Orchestrate all the Things - Up and to the right: TigerGraph scores $105 million Series C funding, the Graph market is growing. Featuring TigerGraph CEO Yu Xu, COO Todd Blaschka

Episode Date: February 17, 2021

The largest funding round to date in the graph market is good news not just for TigerGraph, but for the market at large. We review TigerGraph's progress and the market landscape with TigerGraph CEO Yu Xu and COO Todd Blaschka Article published on ZDNet

Welcome to the Orchestrate All The Things podcast. I'm George Amatiotis and we'll be connecting the dots together. Up and to the right. Tiger Graph scores 105 million series C funding. The graph market is growing. The largest funding round to date in the graph market is good news, not just for Tiger Graph, but for the market at large. We review Tiger Graph's progress and the market landscape

with Tiger Graph CEO Yu Xu and COO Todd Blaska. I hope you will enjoy the podcast. If you like my work, you can follow Link Data Orchestration on Twitter, LinkedIn, and Facebook. It's been a while since we connected, so I thought a good way to start the conversation would actually be to recap the last year. But actually even before we do that, even though we do know

each other, since it's the first time that we're recording this and for my podcast it may be an even better idea if you start by saying a few words about you and your role in Tigergraph and your background as a kind of introduction. Okay, sure. Hi George, thanks for having me and Todd to join your podcast. My name is Yuxiu, I'm the co-founder and the CEO of TigerGraph. My background was in database and distributed system. We started the company in late 2012, so it's about eight years by now. Thank you. Thank you, Kim. And Todd? Hi, George. Good to see you again. I'm Todd Blaschka, the Chief Operating Officer of the company. I've been with the company for about three and a half years. My background has been bringing infrastructure technologies to market. I've been with other

scale-out database infrastructure and also NoSQL systems for over the last 20 years. Okay, great. Thank you. Thank you both for the introduction. And so just for the benefit of the people who may be listening to the conversation, the occasion today is you're about to announce your Round C funding with quite a substantial amount, actually. And I realize that it's been about a year since the last time I connected with someone from Tiger Graph. And last time we connected, I believe the occasion was that you were announcing some analytics offering, which was precisely oriented to COVID-related analytics.

So it's been a year, unfortunately. And we're still kind of stuck there. But since it's been a year, I thought it would be a good idea to start by kind of recapping the year. So how has it been for you? I mean, besides the obvious, you know, everything that every one of us is going through, how has it been for you business-wise, let's say, and what led to today's announcement and the new funding? Yeah, 2020 was a very unusual year for everyone. It's the same for Telegraph. In the beginning of the pandemic, in 2020, nobody knew what's going to be the future.

So a lot of people, a lot of company, were really worried about the future. So especially for the enterprise companies, we heavily rely on social meetings, on-site POCs, workshops. So in the first half of the year, our customer impacted. So as a result, the telegraph business was also impacted to some degree. But then people adapted to the reality.

People really realized that they have to work from home, still contribute. So I think later year, people come back to business and everything kind of go back to normal in some way. So our business actually come back much stronger than ever, actually, because a lot of companies need the digital transformation. They need to give customers the end-to-end, the customer journey, customer 360 type of experience.

So they need the new technology, and the graph is one of the best technology to help customers to connect the dots and give them more insights about the customer, the product, some of the really high-level, high-value business problems. So actually we had the best quarters in the second year in our history, and we see the momentum is still going really strong.

So Todd can share more about the details. One example is that we were planning to have the first open industry Graph Plus AI conference. We had high hopes to make it a huge conference to social with customers, with peers, with practitioners from all over the world. But because of the pandemic, it had to be virtual, but still we made it really successful. Yeah, what I would just add to that is from an industry standpoint, the pandemic has really spurred innovation across all companies, whether they're large, whether they're small, about how to adapt. And I think that's part of the resilience, but also the innovation in general as people. We've dealt with it. We've

dealt with a pandemic. We're dealing with the pandemic. And the way we do business has changed. We need to adapt and stay relevant. And if we don't adapt, we're going to be losing out. And so what we're seeing is companies accelerating their innovation across data, across data analytics. We're hearing companies talk about their internal analytics teams being in essence, startups within their state enterprise. And they're looking at their businesses in a different light because of the pandemic. And that I think has really poised companies to really look at how do I find new insights on my data and be able to make better decisions because I need to reach people in a different way than I have before.

And how do I break through from that competitive situation? So I think we've seen that. I think a lot of the other companies in the infrastructure and in the data space have seen that as well as over the last year. Yeah, I think you're right. And this is something I've been getting from many people I have conversations with during this time. And yeah, in a way, people like yourself or like me, they are kind of lucky, let's say, because amidst this whole chaos and mayhem

we're still able to work remotely pretty much in the same way as we were before. And for some of us, even this whole acceleration that you kind of sketched out has even helped for business, which I guess kind of brings us to the occasion today, so your funding. And obviously this is something that's also shared by the people who decided to back TigerGraph. And so I wonder if you could say a few words about the details around the funding, basically, so the amount, who the VCs that are backing you are, and if you have any kind of background that you can share on how this

came about, and what brings our total funding to a little over $170 million. So this round actually is the largest funding round for the graph database category. I think this really means a lot to the whole graph space. So it's really a recognition of the rapid growth of the graph space. And the graph database, just like any other database, is horizontal. So if you look at the Databricks, Snowflake, MongoDB, they see accelerated growth in their later stages because

it's like running a snowball, right? The more momentum you have, the faster, the bigger you can grow. It's the same thing for TigerGraph. We got out of SaaS mode in early 2018. That's when we started to build our sales marketing team. That's when we started to build our sales marketing team. We started to actually do commercialization in the U.S. and in Europe and other countries. In the last two and a half years, we proved Telegraph is the only distributed, scalable, high-performance graph database by having the best customers across industries. So I think we have so many lighthouse customers across industries. So I think we have so many Lighthouse customers across industries. I think that's why the investing community

realized graph actually could be a huge category, right? Like I said, database system before, like I said, graph database system before, could not deliver the promise of graph analytics. But TigerGraph made a breakthrough. We made the distributed graph database system before could not deliver the promise of graph analytics. But TigerGraph made a breakthrough. We made distributed graph database work. We made a high-performance graph analytics work. Now we are helping a lot of customers to improve the world, right? Whether it's in the healthcare to improve healthcare patient care quality or reduce the cost, or it's helping the power grid company to reduce the outage, right?

So helping bigger banks to reduce the customer's fraud, right? So I think people are realizing graph is really, really the best technology to solve a lot of really hard problems which are previously unsolvable right we have a customers like a Jaguar Nandarola to reduce the key supply chain planning down from more than three weeks to only 45 minutes using Tegra graph so I think that that's why we are able to raise a huge amount of funding because people see the huge potential of graph analytics.

It's not just the legacy graph database space before, right? It's much bigger. And also a lot of people saw the potential of AI and machine learning powered by graph, right? So if you look at our Graph Plus AI Open Industry Conference last year, you saw bigger customers like Intuit, GenGran, Land Rover, AT&T, they talk about how they use graph, how they use graph to power knowledge graph, how they

use graph to do customer clustering. Those are all actually unsupervised type of machine learning applications. So I think that in the past, nobody figured out how to have a high-performance graph to support new generations for machine learning and AI applications. Now with the TegraGraph, it's possible. We saw big telecom companies, big bank customers are using TegraGraph to integrate with their machine learning system. So, and they get a lot of return on the investment on the graph database.

So we are bringing this to the cloud. So, you know, any other company, whether they're big or small, you know, they will have the capability to use graph to power the new machine learning AI and applications. So I think that's very exciting. And everybody, of course, knows machine learning and AI are fundamentally transforming a lot of industries.

So by combining graph, it's going to be even better for the businesses. So I think we need to do more, but it hasn't always been on the roadmap. With the new funding, we'll be able to deliver more features and more quickly. One thing I would just add to what you were sharing there is what we are also seeing and where our lead investor, Tiger Global, has also a shared vision is Tiger Graph is addressing a market that has been trying to answer business questions from a business logic side within their data for over 40 years. It is, it has been an ongoing problem. And that's where we've seen innovation over the last 40 years of different data stores, different approaches. And what, what, what, what, what their shared vision is, is graph is the most logical, the most natural way to help solve and solve business logic

questions in real data, in real time. And with the technology that we have, it creates that foundation to allow customers to be able to do that. And so as Yuxin mentioned, part of the innovation is to help make this even easier to use, to make it available so it's not just a data science application,

which is really where Graph has been, but this is really an analytics application that many other users are receiving value through low code, no code features that are bringing in so customers can get the new insight in a very fast, very consumable way, in an easy way. Yeah, and having had the opportunity consumable way in an easy way. Yeah. And having had the opportunity to familiarize myself with what you do,

basically kind of from the beginning, well, when you exited Stealth, basically, I've had the chance to also see Tiger Graph grow as a company and as a product as well. So adding features along the way. And it seems to me that, unless I've missed something, and you know, feel free to jump in if I have. So it seems to me like the last year for you has been mostly about what you just described. So not so much adding new features. So you already had a cloud offering self-managed, and I think one of the things that you want to do with the funding

as mentioned in the press release is to expand that to Google Cloud. So it's basically operationalizing, I would say, what you already have, not so much adding new features. Am I correct in saying that? In some way, yeah. But we also want to do more. For example, I mentioned that the machine learning AI, we already have about 20 plus graph algorithms to support the machine learning type application. With the new funding, we're going to add definitely more machine learning capability, AI capability inside the TegraGraph out of the box.

Todd mentioned we want to make it easy to use, faster to use for data scientists and machine learning engineers. So, but that's just only inside the TegraGraph. Now we are in the cloud, we have more customer migrating to our cloud. We also want to integrate with Google, GCP, Amazon, Microsoft Azure frameworks for machine learning and AI.

So once in the cloud, we want to give customers the best end-to-end experience, right? By integrating with other systems they are already familiar with. So that's because the TegraGraph bring unique values, so that's even better for customer, right? So they can go to TegraGraph, do a lot more than just using TigerGraph alone.

And then the other new thing we want to do is the graph visualization and the graph BI. You probably remember we, you know, had an innovation for UI. We have our own product called the Graph Studio. We have customers really love our Graph Studio and they use them in production, right? For their fraud investigation, for their customer journey type of application. But actually last year we released a better version of a query, a visual query builder.

Think about, you know, us top-low for graph database, right? So last year it's just the beginning. We have a lot of more exciting functionalities, features we want to bring to our visual query builder. You look at the top-low, right? So take them many, many years to build a really wonderful BI product for their customer, right?

So it's going to be also a long journey for Graph BI, but we have the best database to power the interactive, visual, real-time analytics for end users. So we have advantages here. It's a lot of innovation to bring to the table for the Graph BI because it's new, because there was no really scalable Graph B4, right?

So it's harder to do applications on top of Graph database. Now it's possible with the Tegra Graph, so you will see more innovation happening in the Graph BI space, right? So the RDBMS visualization or BI market is huge, right? I think that's going to happen also to the graph BI space. And also, one thing we want to invest heavily, of course, in the cloud.

Google GCP is just part of it, right? So we already running on Microsoft Azure and Amazon AWS, but there's a lot of new things, Microsoft Azure and Amazon AWS. But there's a lot of new things, new innovation we can bring to cloud. We are building a lot of cloud native features, a lot of cloud-first features for customers, which are not available or even possible for on-prem, right? So there's a lot of innovation still needed to happen.

But even for the distributed graph database, even we won the battle over the last three years, people recognize we are the best choice for high-performance, scalable graph database, but we still have a lot of new ideas to bring to the database. We still have, we want to make it more scalable, you know, highly, you know, functional, add more new features. So we are working with some of the best customers,

you know, to meet their ever-changing demand for their business problems, right? So the last thing I also want to say is that throughout the years, we saw a lot of data points about how people use the graph, right? For example, we saw a lot of data points about how people use graph, right? For example, we saw a lot of anti-money lingerie across the globe. We saw customer journey applications across industries. We saw supply chain, right?

So I think we see enough data points for some applications. TechoGraph is going to build some templates. It's kind of a turnkey. It's never going to be turnkey, but our goal is to try to let the business users to immediately see the value of using TegraGraph. They don't have to build application, build a graph schema, build a query logic all from scratch.

We're going to share the UI. we're going to share the graph schema, you know, the business logic with our customers, with our partners. So our end customer can get the value more quickly than before. So it's already happening with some customers. We are working with our partners. So we are kind of sending a whole package solution, right? It's not just a database, unknown. So by doing this, we're building ecosystem, right?

So our customer can get a manual more quickly and we reach to more users, not just a DBAs or data scientists anymore, right? And the users, they can immediately use the UI, you know, drag and drop, double click, you know, they can already do a lot of investigation, get a lot of insights from that data. So I think there's a lot of things we want to do. It has always been on roadmap, but now with this funding, we are definitely speeding up the Thank you. Yeah, I was going to say precisely what you concluded with basically

that having seen Tiger Graph's course over the years, it's quite apparent to me that you're moving up the stack basically. So you started with a core database offering, and then you're adding layers as you go. Another question, another topic that I wanted to explore had to do with use cases, basically. And you already mentioned quite a few of them, such as fraud, anti-money laundering, or supply chain, or customer journey analysis, and so on. And I wanted to tie this into something else.

So when you first started out, that was 2017. That's when you exited stealth, at least. Graph was not as mainstream as it is now, at least in terms of, well, analyst attention and recognition let's say so by now and i'd like to to think that that maybe helped a little bit in doing as well but you know by now the gardeners and the foresters of the world are you know all about graph it's it's a very hot topic and so on so i wanted to ask you well kind of two things one, whether you're seeing this kind of attention actually translating in sales growth and deals. And that's one part of the question. And the second part of

the question is whether you're seeing more use cases or maybe different types of use cases, because, well, those use cases that we refer to so far, they're kind of typical in a way. And I think by now everyone who's into graph kind of knows them already. So I was wondering if there's any kind of variation. If you're seeing use cases and another thing that these use cases share, in my opinion, is that they're mostly analytical. So I'm wondering, and precisely because you emphasize performance for your product, whether you actually see people using TigerCraft for operational use cases? Yeah. Great question. I'll start and then you feel, please, please add on. I think the point that you bring up, George, is there is a lot of nuance. What we are seeing is where there's data, there can be graph.

And we see that as complementing. So where you look at the modern system where there's Snowflake, there's Confluent, there's Databricks, graph analytics, being able to run analytics on the connected data becomes a natural fourth pillar of a modern system. And the industry as a whole is growing up and is maturing. Your support, customer support, I think there's this overall wave because there still is that basic question of how can I get business answers on my real data in real time? And that I think ties into

your operational analytics question. And what we are seeing is customers, and these are across, because when you get into customer 360 fraud, there are so many specific use cases, so many different applications, but they usually center on connected data. And so we see the nuances of things going even deeper or extending a customer's existing deployment where they may have a case management tracking system,

but what they need to do is be able to prioritize how many of those cases are high value to be able to have individuals take action on. And that could be on cybersecurity, that could be on a customer 360, that could be on fraud, but it's extending the next leg of what they're doing. They have their data, they have the tracking, but now they need to prioritize things on what to take action on. And what they're looking at graph on is not only to monitor what's happening within their enterprise, but to predict. And the prediction capability allows them to avoid problems in the future.

And that really becomes an operational aspect that can go into helping predict better modeling from supply chain or be able to provide pattern or similarity matching that can also go into predictive aspects to better treatment or better diagnosis because you're able to link more data together. So what graph is really bringing is innovation to be able to build upon upon more and more applications Because of the connectedness that can happen at a larger scale and to be able to go deeper and also be able to provide real-time Responses so if a customer is calling in and has a question The customer can look around and get all the information at their fingertips and not have to look at four or five screens and then say well based on this this is our next best you know our recommendation for you to for this for that and that is providing significant savings so we're

seeing graph really become an extension of a customer's existing data pipeline data platform across not only what's considered the standard use cases, but there's so many more extensions beyond those use cases. Okay, just a quick clarification before you jump in here as well. So I guess I didn't frame the question, I guess the way I framed it was maybe a bit misleading, because yes, I do know that you have been doing that already, this kind of real-time operational analytics, but I was more thinking about transactional applications. So in your example, when people do analytics on Customer 360, have you seen,

let's frame it that way, have you seen people, organizations, rip out their legacy RDBMS to replace it with TigerGraph, for example, to run their transactional applications? Yeah, got it. So that's a very good question. George, you already mentioned some of the popular use cases for graph, right? I'll start with more new use cases and then come back to the transactional side. What's really exciting about the Telegraph or in general for any database company is that

you always learn something new from customers, right? Because we're not a domain expert in any, you know, in all verticals, right? So, for example, we have customers using us for blockchain analytics now. So we are not experts in blockchain in any way at all, but our customers are. So the company is doing really nice analytics using TegraGraph. We have a customer, a government agency, use us for tax fraud. Yeah, it's fraud use case, but it's in the tax category. It's to protect the citizenship. It's Australia tax officer.

And also, similarly, we have customers coming from interesting use cases. Todd mentioned cybersecurity. We have customers in San Diego. They look at the hundreds billions of pages, right? And then try to find which pages are fraudulent, scamming, and so they can protect the end users. We have also customers coming from really complex

manufacturing to use Tagograph to do thousands steps of traverse, thousands hops, so to speak, to find which path is the best to manufacture good stuff. So reduce the cost of manufacturing. So we have so many other different use cases. So it's not just banking, it's not just fraud use case. We have many, many. Come back to transactional, we have database, it is a mutable graph database. We support the

transactions. So we, for example, we have a customer who demand 10 milliseconds performance. Of course, we also have a customer who use TechoGraph to do large-scale analytics, which could take a few hours, right? Because they need a, it's basically running machine learning algorithms, right? So, our goal right now is not to completely replace our relational database. You know, if a customer still use relational database to do the bookkeeping, right? And so they, you know, record the transactions

and then later can, you know, retrieve back the transactions really quickly. It's just completely fine. Our value proposition is that if you want to do analytics based on the transaction data, then Telegraph can bring

a lot of new insights. And the analytics could be in real time, less than 10 milliseconds, or could be a few hours. For example, we have a custom using Telegraph to prove known applications. So if I go to the website to apply for a known or acquired card, this is a transaction, right? I put in my name, my phone number, my address, something related to this application. Then immediately, they store this transaction, those events in Telegraph, right? So immediately, our graph is muted up to real-time data.

And then we also need to connect the dots. We need to go from my phone number, my address, my past information, and then they want to connect everything they know about myself to the negative databases, right? To see how I'm connected in a few steps to some negative data points, right? Maybe it's previous fraudulent transactions. Then they give me a score. And then they decide, do they want to approve or if not, they get someone to do the investigation, right?

So this real-time has to be done definitely in human real-time. So even when not replace the traditional relational database, but we have become the system record as well, because we get the real time update. We have everything. So, but for new companies, new applications, we think a lot of them are going to just store the data in Telegraph. So that's, you know, it's a new application,

new architecture, right? But for new legacy, we think people will start with the migration approach first. They build new applications only on top of the graph database like a tachograph. They're going to reduce the workload on the legacy database systems. So it's a gradual approach. It's not a complete shutdown of the older system. I think in reality, in the context of business world, nothing is going to be so dramatic, right?

So the people need the time to migrate. The old application will gradually die off, but the new application will be built on top of new databases. Yeah, that makes sense. I mean, that's a very common and very reasonable transition path. And I was just wondering whether you're starting to see that happening. And I guess you are from the sound of it. Yeah, exactly.

Actually, in the early days of Tiger Graph, we actually started with more real-time application than offline. Because offline, people sometimes say, OK, yeah, I can wait three weeks using Spark or other Hadoop systems. I can get a buy, right? But talk about the real-time type of analytics. If a customer needs one second, if a customer needs one minute response time, nobody can do that. So only Tegragraph can do that.

Then people, that's how initially our customer choose Tegra. Because they had no other choices. No other system can meet their high performance requirement. So they had no choice but to pick up Tegra. So imagine about three years ago when we got out of status mode, right? We had no reputation, we had no, you know, big customer testimony, we, you know, we just, we are not even the first graph database.

So why people choose a TegraGraph? And the more famous, you know, establish the products out there. People choose TegraGraph because they had no other choice. You know, exactly for this kind of real-time transaction analytics application I mentioned. So once customers choose TegraGraph, and then they realize they can do more, they start to do more, right? They started, you know, use Tegograph for all analytics, started using Tegograph to connect more data points.

That's how we actually started with a lot of customers, right? A lot of times it's actually the real-time transaction type application. That's the reason they choose TcoGraph initially. But now, of course, because we are more established, a lot of people know more about TecoGraph, know more about our use cases. Some customers choose us, and the first application is kind of actually offline type application. So now we have all kinds of customers in the mix, right? But in real time transactions,

always one of our strong point. Well, we also, we see too with our customers as, and this could be within the existing use case of the existing group is the expansion, being able to add more data because it can provide more insights. So it could be adding, connecting more data sources as they get their initial system up and running.

It could be adding more data, time series on a longer period of time of data. And this becomes even a bigger working group that can then spawn new applications within the organization because they're able to link all the data together. They don't have all these pods out there throughout the organization because they're able to link all the data together. They don't have all these pods out there throughout the organization.

The data is linked and that's spurring a lot of new activities. So we're seeing the customers innovate because it's becoming a platform that can tie into their existing areas and they're comfortable with and excited about, I can add more data, I can connect more sources, I can bring in external sources now.

And now when I start bringing all this data together, I now can run and get even better and faster and more useful business insights that accelerates the innovation, whether that's new features for machine learning or new applications. And that I think is one of the parts that we've been very excited about is hearing from

our customers is that innovation because they start with initial use case to improve upon where they are, but then it often adds more data, scaling out, and that's where we're able to help support them on a long-term journey with graph analytics. There's one particular use case that kind of stands out for me and I guess for you as well. And it's not a vertical one as customer 360 or supply chain analytics or whatever, but it's more of a horizontal one.

So, and you already referred to it a couple of times, actually, so graph machine learning and graph AI, and you even obviously value it as much as to center your event which you organized for the first time around it. So and I know it's actually it didn't just come out of the blue because I know that it's something that you've been practicing for a while and I know that because back, I guess a couple of years back, we organized a workshop on that together with one person from your staff, Victor, which was a very good one by the way.

So I was wondering, and the reason that stands out for me is because I'm seeing huge traction for it during most of the last year, I would say. So, loads, loads of interest and I guess the success, as you report, of the event that you organized also validates that. So, I was wondering if you'd care to share basically your experience, what you heard from people attending your events and also what you see from people using TigerGraph out in the field for this specific application and the ways that they do that and what your experience has been. Yeah, that's a very exciting topic to talk about. And you're right, so graph-powered machine learning AI, you know, it's not new to us. We have done something before.

But right now, if you look at the research community, graph embeddings, graph neural networks are heavily, you know, being researched, right? So there are a lot of papers, conferences around, you know, graph-plus deep learning, graph neural network, graph powered machine learning and AI. So this is really an active research topic. But in the industry, again, for the same reason, there was no really high performance graph database. So even the concept of using graph to do data science, to do machine learning is wonderful, but in practice, in the business world,

you don't have the scalable database system to support those, right? So now Telegraph made the distributed Graph database work, right? So we have a high-performance system now. Then, now we're moving more towards Graph plus AI plus machine learning. So that's why last year we started a conference. This year we're going to host the same open industry conference again in April and we plan to do more such type of conferences.

Actually if you look at the Relation database world, if you look at the SQL, right? So, you know, initially it's just meant for, you know, business data, right? And, but now if you look at the research community, even some bigger companies that trying to add the more machine learning and the AI capability inside the SQL, right? Inside the relation database by supporting machine models inside of the database, right?

In the SQL language. If you look at Google, GCP, a bigger query, they're adding more or embedding more machine learning capability in the client language, in the system, so that customers don't need to always go out to another system, right? You have to do data transfer, stuff like that. So the goal is to try to make your end user, you know, fast and easy to use your data and the use of the machine learning capability. But we all know that some limitation

on top of relation to base, right? So it's really nice, a lot of people, a lot of company are adding more machine learning capability to relation to base, right? So in different ways, from different layers. I think this is going to also happen to the graph database. Graph database offers, of course, much newer than relational database. Oracle had a huge success about 40 years ago, right? So and I think that is going to happen the same way. So we take a graph, it's a happy journey. We first, our goal is to make sure the graph database, you know,

is really, really become recognized, is going to be used by a lot of companies. Like Tata said, whenever there's data, you know, you need a graph, right? So, I mean, then on top of that, we're going to add more in-database machine learning AI capability through more, you know, built-in algorithms or even query language-level syntax to help the data scientists and machine learning engineers to use those capabilities more quickly and more easily. So, it's a lot of

innovation. I think it's new, that's why it's exciting. We are working with some of the smartest people in the world to lead this innovation. And we're going to have more exciting announcements about this hopefully in this year around the graph, around machine learning AI together. Thank you. And I guess since we're close to wrapping up, let's wrap up with something a bit more specific, but also something a bit more generic. So for the more generic part, I just wanted to ask you, well, you're out, you could say, on the graph market.

And originally my question was going to be about the graph database market but you know having said what we said so far I guess maybe we can even expand it a little bit to include other things than just graph databases so you know my feeling is that it is growing the pie is growing so to speak and more players maybe have a chance to get a bigger part of that buy. And I wanted to tie this into something in which I know that you are involved along with other vendors. So, standardization efforts in standardizing query language for property graph-based, graph database called GQL. And so, I wanted to ask, well, and you can address it in whatever order you see fit, how is that effort going? Because I have the feeling from what I hear from others, at least, because I'm personally not involved really that much in that. But I have

the feeling that maybe it's a little bit stalled. And I wanted to ask if you think that's the case. So that's the specific part and the general part, how do you see the market evolving and growing? Yeah, I'm really excited about the GQL initiative and I think actually it's moving really really fast in comparison to other standards. If you look at the history, actually we're moving at record speed, right? So, you know, being an international committee, being international standards with so many participants, of course there will be, you know, a lot of things that need to be discussed, need to be worked out, right? So, but overall I think it's moving at full speed.

It's moving at a rapid speed. And I think it's going to be a huge milestone for everyone in this graph space. Our customers always love to have international standard query language. No matter how much they love Telegraph, some of the customers actually are on the committee, right? So to express their opinion, to help guide the development of the query language, right? To bring some reality to the standards in some way. So it's truly international across community commitment and collaboration. Actually, I think people are doing great on the community from what I know.

People are really professional. They're really committed to bring the best query language for the whole world. So we, of course, want to make it very similar to SQL, right? So because SQL has the meanest, meanest for developers, right? Really mature community, a huge ecosystem. So we are working with companies like Oracle, try to make sure we are bringing really powerful,

but still really easy to use query language. I think people need to know this is the second query language standard for database. The first one is, of course, SQL. More than 40 years ago, right? Started by INC and then, you know, standardized by ISO, ISO committee. Now GQL is going to be the second query language to be standardized by the ISO committee in 40 years. I think it's exciting, right? If you look at other

K1U database, Document database, they're hugely successful now, right? Much, much successful commercial-wise than graph database, but they're not having an international standard language. Why? Because graph is more powerful, is more expressive than SQL, right? If you look at key-value document database, you know, the client language is much less powerful than relational database, right? If you want to build some application, you can use SQL to completely do it. But if you use other document or, you know,

Kubernetes database, you have to write Java, Python, right? Your query language, your data model is not that flexible, not that powerful. But the graph is more powerful, right? So, of course, the first draft of a GQL is not going to be, you know, fully expressive, right? So, of course, the first draft of GQL is not going to be fully expressive as we wished for, but over time it's going to become

more powerful and powerful. So I think it's really exciting of course for customers, for any customer that's not going to have any vendor lock-in. It's huge for developers because they don't need to decide to pick up, okay, this language or another language or another language for different vendors, right?

It's also huge big news for software companies who want to build, for example, visualization, who want to build the graph BI capability, right? They don't need to choose a graph vendor, right? They can just choose the GQL versus the platform

to build the application. And of course, it's going to be huge win for TycoGraph as well, you know, because we are able to focus on what we are doing best in terms of scalability, performance, right? We don't need to, you know, we still contribute the GQL, but we focus what really matters to need, you know, you know, we still contribute to TQL, but we focus what really matters to customers, scalability, performance. So, you know, it's a nice time,

you know, for a customer to think about the client language issues. So overall, I think it's a huge, huge milestone for everyone in this space. And we are going to see, you know, some result very soon, I think, by the end of this year, early next year, it's happening, you know some result uh very soon i think by the end of this year only next year is happening you know the train started it's not going to stop and it's going to just pick up speed and you know move quickly and more quickly yeah well i think george you sort of captured it a few years ago with uh your year the graph report but we as we know now it's really the years and when you look at the overall industry the whole industry and this is what we're excited about as a whole industry is is just moving and and through GQL working together through an open you know open

industry graph AI event it's bringing the industry together and there's a big pie that's just getting massive. And we really hope to continue to bring everybody together, drive and continue to expand the graph market in general. And I think when you're going back to your report, and I still remember it is, you know, it's fun to go look back at what you saw a few years ago. It's now you updated. It's a rocket ship up into the right in terms of application awareness adoption. This whole market is going very, very fast. And it's only going to get much, much bigger, as we mentioned, because it's going into analytics space, which is a $24 billion market. It's going into machine learning AI,

which is estimated $7 billion market. And then, you know, taking on workloads off of relational that are not designed for relational use cases is also part of another $55 plus billion market. So the market is massive, massive potential for graph. Great, thanks.

And thanks for, well, reminding me about what's really kind of a pain point for me, because I've been meaning to update that report for the last couple of years, but somehow I never got to it. 2020 was supposed to be the year that I was going to do that, but then COVID happened, and I had a whole lot of other issues to deal with. But I hope I can make it in this year. And when I do, you'll be among the first to know. Yeah, look forward to seeing your update. I mean, you have the pulse on the industry and, you know, and that's why it's been a pleasure, you know, speaking with you over the years and you've seen us grow. We've seen your perspective and your experience grow. So it's been, you know, it's kind of fun to look back but it's super exciting to see what's in front of it.

What lays ahead for all, you know, for the graph industry. I hope you enjoyed the podcast. If you like my work, you can follow Link Data Orchestration on Twitter, LinkedIn, and Facebook.