row to 1 million rows. Now, clearly you don't want to create all those permutations, when you're tryna put into a database. With our unique index technology, you can do it virtually and sort horizontally. So let me just select 'Virtual' and walk through the wizard. Now, as I mentioned, we do all these different transformations changed schema, we're going to skip all that and select the order time, records event and say, 'create this'. I'm going to say, you know, 'JSON Flex View', I can set up caching, do a variety of things, I'm going to skip that. And once I create this, it's now available in the elastic API as an index pattern, as well as SQL via our Presto API dialect. And you can use Looker, Tableau, et cetera. But in this case, we go to this 'Analytics tab' and we built in the Kibana, open search tooling that is Apache Tonetto. And I click on discovery here and I'm going to select that particular view. Again, it looks like, oops, it looks like an index pattern, and I'mma choose, let's see here, let's choose 15 years from past and present and make sure I find where actually was timed. And what you'll see here is, you know, sure. It's just one particular data set has a variety of columns, but you see here is unlike that record zero, records one, now it's expanded. And so it has been expanded like a vertical flattening that you would traditionally do if you wanted to do anything that was an elastic or a relational construct, you know, that fit into a table format. Now the 'vantage of JSON Flex, you don't have that stored as a blob and use these proprietary JSON API's. You can use your native elastic API or your native SQL tooling to get access to it naturally without that expense of that explosion or without the complexity of ETLing it, and picking and choosing before you actually put into the database. That completes the demonstration of ChaosSearch new JSON Flex capability. If you're interested, come to ChaosSearch.io and set up a free trial. Thank you.

>> Narrator: From around the globe it's theCUBE, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by AWS Global Partner Network. >> Hello and welcome to theCUBE Virtual and our coverage of AWS re:Invent 2020 with special coverage of APN partner experience. We are theCUBE Virtual and I'm your host, Justin Warren. And today I'm joined by Ed Walsh, CEO of ChaosSearch. Ed, welcome to theCUBE. >> Well thank you for having me, I really appreciate it. >> Now, this is not your first time here on theCUBE. You're a regular here and I've loved it to have you back. >> I love the platform you guys are great. >> So let's start off by just reminding people about what ChaosSearch is and what do you do there? >> Sure, the best way to say is so ChaosSearch helps our clients know better. We don't do that by a special wizard or a widget that you give to your, you know, SecOp teams. What we do is a hard work to give you a data platform to get insights at scale. And we do that also by achieving the promise of data lakes. So what we have is a Chaos data platform, connects and indexes data in a customer's S3 or glacier accounts. So inside your data lake, not our data lake but renders that data fully searchable and available for analysis using your existing tools today 'cause what we do is index it and publish open API, it's like API like Elasticsearch API, and soon SQL. So give you an example. So based upon those capabilities were an ideal replacement for a commonly deployed, either Elasticsearch or ELK Stack deployments, if you're hitting scale issues. So we talk about scalable log analytics, and more and more people are hitting these scale issues. So let's say if you're using Elasticsearch ELK or Amazon Elasticsearch, and you're hitting scale issues, what I mean by that is like, you can't keep enough retention. You want longer retention, or it's getting very expensive to keep that retention, or because the scale you hit where you have availability, where the cluster is hard to keep up running or is crashing. That's what we mean by the issues at scale. And what we do is simply we allow you, because we're publishing the open API of Elasticsearch use all your tools, but we save you about 80% off your monthly bill. We also give you an, and it's an and statement and give you unlimited retention. And as much as you want to keep on S3 or into Glacier but we also take care of all the hassles and management and the time to manage these clusters, which ends up being on a database server called leucine. And we take care of that as a managed service. And probably the biggest thing is all of this without changing anything your end users are using. So we include Kibana, but imagine it's an Elastic API. So if you're using API or Kibana, it's just easy to use the exact same tools used today, but you get the benefits of a true data lake. In fact, we're running now Elasticsearch on top of S3 natively. If that makes it sense. >> Right and natively is pretty cool. And look, 80% savings, is a dramatic number, particularly this year. I think there's a lot of people who are looking to save a few quid. So it'd be very nice to be able to save up to 80%. I am curious as to how you're able to achieve that kind of saving though. >> Yeah, you won't be the first person to ask me that. So listen, Elastic came around, it was, you know we had Splunk and we also have a lot of Splunk clients, but Elastic was a more cost effective solution open source to go after it. But what happens is, especially at scale, if it's fall it's actually very cost-effective. But underneath last six tech ELK Stack is a leucine database, it's a database technology. And that sits on our servers that are heavy memory count CPU count in and SSDs. So you can do on-prem or even in the clouds, so if you do an Amazon, basically you're spinning up a server and it stays up, it doesn't spin up, spin down. So those clusters are not one server, it's a cluster of those servers. And typically if you have any scale you're actually having multiple clusters because you don't dare put it on one, for different use cases. So our savings are actually you no longer need those servers to spin up and you don't need to pay for those seen underneath. You can still use Kibana under API but literally it's $80 off your bill that you're paying for your service now, and it's hard dollars. So it's not... And we typically see clients between 70 and 80%. It's up to 80, but it's literally right within a 10% margin that you're saving a lot of money, but more importantly, saving money is a great thing. But now you have one unified data lake that you can have. You used to go across some of the data or all the data through the role-based access. You can give different people. Like we've seen people who say, hey give that, help that person 40 days of this data. But the SecOp up team gets to see across all the different law. You know, all the machine generated data they have. And we can give you a couple of examples of that and walk you through how people deploy if you want. >> I'm always keen to hear specific examples of how customers are doing things. And it's nice that you've thought of drawn that comparison there around what what cloud is good for and what it isn't is. I'll often like to say that AWS is cheap to fail in, but expensive to succeed. So when people are actually succeeding with this and using this, this broad amount of data so what you're saying there with that savings I've actually got access to a lot more data that I can do things with. So yeah, if you could walk through a couple of examples of what people are doing with this increased amount of data that they have access to in EKL Search, what are some of the things that people are now able to unlock with that data? >> Well, literally it's always good for a customer size so we can go through and we go through it however it might want, Kleiner, Blackboard, Alert Logic, Armor Security, HubSpot. Maybe I'll start with HubSpot. One of our good clients, they were doing some Cloud Flare data that was one of their clusters they were using a lot to search for. But they were looking at to look at a denial service. And they were, we find everyone kind of at scale, they get limited. So they were down to five days retention. Why? Well, it's not that they meant to but basically they couldn't cost-effectively handle that in the scale. And also they're having scale issues with the environment, how they set the cluster and sharding. And when they also denial service tech, what happened that's when the influx of data that is one thing about scale is how fast it comes out, yet another one is how much data you have. But this is as the data was coming after them at denial service, that's when the cluster would actually go down believe it or not, you know right. When you need your log analysis tools. So what we did is because they're just using Kibana, it was easy swap. They ran in parallel because we published the open API but we took them from five days to nine days. They could keep as much as they want but nine days for denial services is what they wanted. And then we did save them in over $4 million a year in hard dollars, What they're paying in their environment from really is the savings on the server farm and a little bit on the Elasticsearch Stack. But more importantly, they had no outages since. Now here's the thing. Are you talking about the use case? They also had other clusters and you find everyone does it. They don't dare put it on one cluster, even though these are not one server, they're multiple servers. So the next use case for CloudFlare was one, the next QS and it was a 10 terabyte a day influx kept it for 90 days. So it's about a petabyte. They brought another use case on which was NetMon, again, Network Monitoring. And again, I'm having the same scale issue, retention area. And what they're able to do is easily roll that on. So that's one data platform. Now they're adding the next one. They have about four different use cases and it's just different clusters able to bring together. But now what they're able to do give you use cases either they getting more cost effective, more stability and freedom. We say saves you a lot of time, cost and complexity. Just the time they manage that get the data in the complexities around it. And then the cost is easy to kind of quantify but they've got better but more importantly now for particular teams they only need their access to one data but the SecOP team wants to see across all the data. And it's very easy for them to see across all the data where before it was impossible to do. So now they have multiple large use cases streaming at them. And what I love about that particular case is at one point they were just trying to test our scale. So they started tossing more things at it, right. To see if they could kind of break us. So they spiked us up to 30 terabytes a day which is for Elastic would even 10 terabytes a day makes things fall over. Now, if you think of what they just did, what were doing is literally three steps, put your data in S3 and as fast as you can, don't modify, just put it there. Once it's there three steps connect to us, you give us readability access to those buckets and a place to write the indexy. All of that stuff is in your S3, it never comes out. And then basically you set up, do you want to do live or do you want to do real time analysis? Or do you want to go after old data? We do the rest, we ingest, we normalize the schema. And basically we give you our back and the refinery to give the right people access. So what they did is they basically throw a whole bunch of stuff at it. They were trying to outrun S3. So, you know, we're on shoulders of giants. You know, if you think about our platform for clients what's a better dental like than S3. You're not going to get a better cross curve, right? You're not going to get a better parallelism. And so, or security it's in your, you know a virtual environment. But if you... And also you can keep data in the right location. So Blackboard's a good example. They need to keep that in all the different regions and because it's personal data, they, you know, GDPR they got to keep data in that location. It's easy, we just put compute in each one of the different areas they are. But the net net is if you think that architecture is shoulders of giants if you think you can outrun by just sheer volume or you can put in more cost-effective place to keep long-term or you think you can out store you have so much data that S3 and glacier can't possibly do it. Then you got me at your bigger scale at me but that's the scale we'r&e talking about. So if you think about the spiked our throughput what they really did is they try to outrun S3. And we didn't pick up. Now, the next thing is they tossed a bunch of users at us which were just spinning up in our data fabric different ways to do the indexing, to keep up with it. And new use cases in case they're going after everyone gets their own worker nodes which are all expected to fail in place. So again, they did some of that but really they're like you guys handled all the influx. And if you think about it, it's the shoulders of giants being on top of an Amazon platform, which is amazing. You're not going to get a more cost effective data lake in the world, and it's continuing to fall in price. And it's a cost curve, like no other, but also all that resiliency, all that security and the parallelism you can get, out of an S3 Glacier is just a bar none is the most scalable environment, you can build an environment. And what we do is a thin layer. It's a data platform that allows you to have your data now fully searchable and queryable using your tools >> Right and you, you mentioned there that, I mean you're running in AWS, which has broad experience in doing these sorts of things at scale but on that operational management side of things. As you mentioned, you actually take that off, off the hands of customers so that you run it on their behalf. What are some of the areas that you see people making in trying to do this themselves, when you've gone into customers, and brought it into the EKL Search platform? >> Yeah, so either people are just trying their best to build out clusters of Elasticsearch or they're going to services like Logz.io, Sumo Logic or Amazon Elasticsearch services. And those are all basically on the same ELK Stack. So they have the exact same limits as the same bits. Then we see people trying to say, well I really want to go to a data lake. I want to get away from these database servers and which have their limits. I want to use a data Lake. And then we see a lot of people putting data into environments before they, instead of using Elasticsearch, they want to use SQL type tools. And what they do is they put it into a Parquet or Presto form. It's a Presto dialect, but it into Parquet and structure it. And they go a lot of other way to, Hey it's in the data lake, but they end up building these little islands inside their data lake. And it's a lot of time to transform the data, to get it in a format that you can go after our tools. And then what we do is we don't make you do that. Just literally put the data there. And then what we do is we do the index and a polish API. So right now it's Elasticsearch in a very short time we'll publish Presto or the SQL dialect. You can use the same tool. So we do see people, either brute forcing and trying their best with a bunch of physical servers. We do see another group that says, you know, I want to go use an Athena use cases, or I want to use a there's a whole bunch of different startups saying, I do data lake or data lake houses. But they are, what they really do is force you to put things in the structure before you get insight. True data lake economics is literally just put it there, and use your tools natively to go after it. And that's where we're unique compared to what we see from our competition. >> Hmm, so with people who have moved into ChaosSearch, what's, let's say pick one, if you can, the most interesting example of what people have started to do with, with their data. What's new? >> That's good. Well, I'll give you another one. And so Armor Security is a good one. So Armor Security is a security service company. You know, thousands of clients doing great I mean a beautiful platform, beautiful business. And they won Rackspace as a partner. So now imagine thousand clients, but now, you know massive scale that to keep up with. So that would be an example but another example where we were able to come in and they were facing a major upgrade of their environment just to keep up, and they expose actually to their customers is how their customers do logging analytics. What we're able to do is literally simply because they didn't go below the API they use the exact same tools that are on top and in 30 days replaced that use case, save them tremendous amount of dollars. But now they're able to go back and have unlimited retention. They used to restrict their clients to 14 days. Now they have an opportunity to do a bunch of different things, and possible revenue opportunities and other. But allow them to look at their business differently and free up their team to do other things. And now they're, they're putting billing and other things into the same environment with us because one is easy it's scale but also freed up their team. No one has enough team to do things. And then the biggest thing is what people do interesting with our product is actually in their own tools. So, you know, we talk about Kibana when we do SQL again we talk about Looker and Tableau and Power BI, you know, the really interesting thing, and we think we did the hard work on the data layer which you can say is, you know I can about all the ways you consolidate the performance. Now, what becomes really interesting is what they're doing at the visibility level, either Kibana or the API or Tableau or Looker. And the key thing for us is we just say, just use the tools you're used to. Now that might be a boring statement, but to me, a great value proposition is not changing what your end users have to use. And they're doing amazing things. They're doing the exact same things they did before. They're just doing it with more data at bigger scale. And also they're able to see across their different machine learning data compared to being limited going at one thing at a time. And that getting the correlation from a unified data lake is really what we, you know we get very excited about. What's most exciting to our clients is they don't have to tell the users they have to use a different tool, which, you know, we'll decide if that's really interesting in this conversation. But again, I always say we didn't build a new algorithm that you going to give the SecOp team or a new pipeline cool widget that going to help the machine learning team which is another API we'll publish. But basically what we do is a hard work of making the data platform scalable, but more importantly give you the APIs that you're used to. So it's the platform that you don't have to change what your end users are doing, which is a... So we're kind of invisible behind the scenes. >> Well, that's certainly a pretty strong proposition there and I'm sure that there's plenty of scope for customers to come and and talk to you because no one's creating any less data. So Ed, thanks for coming out of theCUBE. It's always great to see you here. >> Know, thank you. >> You've been watching theCUBE Virtual and our coverage of AWS re:Invent 2020 with special coverage of APN partner experience. Make sure you check out all our coverage online, either on your desktop, mobile on your phone, wherever you are. I've been your host, Justin Warren. And I look forward to seeing you again soon. (soft music)

>> Narrator: From around the globe it's theCUBE, with digital coverage of AWS reinvent 2020 special coverage sponsored by AWS global partner network. >> Hello, and welcome to theCUBE virtual with our special coverage of AWS reinvent 2020 with additional special coverage of APN partner experience. We are theCUBE virtual and I'm your host, Justin Warren. And today I'm joined by Angelos Kottas who is vice president of product marketing at elastic and he comes to us from San Francisco. Angelos , welcome to theCUBE. >> Thank you, Justin. A pleasure to join you. >> Great to have you here. Now. I've been a big fan of elastic for a while have used your products in a variety of circumstances? You're big partners of AWS and have seen quite a bit of change over the last couple of years. We were talking just before we came on air. Maybe you could talk us through what elastic is doing with AWS and a little bit about those changes that you've seen over the last >> Absolutely one period. >> Sounds good Justin. So first of all many people know elastic as the makers of elastic search. One of the most popular open source of search engines and along with elastic search we have Kibana and beats and Logstash and many people know us as the Elk Stack, right? And so clearly we have roots in the open source community and people have used us for custom applications for years and years. One of the key changes over the last few years is that we've realized that many customers were doing some of the same things with elastic. So we said, what if we really focus on end to end experiences for our three core use cases? And so we chose three use cases and built solutions around them. What is enterprise search, right? Which is how do you find information on your website in your application or in your workspace? The second is observability. So think about software development software in every industry. What about dev ops? What about performance? What about consistency and last but not least, especially you know, with some of the current transitions in digital transformation, think about security. Think about your network security, your endpoint security and how you have visibility across your entire IT ecosystem. So we've chosen those three solution areas and put significant engineering into building out that experience. How quickly can we deliver value, how pre-built can the configuration be the integrations be, the workflow, the reporting and the dashboards around those use cases. The last piece, which is very relevant for reinvent is the transition to cloud, right? So we still offer a downloadable software and many of our customers and users download the elastic stack and deploy it on-prem and hybrid cloud environments. But one of the fastest growing deployment models is in the public cloud. And of course, elastic cloud on AWS is one of our major routes to market, happy to meet many of our customers where they are, which is on AWS. >> Oh it's great to be able to have that choice I think that people can download the software try and get it, get comfortable with it but then people often find that actually running software yourself, there's quite a lot of work involved in doing that. I know that I, I've experienced that myself. Just little things like maintenance and so on. So it sounds like you're actually taking care of a lot of that for customers if they move to the cloud service. But is there anything else special about the cloud service that customers might not be that aware of? >> Well, I mean, choice is a big part of it and so it's not just do I choose cloud it's wearing cloud. So we've actually, we now run elastic cloud in over 40 regions around the world. So we can be close to you in terms of latency, and in terms of performance, in terms of data sovereignty we can be local to your environment. The other aspect it's not just how we simplify deploying elastic. You know, clearly we architect it we install it, deploy and upgraded for you. But also we have focused quite a bit on integrating cloud data sources. So with AWS, as an example, we look at all of the applications and data sources that you host on AWS. And we think about how do we get those data streams how do we get that data directly integrated into elastic. One final piece, actually which I forget sometimes it's not the technical side. It's the business side is the commercial integration, right? So we are, you know, very happy to to be listed on the AWS marketplace. We've made it easy for you to find, deploy and actually build through your AWS commercial agreements via the marketplace integration. >> Right, so easy to get started and to start using it and search is certainly something that elastic is famous for. But you mentioned observability there, a bit of a question I have around observability is, is it that just a fancy way of saying monitoring? There seems to be this, this buzzword around the place. So what do you mean when you say observability? >> So one of the key foundational principles of the elastic observability solution is that, you know you want a unified data database a unified place to store all that data. So it is stretching across logs metrics, application traces it's bringing together a common platform that lets you look at different aspects of observability. So whether you're doing end to end application traces or whether you're just collecting infrastructure logs and looking at performance metrics it's kind of across the board, even looking at things in our most recent release that just came out last week, you know expanding on user experience, monitoring and synthetics. So you can optimize web interactions and web experiences, for example. >> Right. Okay. So there's a bunch of different types of data that are involved there. I know traditionally people would silo those off into a specific customized thing just for that particular type of workload. What is it about elastic that means that you can put all of these in one place? >> Yeah. You know, one of early catchphrases for what does elastic do? What do we focus on? The value we deliver is speed, scale and relevance. And so one of the things that is famous about the elastic way of doing things is the way in which we index data on ingest and so that you can get search queries that return within milliseconds and so that performance characteristics. A second one is scale. And this is actually really key, not just for observability but right next to observability, you get security as well. We like to say, if you're going to observe you might as well protect as well. So when you expand to that universe you have not just hundreds of devices you might have thousands or tens of thousands of devices that you are ingesting information whether it's operational data, whether it's security data. So scale becomes extremely significant. How can you scale horizontally and vertically and maintain that performance even when you are in a fortune 500 scale infrastructure The last piece is relevance. And so, you know that data it's not just about knowing what to look for. It's about using things like machine learning and anomaly detection to uncover unusual patterns of behavior and proactively alerting and making that visible through notifications and through alerts that can actually integrate not just with your elastic operations but actually with third party software. Maybe you want to trigger a service now ticket or a, you know, a Slack integration and all of that is part of the elastic platform as well. >> Right? Okay. So by putting everything kind of in one place that is around what you're talking about. So we have enterprise search and then to be able to find things we're collecting all of the data that we need to find things. And then you touched on security at the beginning and we're starting to talk around security there. So I'm keen to move on to that >> (chuckles) >> By looking at all of these, these different, these signals we can hopefully then manage some of security which I know is very much front of mind for everyone over the last year. Cyber security has very much come to the forefront of everyone's thinking. >> Absolutely. And you know, we've been on the network side of security for some time. So we've had our SIM solution, you know security information event monitoring, but we made a very strategic acquisition a little over a year ago. We saw that a critical piece of visibility is also the end point. And so we partnered with end game and eventually we acquired end game to create end to end visibility on that security. So it is being able to connect, you know the path of data from your servers and network devices all the way to the end points. And an example of the power of this unified architecture is the new elastication that we introduced in beta a couple of months ago. We said, what if we had a single deployment that both does endpoint protection and does malware scanning of your endpoint devices while also ingesting data into your observability systems. And so that's kind of the power of the platform the ability to use common infrastructure common integrations, so that every use case you adopt on top of elastic, it sort of multiplies the value you're getting from using elastic as an infrastructure player. >> Alright that's a good combining a couple of different things into the one tool that you can use. I know sys who I'd spoken to are quite concerned about the proliferation of tools that they have in their environment, it seems that they've bought lots of different things but a lot of them are kind of sitting in a drawer, not really being used. And partly, it's just, we we have so many different ways of dealing with these issues. None of it's really flushed out or sorry has been fully fleshed out that we definitely know this is the one true way to solve this. So what are you hearing from customers as they start to use these security functions? What are they telling you about the way that they're managing security in their environments? >> Well, you know, we think about a few different personas in the security market, right? We think about threat hunters, for example who are looking to identify threats, we're looking at the operations team that do the cleanup that do the you know, the resolution of security threats. And we also, so there's a, you know, there's two competing terms in the security market. We have security operations in the observability world. We have dev ops, right? And, and developer, you know, the continue of developer and deployment into a dev ops role. And so we're starting to see this concept of DevSecOps, right? What if there is a unified set it's not all things to all people and that's an important thing, right? We're not trying to be, your single security vendor for all IT security needs, but instead we're saying, what if you had a security operations analyst, a thrent Hunter an executive, a CSO who's looking for, you know an overall level of threat or compliance to policy and you can bring those experiences together through the elastic security solution. >> Right? So it sounds like you you're trying to allow people to work in the way that they need to providing them the tools that suit their particular circumstance. >> That's right. That's right. I mean, in terms of how do you define success? You look at metrics like meantime to resolution, you know can we reduce the meantime to resolution or you look at law collection and how much more efficiently can you collect logs? You look at asset monitoring and what percentage of your IT infrastructure you actually have unified visibility into, you know we have one great cloud customer OALEKS group. They are a popular online marketplace, you know and they quoted to us that they had a 1900% increase in law collection, right. In terms of scope of what they are collecting logs on they reduce that MTTR by 30% for security incidents so dramatically streamlined and shortened the exposure. And then they increased asset monitoring by 35% across cloud, as well as on-prem. And I think that's the other piece is that, you know whether you deploy your security in the cloud or on-prem you are looking to secure your hybrid environment. And so being able to take data feeds from your SAS partners from your infrastructure running on AWS as well as from those endpoint devices. >> Well, it sounds like there's plenty of scope of interesting things for people to come and have a look at it, at elastic. So, Angelos, thank you so much for joining us here, please. Thank you to my guests Angelos Kottas, vice president of product marketing at elastic. You've been watching theCUBE virtual and our coverage of AWS reinvent 2020 with special coverage of APN partner experience. Make sure you check out all our coverage on your desktop laptop or on your phone, wherever you are. I've been your host, Justin Warren. And I look forward to seeing you again soon. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Application performance management AKA APM, you know it's been around since the days of the mainframe. Now, as systems' architectures became more complex, the technology evolved to accommodate client-server, web-tier architectures, mobile and now of course, cloud-based systems. A spate of vendors have emerged to solve the sticky problems associated with ensuring consistent and predictable user experiences. The market has grown, I mean it's decent size, it's about $5 billion globally. It's growing at a consistent 10% CAGR. It's got a variety of established companies and new entrants that are attacking this space. Hi everyone, welcome to this week's Wikibon Cube Insights powered by ETR. My name is Dave Vellante and today, we welcome back ETR's Erik Bradley, who was the chief engagement strategist at Aptiviti which is the holding company of our data partner, ETR. Erik, my friend, great to see you. Thanks so much for coming on and spending some time with us. >> Oh, always enjoy it Dave. Great to see you too and I'm just glad I got some fresh material for ya. >> As always, you have fresh data. Now, Erik just recently hosted an ETR VENN session and on this particular topic, APM. Now VENNs are an open round table, they're exclusively available to ETR's clients and what we do is we sometimes come in theCUBE and we summarize those sessions in our Breaking Analysis. Now Erik, yo let's start with a summary slide here, guys, if you could bring that up, we just want to make a couple of points and... So as I said Erik, I mean this started back, you know in the System/390 days. Now, distributed systems and cloud of course create a lot more complexity, you got data that's really fragmented. You got user data, you got application data, you have infrastructure data and it gets complicated and you've got guys in lab coats having to come in and diagnose these stuff, lot of tribal knowledge. What are you seeing in the space? >> Well yeah, you know to start back, you know it's funny when the panel I hosted, one of the guys even brought up Tivoli, how long ago that was right? Then of course you get, you know you have the solar winds and you had people like that trying to just kind of monitor your network. You know what we've heard a lot about now is infrastructure has really become code-based. So when that happens, you really start wondering to yourself the lines are blurring between infrastructure and application because at the end of the day, what you're really monitoring is code. So it has gotten incredibly complex, you have OnPrem, you have hybrid, you have multi-cloud approach so it has gotten extremely complex and there's also now a third wave of next-gen vendors getting involved in the mix as well. As you're aware, New Relic and Datadog, obviously, Splunk has been in logging and monitoring for a long time. You also had some of the traditional players throw their hat in the ring through acquisition, that you know AppDynamics gobbled up by Cisco and obviously Splunk trying to continue to reinvent themselves a little bit by SignalFx. So it is a very crowded, complex space, it is a complicated problem but it's also a problem that needs to be solved. You know, we were looking at, you said in your intro about, it's only about a $5 billion market right now but there's been a lot of data out there from industry analysts saying that that's going to grow quite handsomely over the next five years and it could get up to 13, 14, 15 billion. And when I asked my panel about that, I had one gentleman say without a doubt, they see the next 10 years that spending in this space will continue. And when you pry and ask why, they simply state that digital transformation is not going to stop, it's marching forward, whether anyone likes it or not and as it does, monitoring is going to be critical, it's only going to increase and increase and increase. So right now, to your point, it's a small market but it's a growing market and there's a lot of entrance in there and their whole goal is to reduce this complexity that you're talking about. >> Now, one of the things we heard from the panel, guys if you bring up that same slide again, you know the third point on that slide was what's closely tied to digital transformation. You heard a number of individuals say, "Look, your digital business is critical, it's all about monitoring your applications and your data and your infrastructure. And we heard a lot that they wanted a, a single pane of glass and you made a number of points about the market. What are your thoughts on both the digital transformation, maybe the COVID acceleration of that mandate and that notion of a single pane of glass, is that aspirational or is it, in your view, something that is actually technically feasible? >> Not only is it technically feasible, it has to happen. It's going to be demanded by the large enterprise, they can't continue to monitor hundreds and hundreds of applications. They need something that not only can give them observability through their entire stack, but they need to be able to view it in one way, there's enough fatigue in monitoring and logging. And actually it goes even further than one pane of glass, they're demanding that these systems can now actually employ machine learning algorithms to be proactive. It's not enough to just say, "Okay, I observed this," you have to let me know that this may happen in the future and what to do about it. So not only is it feasible, it's something that is being demanded by the end-user market and the players that survive are the ones that already have that in their roadmap. >> Now, as we always like to do in these sessions, we're going to bring up some ETR data and we like to position the companies. So what we do is, we're going to bring up some of the pure players, pure-play companies and you can see them on this slide. But Erik, and when we talk about companies in this space, they are well over a dozen. It's just again for reference, you know it's Cisco with AppD, you mentioned that before Dynatrace is one of the leaders, New Relic has been around for awhile and is doing well, Splunk, Datadog. Now of course, and we're not showing them here, AWS, Microsoft and Google cause they just sort of, they pollute the chart. But so I want to start with the guys that are on this view and maybe talk about a few. Elastic came up a lot, certainly AppD came up a little, Dynatrace was obviously mentioned, especially in large organizations. Lot of conversations about New Relic. So let's go through them. Where do you want to start here? >> Yeah there's a lot to go through and we did spend the majority of the panel talking about the individual players, the differences between them and also what we thought their longer term prospects were but yeah, we'll go through each one. I think maybe to start with, let's go back in time a little bit, right? Cisco is a wonderful acquirer, they do a great job at M&A. A lot of companies will acquire something and let it die on the vine. Cisco has proven recently that they are reinventing themselves as a full platform play, whether that be through, you know, kind of, their networking reach or whether it be through the security. And AppDynamics is one of those that actually kind of gives you a little bit of both with being able to monitor. It is a great play for people that are already involved with Cisco. Now, I don't think you're going to see too many people that are non-Cisco customers run out and buy it. There you're going to see some of them, maybe the pure plays or one of my guests called the third wave of vendors. And that third wave is really about a Datadog and a New Relic. Let's talk about Datadog first. >> Yeah let's bring that back up guys, if you would. Now let me just, sorry to interrupt you Erik (indistinct) The vertical axis here is net score, that's the ETR's primary metric, and that's an indication of spending velocity, the higher, the better. And on the horizontal axis is market share. Now we're showing the July data, the October data is in the field, you know once ETR releases that to its clients, then we'll share that with you. But the first thing that jumps out at me is other than Elastic Erik, I mean, I'm not blown away by the spending momentum in this space but let's talk about that and then some of your thoughts on the specific vendors. >> Yeah, you know I'll go back because you asked a little bit about the digital transformation, I don't think I answered it fully. So to your comment about maybe not being impressed with the spend, I think this is one where the spend is going to come, kind of as a laggard because you're not going to rush out and go buy the software to monitor until you've built out the, what needs to be monitored. So as we're seeing this increase in the digital transformation, and I think you and I had a conversation in the past, but when COVID first hit and I did a series of panels, we had one person say that this virus is going to increase digital transformation by five to 10 years. Now that was an amazing statement. Basically, if you were on the fence, if you didn't, if you weren't already heading down to digital transformation, you needed to play catch up quickly. So now that you are doing that right, now that you're moving from OnPrem to a multicloud or a hybrid cloud environment, you have to get observability, you have to get monitoring into it. So now these players start to play catch up and this is where you're going to see the proof of concepts and you're going to see people trying to decide which direction they're going to take their company. Now back to the actual vendors. I believe that there is some differentiation, right? So we'll just take, for instance, Splunk. Splunk is obviously probably the biggest boy on the block when it comes to just straight up logging and monitoring. They've leveraged that big boy position to really, you know, add some costs, kind of intimidate their customers they've been compared in the past of the type of things that Oracle used to do from their cost perspective. And that's opened up some new competition, Datadog is one of those. According to my panel, Datadog is viewed more for logging and monitoring than it is truly full end-to-end observability throughout your entire network and application system. So that is one of the areas that's there. Now, to stay on those two names for a quick second, Splunk obviously has some holes in what they're trying to offer, they went out and tried to buy SignalFx to fill one of those holes. Now according to my panel again, did a great job filling that hole, problem is if you have a boat with three holes, you can't put your fingers everywhere. So they think, hey listen, Splunk scrape, they're going to keep the company they have and I know that we can talk a little bit more about valuations and the equity side later, but I think it's very clear that their sales and revenue are trending flat to down, whereas some of these other names still have great acceleration in their sales. So Splunk and Datadog both are really facing pressure from Elastic or generally just open-source. >> I was struck by the panel and how much emphasis they, how much complaining they did about Splunk pricing. Generally, I feel like hey, if your price is too high is the biggest objection, that's actually not a bad thing for a company but the way they kept hitting on it and said, "Hey, we're actively looking for alternatives" and Datadog was one of those and given the momentum that Datadog has, I don't think that that's necessarily a positive. But you know Splunk has a lot of loyal customers but you know to your point if you go back to the slide, Elastic came up very, very strong and they are head and shoulders from a spending momentum above the rest of the crowd here. >> Right. And you know, so you're right. If the only problem with a vendor or a technology is cost, usually you live with it because that means it's giving you what you need. So okay, it's expensive but it's also the best in breed and that's where Splunk has been for a very long time. And I think they're resting on their laurels knowing that. Enter Elastic and you say to these guys, the panel, I asked them, well okay, you can make Elastic work but is it truly a viable alternative from a technology standpoint? And the answer to that was not only is it viable, it's half the price. So if you can bring something in that can do the job the same and it's half the cost, it's really difficult not to at least try. And I had one of the other gentlemen who was a Datadog customer said, "Listen, we love Datadog, we were a huge customer and then I started getting enormous bills and I just switched over to open-source, I switched to Elastic, I switched to Kibana, I switched to Kafka and I can do this search myself. Now the difference is not every enterprise has the human skillset to do so and I'm not saying Splunk's going to turn around to disappear tomorrow, not even close. Because there is a difference in spending that money with the vendor or spending that money developing the human skillset to use open-source. But the bigger backdrop here is there are more alternatives than there used to be, there's more competition and the space is getting very crowded. >> Yeah, comment on open-source. I mean open-source is free like a puppy. But the thing about that, and we had one of the panelists was a very senior consultant, exclusively work with very large companies, he told a story about one of the companies years ago, he came in to solve a problem. The problem was they had 70% availability and then they had no visibility on their infrastructure and there's really no great, no good monitor, they get them up to whatever, five nines or two, three nines or wherever they got them to, but dramatic improvement. And so, but he said, "Look it, I work with companies with billions of dollars, $3 billion IT budgets so they don't rely on open-source for this stuff, they're happy to spend." But there's a huge market, particularly in the mid size where we heard that New Relic plays in a big way, it might be more receptive to open-source. >> Couple of great points there Dave, honestly. I'm going to jump over to the use case that was given by that person who was in a healthcare role. And essentially the part I didn't write into my summary was that his CEO was two days away from shutting down the entire business because he was so frustrated that he had no observability and Dynatrace was the one that was able to step in and fix that. And this gentleman did say that the majority of the companies that he does work with which are all in the Fortune 100, Dynatrace has a stranglehold in that spot. So that's really interesting to note. Now on the flip side, when pushed a little bit more later in the panel, he said, "Dynatrace is sort of resting on its laurels from a product roadmap standpoint and that's going to open up the possibility of a New Relic getting in," a transition to New Relic as you mentioned on their small to medium sized business. They recently launched a new pricing strategy which is basically a free version to get you involved to kind of get their hooks into you and see if you can work it out. And basically what they're trying to do there I think is, you know, make up for their lack of marketing. As you saw the panel that we spoke about said, "New Relic's technology is fantastic." They have the ability to provide a single pane of glass which is the Holy Grail in this space and they have the ability to provide machine learning and proactive type of ability which again are the two things that all of the end-users are asking for. The problem is that most people might not be aware of it because New Relic doesn't have as flashy a marketing department, they don't have the dollars as much as the others to go out there and compete with the Splunk and Dynatrace and Cisco. But from a roadmap perspective, it was almost unanimous that our panel agreed, New Relic is by far, one of the leaders from a functionality standpoint. >> Yeah, if you guys bring that slide up one more time, the X Y. I mean, I look at where New Relic is and I'm like wow, I'm surprised. I mean this company, I mean they were the hot company for awhile and I think still have the capability. You're talking about the technology. NRDB, New Relic database is like, it kicks ass. In fact, you know Erik, somebody brought up in the panel that they thought that snowflake could compete in this market because essentially Snowflake's positioning is this data cloud. But you know, here's New Relic, they have a purpose-built database specifically for monitoring an APM so you would think that with that technology, they could really make some moves. And then I just want to bring in two other companies to the mix here. Honeycomb who I think even their founder and former CEO now CTO, she coined the term I believe, observability. And there's another company that is run by Jeremy Burton, company's called Observe, okay (indistinct) and it's funded by the Silicon Valley Mafia. So that's going to be an interesting one to watch, they're coming out, well they're out of stealth but they're doing a launch on October 7th. So I think those are two companies that could disrupt this space and I would expect to see, as you said, it's a latent momentum in net score from a dataset standpoint because people are trying to plug the holes cause of COVID, you know security, work from home, that pivot and now it's really on to digital transformation and that's where APM really comes in. >> It really does and again, it comes back to that comment someone made a long time ago that everything's becoming code as software eats the world and everything becomes code, you need the ability to kind of monitor that code, enter Honeycomb. And as you know, we have two different studies at ETR, one of them is for emerging technology. Honeycomb is in our emerging technology study that's more of a private series B to series E round stage whereas our main study is for companies that are pre IPO or already public. But Honeycomb is a little bit different in my opinion, that they're focused very much so on the developers or the software engineers. They're a very microservices oriented type of product whereas some of the other ones may have started as an infrastructure monitoring and then kind of work their way backward into application. But Honeycomb certainly needs to be observed and it's funny when you talk about that, the one thing I think is, "Oh great, more players." The crowded space gets even more crowded. And I think well you know, kind of foreshadowing something you and I will be speaking about in a little bit but there's a lot of players in this space and there's a lot of other possible interest in there. You mentioned Snowflake. It actually wasn't brought up from our panelists, it was a question that came from one of my clients that said, "Hey, I'm curious, can snowflake play in this space?" And the panel thought about it for a second and said, "There's absolutely no reason why they can't, they most certainly can." And we all know the cash they have so I mean the easiest way to play in that would maybe be to buy some of the technology, integrate it in and yeah, they have that portability. And if I can real quickly, they've just, one of the things that came out that was so important about this, we haven't spoken about the vendors is, is the public cloud. The public cloud offers this. They offer monitoring, they'll give it to you for free. If I'm going to run Kubernetes at Google, I'm going to get the monitoring for free which is super nice, right? But if I have an enterprise that has multicloud or hybrid cloud, and I'm working outside of that public cloud silo, it doesn't work. This is the exact conversation you and I had about Snowflake. AWS Redshift's fantastic but it doesn't work outside of AWS. So if every one of our enterprises continues on the digital transformation, they need portability. They have to be able to go across any architecture structure and that's why these independent providers are really starting to gain steam when you would think they could never compete with the public cloud. >> Yeah man, that's a great point. And we've talked about this in the context of Snowflake that who are you going to trust with your multi-cloud strategy? Are you going to trust AWS? Are you going to trust Google? Yeah, okay, they got Anthos but we kind of know why they're taking that posture. Microsoft, look, I'm probably going to partner with somebody who can, who's maybe I have a relationship with them with my OnPrem and that is really sort of agnostic to the various clouds so I'm glad you brought that up. And you know the point you're making about Honeycomb is a good one and I'll add that, again, it gets more complex with microservices and containers, that's spinning them up, spinning them down. Sometimes these, first of all, these microservices, sometimes aren't that micro and second of all, you're sometimes talking about hundreds of thousands of containers so it's a really increasingly complex environment. All right. What I want to do is-- >> You didn't even touch on serverless, we'll do that some other day. >> Oh, yeah, I mean absolutely. A hundred percent, right. So, now let's take a look at some of the valuations, guys if you bring that up for me. So I put this little chart together and it's always instructive. Now I like to, simple guy Erik so I like to... So you see, the company, I take a trailing 12-month revenue and then the market cap as of 9/25. And then just a simple revenue multiple, just to get a sense, it's not a hardcore valuation model but it's interesting and there usually is a correlation to the growth rate, I just pulled that off the latest quarterly growth rate. I mean, look at Datadog. I mean that's like Snowflake pre IPO valuations. I mean you're really, right around there with smaller revenue, smaller growth rate, Snowflakes up in the whatever 120% range but well eye-popping. You know the same valuation as Splunk, I mean that's just amazing. What do you make of this data? >> Well, you know I was an equity analyst for almost 15 years on the Wall Street side. So the, my first caveat is a trailing revenue to the multiple is not always the same because people are looking at what the forward expected revenue will be but I actually do see the correlation here. And when you brought this up, my eyes popped open. I do not understand why Datadog has a 27 billion market cap on a trailing 350 million in revenue. I just don't know if their forward looking growth really warrants that and at the same time, then you look at a Splunk, right? I mean they have two and a half billion in revenue but their growth rate's down and truthfully, when I see a -5% growth rate, I don't know why you weren't at 12% sales either. I would argue that there's quite a few names on here that could be in for a reckoning, ETR actually as far back as a year ago caught this in our data and said, "Hey, there's some inflection points here and I think investors need to pay attention to them." And since we came out with the July report, a lot of these names we're talking about, despite insane valuations in the equity markets are flat to down. And, you know I do think that, hey if they stay stagnant and their technology is right but it's a crowded space, I think we're really leading to the point where as one of my panelists said, this industry is ripe for consolidation. These players are not all going to be here in 12 months, it's that simple. >> Yeah and by the way, thank you for mentioning that as a former equity analyst, you were right (indistinct) 12 months, it's kind of the rear-view mirror. But I'll tell you, two reasons why I do that. One is, I put the growth rate in there so you can pick your own growth rate and your own forward revenue. The other is it's really easy for me to get TTM off a Yahoo as opposed to >> Right exactly. >> And so truth be told. But, guys bring that back up one more time cause I want to make a point about New Relic. I mean I think they are potentially right for an M&A because they got great technology. Now remember Elliot Management is in there and when Elliot's is in there, stuff's going to happen. They're going to start cleaning house, they're going to really create changes, they don't just get in in a big way and sit back and watch, they are extremely active. And the New Relic, leader in this space, great technology, great heritage. So either they got to clean up and get that valuation back up maybe as you pointed out, little bit better marketing posture, et cetera or they get taken out. >> Yeah and let's think about the two things that coincide, right? You have one of the world's best activist funds get involved in Elliot Management. And as you said, they don't get involved to just sort of watch or observe as we're talking about here today, they are very active in trying to get some sort of a, you know, corporate action done. And at the same time, all of a sudden New Relic comes out with a new pricing model. They're trying to create a moat around the small to medium business, right? They're trying to grow their footprint. Now the great thing about getting involved in small to medium businesses, it starts off for free but you grow with them. So I don't think those two are a coincidence, let me just put it that way. I think that they're coming in, they're trying to entrench themselves in a new market and set themselves up for future growth and I truly believe that based on the product roadmap and the feedback we were getting from the end-users in my panel, New Relic has the ability to look across all architecture, it has the ability to provide a single pane of glass and it has the ability to incorporate machine learning for proactive response. Their roadmap is fantastic, they have an active manager inside as an investor, I don't think they're going to be around for much, much longer. And obviously that you look around and you wonder who the acquirers will be and it might be one of the major cloud players. >> Yeah that would be interesting. I mean it gives them a play in a multicloud world and either they're going to just use that for their own advantage or they will actually see that as an opportunity, we'll be itching to watch. Alright, anything we didn't cover that you want to touch on or give us your final thoughts, please Erik. >> You know I would also just sort of mention a little bit about Splunk. This is a company that has a tremendous amount of revenue, a tremendous installed customer base but many, many times we've seen it before and Oracle is the greatest example. They kind of forget about their customers and they don't treat them properly. And I can't tell you how many people I have mentioned to me said, "Hey when this all went down in the viral pandemic and I went to Splunk and I asked for a little bit of pricing flexibility, I asked for this, I asked for that and they just wouldn't give it to me." And I wrote an article once called (indistinct) never forget similar to an elephant. And when they come out the other side, they're going to find a way to replace them. And today I also wrote an article that it was our 200th interview and I entitled it, The Splunk Funk. And basically it's about all the alternatives that are now out there, not just open source, but other vendors, even the vulnerability management players like a Rapid7, like a Tenable are getting into this space now. Fortinet, which one guy called "Fortaeverything" is a company that's really expanding. So I would just really kind of caution some of those vendors out there that don't rest on your laurels, don't take your customers for granted because sooner or later, they're going to be in a position to bite the back. >> Well I'll say this about Splunk, I've been following the company since the early part of last decade and I've done a lot of Cube interviews at their shows. They do have a passionate, passionate customer base, they got the experts that run around with that crazy hat and I've seen Splunk killers emerge for the last decade and so... But I think your point is right. I mean they've, the SignalFx acquisition was something that, it was a hole to fill and it gets them into a subscription-based model, they're going through that transition now. But I think they have some real gravity with their customer base. So, all right, let me summarize. For years, the application monitoring and management, it's really relied on alerts, logs, traces and even what I call tribal knowledge. In that world of pre-distributed systems, that was fine, like I said a trace can tell you what was going on. But things have begotten much more complicated architecturally with cloud and mobile and they're really changing fast now. Erik mentioned serverless, we talked about containers. So, today it's much harder to understand the customer experience because it's difficult to get a full picture of the data. And what I mean by that is that the user data, the application data, the infrastructure data, they're all fragmented and the Holy Grail solution really takes all this disparate data, it ingests it, it transforms it. Connects the dots if you will, across clouds, Onprem and then it shapes it, brings in machine intelligence, really creating an organic systems view that can proactively tell you that there's a problem coming. And finally, nearly absolute Nirvana is doing this in a way that non-technical people are going to be able to understand the true user experience. You know in theory, this is going to allow organizations to remediate in 110th the time with much, much lower costs and that's going to be critical in this world of digital transformation. So thank you Erik, really appreciate you coming on today. >> Always enjoy it Dave, it's always great talking to you and hopefully we'll do it again soon. >> All right, I can't wait. And thank you everybody for watching this episode of theCUBE Insights powered by ETR. Remember these episodes, they're all available on podcasts. We publish weekly on wikibon.com and siliconangle.com so you got to check that out. And don't forget, go to etr.plus for all the survey action. Would appreciate if you kindly comment on my LinkedIn post or tweet me @dvellante or email at david.vellante@siliconangle.com This is Dave Vellante. Thanks so much to Erik Bradley, be well and we'll see you next time. (bouncy music)

>> Live from San Francisco, it's theCUBE covering IBM Think 2019, brought to you by IBM. >> Hey, welcome back everyone. We're here live at theCUBE in Moscone North in San Francisco, for IBM Think 2019. I'm John Furrier with Stu Miniman, talking to all the top executives, top people here at IBM, getting the scoop on cloud and AI. Our next guest, Jason Gardner, Vice President of Worldwide Sales for Hybrid Cloud at IBM, manages key product, which is part of the IBM Cloud Private, big part of the announcements, big Cloud story here. It's multi-cloud, it's hybrid. Welcome back. >> It's hybrid multi-cloud. Thank you, for having me back. >> CUBE Alumni been on as early, going back as 2012. Now, one big event. >> I can't believe it's been that long. But yeah, I'm happy to be back and I can't believe I've been on theCUBE for so long. >> Talk about your new role, and you had previous roles within IBM dealing with the kind of clients and integration. Your role now is worldwide sales. You're taking this Cloud Private offering, bringing the customers, being as the linchpin for integration. Talk about what you do and some of the engagements you have. >> Yeah, previously, I was really focused in on development and offering management on, point products and how they help clients move to the Cloud. Things such as our Pure Business, our Spare Business, and now I've actually been able to move into a much more horizontal role, where I have the portfolio across the Hybrid Cloud integration side, so everything from our Websphere family, which includes IBM Cloud Private, straight to the integration challenges that that brings as well as our digital business automation portfolio. >> Yeah, I have a personal joy. Stu knows I'm fanatic about Kubernetes, and when I heard Ginni Rometty say Kubernetes twice in a CNBC interview you know it's made it. >> Yes. >> Kubernetes is a big part of cloud native containers, really now has created the connective tissue to make cloud and multi cloud viable. This is a key part of it. I want you to talk about the context of these trends and unpack this Cloud Private offering. Because it's instrumental in seems in the news. >> It is, it is. >> What is it about? >> It is, it really creates that ubiquitous layer I think that we've all been searching for. That next generation of virtualization and connective tissue as you call it. And as you begin to unpack that it really kind of starts with the rise of microservices and the need to be able to pack them very tightly into containers. That's really the birth of Kubernetes, was the ability to orchestrate those containers. So Kubernetes becomes that ubiquitous layer in there. But, IBM Cloud Private takes that and takes it to the next level, right. And, really what it is, it's the services on top of that, the cloud services which enable those containers to work together. And, it is a lot of open source capabilities such as Helm, Prometheus, Kibana and some of those core services that those microservices require in order to be able to run efficiently. >> So, Jason, we know it's a multicloud world. Everybody out there would love to say, oh yes, there's one cloud, I can simplify it. I'd like to get to a nice scalable model that's simple. But, the reality is customers choose lots of different solutions because they have different needs. The Private Cloud piece is not really well understood. I'd love you to take us inside your users. Because they say okay, I'm using Amazon, I'm using Microsoft Business Services. There are certain data things that Google has. IBM has AI and business productivity and database offerings. That Cloud Private, what are the services, what are the use cases, what are the reasons why I'm buying this and being part of my overall portfolio. >> Yeah, Ginni called it Cloud 2.0, right. 1.0 was about lifting shift, it was about cloud native, and that really got us about 20% of the way there. It's at 80%, that's the real challenge, that's really where the complication comes into play. That's really what Private Cloud is about. Because not everybody can be able to take their applications, throw them away, build cloud native, or lift and shift them. If you think of big regulated industries like banking, insurance, healthcare, government. They really need to be able to have that level of security and assurances that they need within there. And, that's really where private cloud comes into play, is those really tough, challenging problems in the industry. >> Yeah, I love that. A trend I've heard from a number of customers, you talk about them getting to containerization and multifactor services, is, step one is, I've got to modernize the platform-- >> Absolutely. >> Then I can modernize the applications on top it. Is that the trend you're seeing? >> Yeah, definitely. We've been building on microservices and modernization, it's a journey right, and it's a journey of discovery I think for a lot of clients out there. And, we'd all love to be able to say, OK this is my platform and now I'm going to work on the applications. But really, sometimes the starting point may be one or the another, and it usually comes in a space of a digital requirement, and so they begin to out modernize the application and then realize, jeez! I need to be able to manage all of this, I need to be able to deploy it all, and that's when the platform comes into play and all the other services, I should say, that come along with it. >> Stu, I think you coined the term Private Cloud. I think wasn't it? >> The true private cloud. >> True private cloud. So the private cloud, again, it's all cloud operations, so I kind of disagree on this whole point about one cloud or multi-cloud. Because I think, yes multi-cloud, but you see people use cloud for workloads, right? So pick the right cloud for the right application. So this basically says, okay, if you want to use Amazon, use Amazon if that's what you want, but if you are going to use 365, maybe use Azure. >> Yep. >> If you are going to use G Suite, use Google. You guys kind of have the business apps nailed down. >> Right. >> So If you're going to use your business apps, maybe IBM. This is your opportunity. >> This is our opportunity. >> Talk about specifically the kinds of apps that you guys will power with your cloud, because multi-cloud certainly makes sense for you guys. It's multi-cloud, you won't that portability and interoperability, but the apps that you're going to power with IBM Cloud. Talk about what they are, how-- >> Yeah, if you look at, from a language perspective over the last, jeez it's been 23 years I think, since the rise of Java, right? And 1995, when the first app servers came out. Those app servers, that is really where ore applications really run on top of. And, it's those core Java applications, that are now needing that facelift, right? They need to be able to be injected with new forms of AI, new types of integrations, new types of personalization of that digital transformation that's driving it, and that's really the core suite, right? And if I look at that core suite in there, and then what do you do to modernize a Java application, and what kind of tools are available to you. How do you then manage, how do you distribute, and how do you scale those applications. It's very important. >> What is the adoption of the private cloud or the Cloud Private product. >> Yeah. >> Talk about some of the trends, how is it being used, be specific on how customers are using it. What are some of the use cases? >> Yeah, so the primary use case is to increase the agility, lower cost on the overall managing of them. But it's the increase in the agility, which is really hard to measure. Because clients want to be able to react very fast to it. And so as they build up microservices, microservices then become independent with one another. You can then update ones, very quickly and easily. They manage and they run independently, and they scale independently, and so Cloud Private provides you with all those services to able to run those microservices as containers, but then be able to tie them together in a much more comprehensive enterprise suite. You know, a core technology like Helm, I'm waiting for Ginni to say that one on stage. But a core technology like Helm, really provides that robust, enterprise class distribution for scalability and high availability of a microservice based application. >> Jason, can you bring us inside the organization of the customers your selling to? It used to be, it was the refresh cycle. It's like OK, my X86 refresh, or you know, the budget cycles that I had. Cloud is quite a bit different. >> It is. >> Private Cloud is kind of straddling between the old world and the new world. What are the dynamics you're seeing as to who controls the purse strings? Are they moving faster to that opex model. >> You know, there's no one person who owns the purse strings on it, but it does float between the infrastructure team, knows that they need to do something different, the developers or the application development team, and really the strategy, the Chief Strategy Officer, in that IT organization is really where it's coming together. Because one thing I think that we've all learned is that developers will find the easiest, fastest way to do something. No matter what rules or policies we put down. And this is about providing them with an environment that has guardrails, for them to be able to innovate as fast as they want, use the tools that they want, that their most comfortable with. Really, it's a grass roots kind of movement into these microservices, led by the developers. But the purse strings are still held at the CTO side. >> That's always a fascinating interest, because the developers they're going to go do it, but they're not usually the ones with the budget. >> That's right. >> But when do the ops people get involved, the business people, to make sure that IT manages it, gets rid of like stealth IT? >> And a lot of clients have learned to listen to the developers, because the early days of cloud, they didn't, and developers found ways through it, no matter what. And so that's really what it's about. It's like a game of bumper cars, right? You got to make sure they stay within the ring of what's safe. And, especially in this day and age of the security requirements that are out there, it's more important today than ever before. >> Jason, can you share some data around some observations that you've noticed on trends around industry uptake or is there any patterns in terms of the customer base? Obviously, people aren't going to going to cloud operations. Just, Ginni mentioned 60/40, 80/20, the ratios. What does that all mean? And, just share the trend data around adoption and patterns? >> Probably the biggest onE in there, is the 80/20, right? That there's still 80% of the applications left in the world are still locked behind the brick and mortar. That's probably our biggest piece of our opportunity, and providing clients with a way to lift them up and be able to modernize them. I think is where the huge opportunity is. But then looking at where do they land, it's not all going to public cloud, right. So private cloud it's a huge business. I think a lot of us underestimated how large that business really is, and depending on the industry, you'll see 50/50, 60/40, 40/60 split, depending on the regulations within that industry, that country, the geography, of where they really want to go to. And, a lot of our clients are asking us for solutions around that private side, but yet be able to have the flexibility to be able to-- >> So you're seeing friction on the public cloud, mainly that's inherent from either regulatory compliance, or just technical challenges. Is that kind of the vibe? >> That's probably the first one. I think there's still that regulatory requirements of data residency, and how do I get my data to application. I can build all the applications I want in the cloud, but how do I get my data there? How do I synchronize it? My lineage of my data. So they really challenged her on that. But, then on the other side of it, is around the cost, right. And, if you wanted to rebuild all of your applications, as true cloud native, from scratch. It will take you a very long time and be very, very expensive. And so, there's also a cost element and speed. You can modernize something much more quickly, and be able to get it to that same level of service, without having to start over. >> We had Arvind on earlier, yesterday, and I want to get your thoughts on the impact of the Red Hat acquisition news, because if you look at what Open Shift is doing with Cloud Private. Arvind was saying yesterday that, Arvind Krishna, he's like, this is really enabling a lot of the acceleration for the modernization of the new cloud stuff, and keeping the legacy stuff and/or transition out on different timetables. Your thought on that? >> Absolutely right, Open Shift is going to be a critical component for our overall hybrid strategy. I'm very excited about it and really looking forward to it. And, Cloud Private and the services that I talked about, run in Open Shift today. That was part of our partnership agreement. I think that you guys were at, that Arvind talked about at that time. But, it provides the platform, for all of those traditional applications, which we've modernized. And the interesting thing is that we've actually modernized ourselves. We've modernized our middle-ware. We've modernized some of those products that are you know, 10, 20 years old. Everything from WebSphere, to MQ, to BPM. They've all been modernized in that same fashion. >> Yeah, Jason, speaking of modernization. Bring us inside you're sales force a little bit. How do they keep up, and what's the skill set that you're looking for, on your team to sell on this. You know, they need to understand Helm and Kubernetes, and all these microservice architecture, where five years ago, it was a totally different world. >> Absolutely, you know I think that if I look at a, it's not a skill, it's passion, right? It's that never give up type of mentality, I think that we look for, in a sales force and I never give up attitude really provides you with that foundation, for never stop learning, right. If anything that you've guys have noticed here over the last ten years in your guys' journey, is that this industry just changes so repidly, all the time. And, so as a sales force, you can't just acquire skills. You don't go out and hire skills. You hire people and you hire passion, and you hire people with that never give up attitude. I've been going around. We've been doing our sales kick-offs. I've done two out of the three now, so far. I tell you they are energized. They love it. They are energized about the Red Hat Acquisition. It shows that IBM really gets it. They've been telling me, does IBM really get it? And now they're like wow, we really do get it? And, they're really energized, because all of the pieces are falling into place, around this modernization, and clients, and we're hitting the timeing. >> It's time to hit that pedal to the metal, put the gas on-- >> They always say, there's no speed limit on sales. >> (laughs) Exactly. OK, first of all great, great conversation, and thanks for waiting out our journey. Stu, I would say that the salespeople got to watch all theCube videos, because all of the best content is coming out of theCube here, and great to have you on. But, quick plug, I'll give you the last word. What's the pitch, share the pitch for the Hybrid Cloud, what your team is offering? What's the, the core pitch for your customers, when you go to them? >> I think the core pitch is around modernization. It's the journey that clients are on, from application development, to how you build your apps, and how you build the microservices. How you integrate those applications, what's your API strategy, how do you move that data around securely, and then how do you manage all of those pieces together in that new modern world. And then, really looking your overall processes, and can you modernize your overall processes, add AI capabilities into that. So, it's that modernization journey. That's really what I talk to them about, and you don't have to do everything, right? Start small, start as a pinpointed piece, and we'll help you along that journey. And it becomes a journey of self-discovery, but we're there the whole way. We're a partner, that's really what it's about. >> Jason Gardner, Vice President of Worldwide Sales with Hybrid Cloud at IBM. TheCube, bringing all the data here, from IBM Think 2019. This is day three, of four days of coverage, here in Moscone live in San Francisco. We'll be right back with more, after this short break. (upbeat music)

>> Good morning, everyone. My name is Sudip Datta. I head up product management for Infrastructure Management and Analytics at CA Technologies. Today I am going to talk about the key pillars for modern analytics and monitoring for hybrid cloud. So before we get started, let's set the context. Let's take a stock of where we are today. Today in terms of digital business, software is driving business. Software is the backbone, is the driving force for most of the business services. Whether you are a financial institution or a hospitality service or a health care service or even a restaurant service pizza, you are front-ended by software. And therefore the user experience is of paramount importance. Just to give you some factoids. Eighty-three percent of U.S. consumers say that the brand that, the frontal software portal is more important than the product itself. And the companies are reciprocating by putting a lot of emphasis on user experience, as you see in the second factoid. The third factoid, it's even more interesting that 53% of the users of a mobile app actually abandon the app if the app doesn't load within a specified time. So we all understand now the importance of user experience in today's business. So what's happening to the infrastructure underneath that's hosting these applications? The infrastructure itself is evolving, right? How? First of all, as we all know there is a huge movement, a huge shift towards cloud. Customers are adopting cloud for reasons of economy, agility and efficiency. And whether you are running on cloud or on prem, the architecture itself is getting more and more dynamic. On the server side we hear about server-less computing. More and more enterprises are adopting containers, could be Dockers or other containers. And on the networking side we see an adoption of software-defined networking. The logical overlay on top of the physical underlay is abstracting the network. While we see a huge shift, a movement towards cloud, it is also true that customers are also retaining some of their assets on prem, and that's why we talk about hybrid cloud. Hybrid cloud is a reality, and it's going to be a reality for the foreseeable future. Take for example a bank that has its systems of engagement on public cloud, and systems of records on prem deeply nested within their DNC. So the transaction, the end-to-end transaction has to traverse multiple clouds. Similarly we talk to customers who run their production tier one application on prem, while tier two and tier three desktop applications run on public cloud. So that's the reality. Multi-cloud dynamic environment is a reality of today. While that's a reality, they pose a serious challenge for IT operations. What are the challenges? Because of multiple clouds, because of assets spanning multiple data centers, multiple clouds, there are blind spots getting created. IT ops is often blindsided on things that are happening on the other side of the firewall. And as a result what's happening is they're late to react, and often they react to problems much later than their customers find it, and that's an embarrassment. The other thing that's happening is because of the dynamic nature of the cloud, things are ephemeral, things are dynamic, things come and go, assets come and go, IT ops is often in the business of keeping pace with these changes. They are reacting to these changes. They are trying to keep pace with these changes, and silo'd tools are not the way to go. They are trying to keep up with these changes, but they are failing in doing so. And as a result we see poor user experience, low productivity, capacity problems and delayed time to market. Now what's the solution? What is the solution to all these problems? So what we are recommending is a four-pronged solution, what we represent as four pillars. The first pillar is about dynamic policy-based configuration and discovery. The second one is unification of the monitoring and analytics. The third one is contextual intelligence, and the fourth one is integration and collaboration. Let's go through them one by one. First of all, in terms of dynamic policy-based configuration, why is it important? I was talking to a VP of IT last week, and he commented that the time to deploy the monitoring for an application is longer than the time to deploy the application itself, and that's a shame. That's a real shame because in today's world application needs to be monitored straight out of the box. This is compounded by the fact that once you deploy the application, the application today is dynamic, as I said, the cloud assets are dynamic. The topology changes, and monitoring tools need to keep pace with that changing topology. So we need automated discovery. We need API driven discovery, and we need policy-based monitoring for large scale standardization. And last but not the least, the policies need to be based on dynamic baselines. The age, the era of static thresholds is long over because static thresholds lead to false alerts, resulting in higher opics for IT, and IT personnel absolutely, absolutely want to move away from it. Unified monitoring and analytics. This morning I stumbled upon a Lincoln white paper which said 20 tools you need for your hybrid monitoring, and I was absolutely dumbfounded. Twenty tools? I mean, that's a conversation non-starter. So how do we rationalize the tools, minimize the silos, and bring them under single pane of glass, or at least minimal panes for glass for monitoring? So IT admins can have a coherent view of servers, storage, network and applications through a single pane of glass? And why is that important? It's important because it results in lesser blame game. Because of silo'd tools what happens is admins are often fighting with each other, blaming each other. Server admins think that it's a storage problem. The storage admin thinks it's a database problem, and they are pointing to each other, right? So the tools, the management tools should be a point of collaboration, not a point of contention. Talking about blame game, one area that often gets ignored is the area of fault management and monitoring. Why is it important? And I will give a specific example. Let's say you have 100 VMs, and all those VMs become unreachable as a result of router being down. The root cause of the problem therefore are not the VMs, but the router. So instead of generating 101 alarms, the management tool needs to be smart enough to generate one single alarm. And that's why fault management and root cause analysis is of paramount importance. It suppresses unnecessary noise and results in lesser blaming. Contextual intelligence. Now when we talk about the cloud administrator, the cloud admin, the cloud admin in the past were living in the cocoon of their hybrid infrastructure. They were managing the hybrid infrastructure, but in today's world to have an end-to-end visibility of the digital chain, they need to integrate with application performance management tools, APM, as well as what lies underneath, which is the network, so that they have an end-to-end visibility of what's happening in the whole digital chain. But that's not all. They also need what we call is the context of the application. I will give you a specific example. For example, if the server runs out of memory when a lot of end users log into the system, or run out of capacity when a particular marketing promotion is running, then the context really is the business that leads to a saturation in IT. So what you need is to capture all the data, whether they come from logs, whether they come from alarms, capacity events as well as business events, into a single analytics platform and perform analytics on top of it. And then augment it with machine learning and pattern recognition capabilities so that it will not only perform root cause analysis for what happened in the past, but you're also able to anticipate, predict and prevent future problems. The fourth pillar is collaboration and integration. IT ops in today's world doesn't and shouldn't run in a silo. IT ops need to interact with dev ops. Within dev ops developers need to interact with QA. Storage admins need to collaborate with server admins, database admins and various other admins. So the tools need to encourage and provide a platform for collaboration. Similarly IT tools, IT management tools should not run standalone. They need to integrate with other tools. For example, if you want monitoring straight out of the box, the monitoring needs to integrate with provisioning processes. The monitoring downstream needs to integrate with ticketing systems. So integration with other tools, whether third party or custom developed, whatever it is, it's very, very important. Having said that, having laid what the solution should be, what the prescription should be, how is CA Technologies gearing up for it? In CA we have the industry's most comprehensive, the richest portfolio of infrastructure management tools, which is capable of managing all forms of infrastructure, traditional, private cloud, public cloud. Just to give you an example, in private cloud we support the traditional VMs as well as hyper converged infrastructure like Nutanix. We support Docker and other forms of containers. In public cloud we support the monitoring of infrastructure as a service, platform as a service, software as a service. We support all the popular clouds, AWS, Azure, Office 365 on Azure, as well as Salesforce.com. In terms of network, out net ops tools manage the latest and greatest SDN and SD-WAN, the VMware SDN, the open stack SDN, in terms of SD-WAN Cisco, Viptella. If you are a hybrid cloud customer, then you are no longer blindsided on things that are happening on the cloud side because we integrate with tools like Ixia. And once we monitor all these tools, we provide value on top of it. First of all, we monitor not only performance, but also packet, flow, all the net ops attributes. Then on top of that we provide predictive insights and learning. And because of our presence in the application performance management space, we integrate with APM to provide application to infrastructure correlation. Finally our monitoring is integrally linked with our operational intelligence platform. So in CA we have an operational intelligence platform built around CA Jarvis technology, which is based on open source technology, Elastic Logstash and Kibana, supplemented by Hadoop and Spark. And what we are doing is we are ingesting data from our monitoring tools into this data lake to provide value added insights and intelligence. When we talk about big data we talk about the three Vs, the variety, the volume and the velocity of data. But there is a fourth V that we often ignore. That's the veracity of the data, the truthfulness of data. CA being a leader in monitoring space, we have been in the business of collecting and monitoring data for ages, and what we are doing is we are ingesting these data into the platform and provided value added analytics on top of it. If you can read the slide, it's also an open framework we have the APIs from for ingesting data from third-party sources as well. For example, if you have your business data, your business sentiment data, and if you want to correlate that with IT metrics, how your IT is keeping up with your business cycles, you can do that as well. Now some of the applications that we are building, and this product is in beta as you see, are correlation between the various events, IT events and business events, network events and server events. Contextual log analytics. The operative word is contextual. There are a plethora of tools in the market that perform log analytics, but log analytics in the context of a problem when you really need it is of paramount importance. Predictive capacity analytics. Again, capacity analytics is not only about trending, right? It's about what if analysis. What will happen to your infrastructure? Or can your infrastructure sustain the pressure if your business grows by 2X, for example? That kind of what if analysis we should be able to do. And finally machine learning, we are working on it. Out of box machine learning algorithm to make sure that problems are not only corrected after the fact, but we can predict problems. We can prevent the problems in future. So for those who may be listening to this might be wondering where do we start? If you are already a CA customer, you are familiar with CA tools, but if you're not, what's the starting point? So I would recommend the starting point is CA Unified Infrastructure Manager, which is the market leading tool for hybrid cloud management. And it's not a hollow claim that we are making, right? It has been testified, it has been blessed by customers and analysts alike. And you can see it was voted the cloud monitoring software of the year 2016 by a third party. And here are some of the customer experiences. NMSP, they were able to achieve 15% productivity improvement as a result of adopting UIM. A healthcare provider, their meantime to repair, MTTR, went down by 40% as a result of UIM. And a telecom provider, they had a faster adoption to cloud as a result of UIM, the reason being UIM gave them for the first time a single pane of glass to manage their on prem and cloud environments, which has been a detriment for them for adopting cloud. And once they were able to achieve that, they were able to switch onto cloud much, much faster. Finally, the infrastructure management capabilities that I talked about is now being delivered as a turnkey solution, as a SAS solution, which we call digital experience insights. And I strongly, strongly encourage you to try UIM via CA digital experience insights, and here is the URL. You can go and sign up for the trial. With that, thank you.