Daily Stock Analysis - Datadog $DDOG SaaS Observability & Monitoring leader #stockanalysis #stocks

good morning guys today is February 14th it's about 9 o' Happy Valentine's Day hope everybody's doing well one of the companies that showed up on the radar is data do now before I go into datadog overall the market as I've been mentioning for maybe about six weeks now is a very resilient Market we were expecting a big correction in January yesterday the market went down 300 points but it's about 130 points up today so it's still a resilient Market lots of money on the side in October November December that is now being put into work given that a lot of people keep money in in safe movements when the stock market actually took a turn for the negative August through October so it's going to continue to be a strong Market I think for a little bit of time at least but valuations are stretched so be very careful if you are in for short-term trades go from a longterm investing standpoint look for companies that hopefully haven't run up as much there are not too many of those good companies that haven't run up up too much very rare so one of the companies that showed up on the radar is data dog again expensive company I think but I wanted to talk a little bit about observability overall what observability is what monitoring is what data dog does why they're good what are the biggest challenges with data dog most important of them being pricing they're one of the most expensive software as or service uh products to use as a customer so you get a lot of complaints from customers and cios about how expensive data dog has become uh including coinbase which publicly mentioned that they had an immense amount of money that he had SP on Dat Dog which was totally unexpected but let's go through data dog one step at a time and see what they do how they do it and why they do it now data dog has been around for quite a bit of time and the monitoring space has been around for quite a bit of time so what has changed is three important things number one devops has become a lot more important and as a culture among a lot of developer teams so instead of previously back in the '90s and the 2000s you'd have companies that have engineering teams or developer teams that build the product once they build the product then they'll give it over to the operations team which runs it in production so they will scale it they will manage it and so on and so forth so devops essentially means developers are also doing operations they're not only building the application but they're also running it in production the second big change was cloud cloud enabled devops a lot more easier now you could have infrastructure as code companies like ashik cor terraform for example as a product and also companies like chef and puppet essentially said you can describe in code what you want in your production environment previously if you remember it operations team would go to Dell go to HP would go to Compact and Sun Microsystems Etc or other companies buy servers figure out how to provision them and then get it up and running but Cloud companies like AWS and Azure essentially said you could tell us exactly what kind of an instance you want how how much memory how much CPU Etc we will get it up and running now you can describe this in code as a developer makes it a lot more easier infrastructure code just infrastructure as code essentially allows devops to be able to be more successful so devops is number one uh the second one is cloud and the third one is the ability to be able to triage and manage to observe within the application the applications have become distributed and a lot more complex than they were in the '90s and the 2000s so now that they've become a lot more complex you need to be able to have the ability to monitor trace and track log files to figure out exactly where the problem is while the application is actually having problems as opposed to when it's in production go from a user standpoint from a system standpoint and track and monitor those monitor those so that's the big difference between observability and monitoring those things have changed dramatically those three things have changed dramatically over the last 10 to 20 years that's why you'll find a lot more companies in the observability obs serve ability space there are lots of companies in the space I'll go through a little bit of who's doing what to a certain extent but data dog itself uh provides observability platform that allows you to do three most important things number one look at metrics so if you have an application and you want to be able to look at how much CPU is it using how much memory is it using or for example metrics within the application is it sending the iio a lot more frequently is it giving database calls more frequently uh what happens when specific operations happened as a CPU Spike that's the first part most applications also now put out logs so that is a tracking think of it like you're walking across or you're taking a marathon and every time you go past a one mile or two mile Landmark Milestone you say hey I've done this I've gone past the first mile Second Mile third mile that essentially gets put into log files and then you can look at log files to be able to observe what Behavior seems to be happen where's the program getting stuck where's the application getting taking more time that gives you the capability to be able to manage the application very very effectively and the last one is just tracing finding out how root causes of a problem by seeing where the application is moving across multiple different capabilities so in a lot of teams you'll find that they have individual teams that are responsible for different portions of the application or different capabilities for example in something like a in something like an Uber you will find that the front end team itself is split into multiple different teams as one team that is responsible for the mapping capability another team's responsibility is just the ETA third team might just be pricing now each of these teams has to interact with each other through the application and tracing allows you to be able to find out where it is getting stuck where problems might be and so on Now application observability as well as I mentioned monitoring tracing log files also have an important element number one you should be able to track them number one number two you should be able to visualize them to see where the problems might be and that's what application observability is systems observability on the other hand one of the big changes that has happened is a lot more people using uh kubernetes as a container technology as opposed to using virtual machines which have a lot more overhead which means you need an entire operating system to be able to get up and running instead kubernetes as a much smaller footprint allows you to be able to use applications with much less CPU and and usage of of memory and also storage if you will okay what the benefits of observability number one you can find out what the impact of your business is right now in real time uh and what that is a big Advantage for is companies that are on demand companies like door Dash like uber but also for companies like Netflix and more and more applications like e-commerce users are expecting the application to perform much faster and quicker that becomes a huge value to be able to manage those with observability second one is infrastructure is moving more from uh systems servers uh virtual machines to kubernetes and containers smaller footprint you need to be able to Monitor and manage that when you have traceability to be able to see within other applications without having access to that application team productivity improves so you can focus on your port and you can see hey this seems to be happening and I go tell the other team to take care of that troubleshooting again managing it through traces managing through log files helps you get to the root cause of a problem much quicker uh most users will face the problem first and because of that you have to now triage as a developer or as a monitoring person to figure out where the problem might be so user experience becomes the most important metric that a lot of people are measured against which is is the user facing issues is the because a lot of times you would see back into the 2000s I remember this um users would face problems we wouldn't know about it only time we'd know about it is because someone would open a ticket and then we'd have to go in and say okay users are facing a problem but everything in my dashboard looks green so that's the red on the front end green on the back end that's the issue that you need to resolve so monitoring and managing from a user perspective gives you the ability to be able to trige very quickly and then application performance itself performance and availability are the two most important things availability being number one application should be up and second it should perform fast those are the two most important things for applications overall in terms of management so application performance monitoring is the ability to look at the application and say is it doing well enough and then visibility into the entire sectum now the complexity in terms of the systems themselves because of user expectations have become significantly more important now the number of technologies that a single stack would use maybe 20 years ago when you had standardized non-rem Solutions versus what is right now open source and a lot of has lots of Open Source and has a typical application in the 2000s when I remember uh when I was at Cisco or when I was at at U HP and Mercury interactive us used to be about 20 to 30 the build materials would be 20 30 different um open source Solutions or or different pieces of software that you would build and end user application with now that is 200 on a minimum in a lot of cases it's much much more because these are complex applications which have been split into multiple each individual components that need to talk to another second the second portion of it is the Computing units themselves we've just gone from somewhere in the last 10 years the amount of compute that has been required has gone up 10 times uh just dramatically as we go into more serverless and microservices architecture which is break down the basic components provided by our API and interfaces to other systems it becomes a lot more Dynamic and that makes it a lot more scalable but at the same time increases complexity frequency of releases I used to have releases once a quarter maybe once a year at best now it is literally every day there are multiple releases Netflix Uber uh and other companies that are not Cloud first companies as well including Walmart and Pepsi they also release multiple times a day because they have to be able to keep having multiple streams of developers working on different portions optimizing their portion of the application makes it more complex and when you look at previously it used to be just remember I told you monitoring was an operations specific issue once you put to development put into operations and say hey you guys run it now developer and operations devops together are working to be able to and in some cases it's just a developer organization which is becoming an office organization then business teams are coming back and saying can we manage and monitor this application and finally secure the applications because you need to make sure that whatever you're building from an application perspective is secure so all of these makes the applications a lot more complex which means observ ability observe ability keep to be messing this up many times becomes a lot more important so what data dog does is it helps you be able to monitor 600 different types and and observe 600 different types of application specific units they manage log files they manage traces they manage metrics you can take all of these together and be able to put them in dashboards manage them understand and get visibility about them put workflow so that if this happens and then that happens do the other thing then Telemetry which isin the application put specific things that allow you to be able to send data back now the use cases for that typically can be infrastructure monitoring or application performance monitoring all the way down to Cloud management and so on so you'll get the a this in a single visibility and that gives multiple different teams so another way to look at it may be top down at the top you have users just below that you have the datab visibility platform developers get a different visibility view which is more of a metrics log files and traces view the it Ops organization might get only a metric only view the security organization will be a security only specific view now these could be uh and rum is real user monitor by the way if you see this over here uh Cloud seam is what the security teams use below that they provide dashbo wordss they provide agents to be able to restart which is called um management and also actuation if something goes wrong fix it that's actuation that's what agents do and then they collect the data from multiple different places okay observability overall has multiple different areas and there's a very competitive set this is a small set there's probably close to one another 100 vendors I could easily put in this area the way to think about it is Data dog allows you to be able to do infrastructure applications and log f management observability overall and then there are specific vendors they actually compete with in all of these different areas um din Trace which you can see over here let me just draw that and Pen okay dat Trace is the number one competitor you're going to see with data dog all along uh they're very very good competitor they've been around for a long time so dat Trace was known as compy beare I used to compete with them back about 20 years ago or maybe yeah 20 years ago and um there are other companies like Dynamics which is a Cisco company now New Relic so you see din trace and New Relic to to be the number one and number two competitor for data dog overall and then within specific areas for example log management Sumo logic and Splunk do compete with them each of the cloud vendors themselves have a solution for being able to manage logs and uh infrastructure within the cloud and then there are other third- party Solutions open source which are doing very well grafana and kibana are actually doing very very well from an open source perspective now this is the Legacy things these are the four companies that are the Legacy companies uh actually five six there's one more missing HP HP makes open view so BMC HP open view Microsoft sorry TI were the three along with CA which is now broadcom so CA unicenter BMC Patrol uh IBM TI and HP is missing from your open view were the four big Solutions in the 1990s ' 80s and '90s Microsoft had system center so they used to be another provider there just for on premises now as you move to the cloud a lot more of these companies came about and the cloud vendors themselves started giving a lot more solutions so that's just for the infrastructure monitoring part then the application monitoring side you still have HP that competes so surprising that HP doesn't show up in this one and then uh this is by the way by public clums this is good website you should go check it out and then a lot more companies on the elastic management side sorry elastic such as on the log management side so data doog is a platform for provides the entire Spectrum infrastructure monitoring application monitoring log monitoring and management managing from a user which digital experience management security related Solutions which is relatively new about two years ago and then managing visibility continuous integration and continuous testing um and they provide an AI based solution as well to be able to get insights and anomalies and then they can actually manage across multiple set of Integrations that they offer what's good about them the the transformation that lot of companies are doing to digital transformation is what they call it at the CEO level or in the CIO level is the move to be able to make the money to make the company a lot more service oriented recurring Revenue based even larger companies are trying to be able to do this right now and then migrating to the cloud which gives you the ability to be able scale more quickly make capex expenditure a lot more Opex oriented the next generation of cloud and devops is being built a lot more different use cases or what Dat Dog is building for and going beyond the observ availability Market as well even now it's still a $60 billion Market the cloud spend itself is about a trillion is what they're expecting it to be right now it's about let's see somewhere about 500 billion to about a trillion is where they're right now so if you look at Amazon is own is about 100 billion Microsoft is about 70 75 billion and Google is about 30 billion just those three together make about 200 billion and the rest of the market is another 50 to 60% so that makes it roughly about 500 billion market and then you go above and beyond that um if you look at it from a 2027 perspective it's almost expected to be a$ 20 trillion Market growing at 16% overall uh as a percentage of the total spend uh it's about 20% uh will be 20% if you will so Cloud spending is growing up rapidly which means the amount of money that you need to spend to observe your applications also goes up PR well the observability market itself is about 62 billion 2026 right now it's about 50 billion or so so the most important observability part of this Market is not only how fast it's growing but what are the other areas that data dog could go into obviously a lot of what they tend to do tends to be in areas that people are using Here and Now for example Cloud monitoring which is a big important area for a lot of companies uh kubernetes and container monitoring application monitoring real user monitoring things like that and observability more from a tracing and log files and seeing what is it that they can look for the key metrics and pull those up now they've not only Built their own application I'll show you what their history has been in terms of just building uh from the 2007 2012 time frame but they've also acquired a lot of companies for log management U visibility from a continuous integration CI stands for continuous integration standpoint say they good job what um you know roughly about 5 to 10 Acquisitions over the last uh seven years but consistently making it so that they can address different areas of observability that they don't have so um they have introduced these new products over time this is what I'm talking about 2007 was when they got started from then they've had a platform that they've built for data management infrastructure monitoring was introduced application performance monitoring they acquired log management and then they've kept on going by including new applications and new products onp of the platform so this is their view of the products from a when they were introduced standpoint so they did a big amount of they took a lot of time to be able to get this portion right Cloud monitoring first monitoring container monitoring this takes a lot of time to be able to get right and then since then they've just done an excellent job of building new products and most customers buy two to six products and a few of them even buy um more than six products from data do so each of these individual products is priced separately so about 80% of the customers they using two products okay and about um 40% of their customers using four products and about 15% so they have a lot of room existing within their customer base as well to be able to move so where are the growth levels growth levs introduce new products besides metrics traces and logs that's another portion of it that's first portion of it grow internationally which is the second portion of it the third portion of it is get their existing customers to buy more products from them which means they're committing more to it what they've done so far is increased pricing just to give you a context look at their pricing they are in some cases and this is just one or two examples that I will show you New Relic and din tray are almost a fifth less expensive for small companies and about a third less expensive for larger engineering teams data dog is expensive you will get this question if you go and search and do a simple search on what is good about data dog or what are the pros and cons of data dog and the number of thing one thing you'll get from con side is just pricing data dog is expensive couple of quarters ago coinbase who was one of the customers that mentioned that they suddenly got a bill from data dog that they didn't even expect they didn't mention the name of the company neither did they mention that they got it from data dog but uh everybody put two and two together and said data dog is the company they went from rough $100,000 to million dollars or more or much more actually in one quarter that that became Big Challenge okay what can you do to be able to help manage from a data dog perspective they discover a problem monitor the health of the system and then reduce the resolution those are the three most important things when you have to resolve issues you proactively monitor to make sure that you know about the issues before they happen number one number two is to make sure that you can consistently track to find out where the problem exists and then resolve the problem number three and data do helps you do that by being able to fix that now how do you identify issues both from front end and backend applications you have to be able to monitor it consistently end to endend applications or digital experience which is this infrastructure side you manage all of these together and you track the logs helps you be able to get a much better view on what's Happening underground okay now Cloud security is a new area that data dog is getting into as you can see a lot of these companies are trying to get into other Tams they're trying to improve the Tam increase their total addressable Market by going into a Jason areas now the same companies that are developers are using for observability are saying hey can you help us with security log files as well so more developers come in they're making a few more mistakes from a security standpoint and as they're connecting the cloud becomes a little bit less secure and as each of these terms becomes very specialized they're also finding that it is a lot more easier to be able to have data dog be the platform that helps them get all this data lot more teams involved in Cloud security than before which is why you see there are hundreds of companies just in the cloud security thousands I fact and S Ms seems uh which is infrastructure monitoring and and and management that is being done by Sumo logic by Splunk Splunk is the leader Sumo logic is probably a maybe not a number two but very close data dog wants to get into that market as well now data dog security products provide both observability and security monitoring from a what you used to do from infrastructure aiting you can do from cloud security all of these different Cloud products whether that's power networks individual products such as end user or endpoint monitoring and network monitoring all of these throw out specific logs or they throw out specific alerts those can all be managed within data dog itself application security not from just a performance standpoint but also security standpoint finally manage it from a law so data data dog sols for this this complexity that exists because of so many different applications with so many different systems that all need to be integrated into a single place what if something was an issue with Azure going down or what if there is a log file within the Java uh runtime that you created that creates an issue now both Gartner and Forester have named data dog and din Trace among the leaders category neic as well but not so much so given how struggling how they've been struggling data dog though has just been killing it in terms of their revenue and growth now data dog is mentioned as a leader in the Gartner magic quadrant along with din phrase look at Din phrase a little bit higher and up to the right and the second one is Forester they've also said that AI introduction of AI into iops there's a leader also and D din TR and Dat Dog are the two leaders in this space the other ones are coming up a little bit but that tells you how competitive the market is and D face is a little bit of a better bued company because of its pricing being so effective they have better products in some cases not all but Dat Dog is just used in so the differences between din trace and data dog tends to be mostly in terms of their customer segments lot lot of companies don't use both some of them do not a lot of companies do but Cloud native developer friendly companies start with data dog din Trace is used more by the Ops teams that have said hey we used dat Trace all along can you now use this for developer related C so that has resulted in a phenomenal growth for day to do from about $100 million we go through the details and the fundamentals to $2 billion in 2023 just look at that it's 20 times growth this is a phenomenally fast growing company 66% kager uh and quarterly Revenue has been just consistently up and to the right I don't see anything that goes down in this one so far they have about 25,000 customers now uh and now their estimate is a little more than that 26,800 customers they've grown very very well quarter over quarter they're adding customers they moved a little bit down in the Q2 time frame but overall moved very very well uh million customers with a million dollars spending more a million dollars is about 315 320 something like that and then customers moving with more spending more than $100,000 is about nearly 3,000 customers so that has been very good growth this is what drives the growth in Revenue customer comes in starts using one product spends about 20 to $30,000 and slowly moves up the food chain to spend more than a million bucks okay the summary picture two billion in Revenue growing at 30% this is one of the faster growing companies snowflake data dog very fast growing Sentinel one crowd strike are the fast growing companies in the SAS space don't look at palent and companies like that they're looking at growing at 20% they're not even close to where data dog is volunteer is not even close and for all practical purposes data dog is much better company in terms of growth they do have the challenges uh but you know much better company uh Gap operating margin is about 20 % free cash flow is about 25% 120% net retention rate very strong movement there as well about 30,000 or so customers right now growing to their their total segment based about 300,000 customers and over 3,000 customers spending over $100,000 on them on an annual recurring basis um they have over 20 different products all of them within the simple data do Platform you can use it for security monitoring you can use it for application monitoring you can use for log files you can use for observably all of those on an ongoing basis about 5,000 people in the that's data do just as a background to give you a little bit of context now what we're going to do is then go through our five step process to figure out what we like about this company what we don't like about this company and then see where to go from there okay we've already seen a little bit of this data so you're not going to find that first part very very difficult to understand balance sheet I'm sorry income statement annualize 100 million 2017 2 billion 20 times growth this is just phenomenal look at this 100% growth before before slowly slowed down picked up back again now about half of the previous growth 30% growth last year still growing at 30% gross profit very strong in the 70% range and finally they turned a profit that income became profitable in 2023 they will continue to do that is my hope okay on a quarterly basis growing down to about 25% growth right now and operable for two quarters in a row so they're probably going to end up a little bit better than where they were before so this is going to be expensive stock $44 billion for $2 billion us just for people who will complain to me all the time and say palent is a much better or bigger company just to give you context so this is 2 billion dollars growing at 25% and palent is maybe half the size um doing about a billion something I think and they're about 53 more this is richer valued so they're doing about the same okay so they're doing about the same growing at 20% was it 25% they 53 billion versus look data dog growing faster bigger Market well technically can say um penter has a very large addressable Market still less expensive than P so um banteer versus datg is not a good comparison din Trace versus datg is good comparison most part din Trace is less expensive but not growing as fast as data dog is a very good executing company okay now balance sheet wise um about $3 billion in cash two three billion actually you got a lot of current assets and debt of about almost a billion dollars manageable and a cash flow of 200 million yep that's good as well this is going to be the challenge so they're doing thousand PR is a thousand that's ridiculously high and price to sales is 21 that's also ridiculously high for a company growing at 25% both of these numbers are very high yes their earnings are growing very quickly but very very rich 80% gross margins 10% net margins it's not bad at all they need to improve this net margin to at least a 20% range 80% gross margins and you're still doing only 10% so they're moving it in the right direction you need to be at 20 30% net margins for you on a scale of 1 to 10 I'll summarize this as a company that is roughly in the six to seven range valuation is a big challenge th PE is just ridiculous and the profitability is not significantly there yet to summarize data dog from an overall technology perspective observability is a strong Market growing fast and large Market 60 billion right now the other thing that data dog has done very well is expand their time their total addressable Market from just observability and monitoring to also security now which makes it a lot bigger the third thing about Dat Dog is they've grown both organically and also through acquisition they've done fire acquisition so far smaller ones to be able to expand the footprint of products that they have they have 20 products in a single Suite across the platform and they're able to grow they have more than 3,100 customers spending more than 100K and about 150 customers are so spending more than a million dollars annually so good strong Market products that are used by developers Who start using it first and then slowly they expand biggest negative they're very expensive and also once you get data dog in it's very hard to take it away uh from a customer perspective that may be negative but I like it as an investor okay so what does it do from a monthly uh on a monthly basis buil the base stage one stage two markup stage three and then a decline stage 4 good consolidation period this was a consolidation period in 2021 hug huge mark up in 2022 consolidation very little time big mark down hit the $60 Mark and his double cents so this right now on the long-term basis is a higher highs higher lows that's where it is higher highs higher lows so this is on an uptrend markup phase let's go to the weekly chart to see what we can see with anything different so this comes up so there's quite a lot of resistance for this in multiple different places given how much the stock had run up before so you're going to find quite a bit of decision in the 150 mark But even right now the stock is ridiculously expensive so it will do well 180 is a price Target by most analyst it will do well but man this is expensive so if you get a little bit of a sneeze in the market and people realize that things are very expensive this is going to go just like what it did from 199 Mark all the way to 560 this is a 70% markdown this might even go down um but not right now they're still doing very well growing very fast the options for getting good quality companies that are growing fast with strong balance sheets and consistent growth is so um so very nice markup after this phase so on The Daily on the weekly chart going to find some resistance in the 150 Mark and look at how often it has touched this 111 Mark seems to be quite often so and also in this Zone you're going to find 114 to 111 is a good zone for it to be able to get bounc off of and that also represents the 20-day moving average on the weekly not 20day 20 moving average on the weekly now let's go to the daily chart it's been hitting 52- week highs consistently just touch the the 20 so look at this it it does give you a chance when it gets down to the 200 day moving average it gave you one in November that was in the 80 Mark and then it's doubled almost since not doubled about 60% high so good move by data dog I still think it is expensive iive you look at the valuations uh just show you that again just to give you a little bit of context go back to statistics look at the valuation this was 13 times and it's gone to 21 times 12 times 12 13 times yeah 13 times to 20 times I don't know what it done it hasn't done that much to deserve such a huge markup in valuation but every company has gone this way uh okay so from a daily chart perspective moved up going broken through a lot of resistance so you go back to the weekly chart just just to show you a little bit of context it had to break through a lot of resistances Hit the low 63 broke through this point of resistance this was a big resistance point in the 87 Zone broke through this resistance as well 113 so this now becomes a new support it flips over the resistance becomes a support so it should get good support for it in this Zone 114 to 200 and let me move this a little bit lower 114 Zone all the way to the 111 Zone somewhere over here uh would be a good resistance the way to try actually would be like this this is the Zone if it gets here buy it uh it'll move up to 150 180 fairly easily if the market gets even worse you might want to wait for somewhere the lower Zone which is the 102 Zone but it's not going to go much lower than that right now though Market is very strong I would easily expect this to get to the 150 Mark in the next few before the next earnings they just had very good earnings that they declared uh immedi you know the street immediately sold off after that but they figured out okay okay this was not a bad mad bad earnings and then moved right back up again uh this seems to show this candle today not today is it today yeah seems little bit of resistance it wants to move a little bit lower so expect it to at least get to the 129 Mark which would be the 20-day moving average for a trade but realistically if you want to buy for the long term this is a good stock to buy I would buy it in the 111 zone or even sometimes if the market is really poor you could get in the 100 Zone thank you very much for watching let me know what you think and

2024-02-18 19:27

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