This Week in Enterprise Tech Episode 502 Transcript

This Week in Enterprise Tech  Episode 502 Transcript

Please be advised this transcript is AI-generated and may not be word for word. Time codes refer to the approximate times in the ad-supported version of the show.

Louis Maresca (00:00:00):
On This Week in Enterprise Tech, we have Mr. Brian Chee and Mr. Curtis Franklin here today. Now license plate rendering and reading technology is interesting, but it can really help automate all types of scenarios. However, it can actually be a double-edged sword. We'll discuss what that means. Plus union organization probably have a large amount of unstructured data, but what if you can make graph quarries across all of it, make sense out of it all. Well, today we have Katana Graph. He's CEO and co-founder of Katana graph. Talk about the graph APIs they have and just how you can layer AI technology. On top of all of your data, you definitely should miss it. Quiet on the set

Brian Chee (00:00:38):
Podcasts you love from people you trust. This is TWIT.

Louis Maresca (00:00:47):
This is twit This Week in Enterprise Tech episode 5 0 2 recorded July 15th, 2022. A graph is worth a thousand words. This episode of This Week in Enterprise Tech  is brought to you by Cisco Mera with employees, working in different locations, providing a unified work experience seems as easy as herding cats. How do you reign in so many moving parts, the Mera cloud manage network, learn how your organization can make hybrid work, work, visit mera.cisco.com/twi. And by user way.org user way is the world's number one accessibility solution. And it's committed to enabling the fundamental human right of digital accessibility for everyone. When you're ready to make your site compliant. Deciding which solution to use is an easy choice to make, go to user way.org/twi with 30% off user way's AI powered accessibility solution, and by Cisco orchestrated by the experts at CDW, when you need to get more out of your technology, Cisco makes hybrid work possible. CDW makes it powerful. Learn more at cdw.com/cisco. Welcome to twit this weekend, enterprise tech, this show that is dedicated to you, the enterprise professional, the it pro and Nike, who just wants to know how this world's connected. I'm your host Lewis Mosca. You guide through this big world of the enterprise, but I can't guide you by myself. I need to bring in the professionals and the experts starting with our very own Mr. Brian cheese, net, architected, sky fiber, and all around tech geek Chebert. How are you? My friend what's been keeping you busy this week.

Brian Chee (00:02:29):
I've been trying to clean up a little bit more so I can go and set up my 3d printers, compliments of Padre. He need to clean out some space in the California storage closet. So I inherited his 3d printers. So I can go and start playing now just as a warning folks, if you hear something like some, someone summoning, a sand worm in the back with a <laugh> the folks at duke energy apparently are, are deciding of course, right during the podcast to run underground conduit. And the directional drilling reminds me of June.

Louis Maresca (00:03:12):
<Laugh> looking forward to that. Thank you, Brian. It's great to have feedback. Well, folks, we also have the welcome back, our very owner and our friend of the show, Mr. Curtis Franklin. He's a senior analyst and I'm DIA and he's our security and an enterprise expert Curtis. You're, you're heading out pretty soon. You're going to, was it reinforce?

Curtis Franklin (00:03:31):
Yeah. AWS reinforce in Boston. I'll be up there the week of the 24th of July looking forward to that. And then just a couple of weeks after that, heading out to the desert for black hat and DEFCON. So you know, I am going to try to get away from the heat and humidity of Florida by traveling to the humidity of new England and the heat of the desert. So, you know, we'll just hit, hit the entire sort of ecological and environmental span in the course of a few weeks.

Louis Maresca (00:04:08):
<Laugh> now, are you are you expecting anything? What, what, what's the expectation for reinforc? What are you, what are you expecting that they gonna announce anything there that you heard of?

Curtis Franklin (00:04:17):
It's not so much the announcement that I'm looking for there, actually, I'm talking to people about enterprise security management. So looking at things like regulatory compliance, looking at issues like coming up with the proper controls, processes, procedures, all the things required to manage your presence in a large public cloud. I think there's gonna be, there's gonna be a lot of good discussion going on, not only from the people associated with AWS, but their clients who are gonna be there. So I'm looking forward to having a lot of those conversations that tend to crop up only at a major conference when you throw a bunch of interesting and and talented people together in one place.

Louis Maresca (00:05:13):
I'm looking forward to your coverage there. Thanks Curtis for being here. Well, speaking of lots of large discussions, we have lot talk about this week, so we should definitely get started. There's some interesting technology out there where, you know, such as license plate readers can, they can actually help automate a bunch of different scenarios, however they can, they could be a double edged sword we'll discuss on what that means. Plus you and your organization probably have a lot of large amounts of data and structured data out there. Now, what if you can make graph cores against that data simp just to make it simple for you and your developers and your services. Well today we have K chef Pali, CEO of co-founder of KA graph. Talk about graph APIs, just how you can layer AI on top of all of your data. Lots of exciting stuff to talk about.

Louis Maresca (00:05:52):
So definitely stick around, but first, like we always do, let's go ahead and jump into this week's news blips. Now you probably remember that last December, we talked about the disclosed vulnerability in log for Jayna. The vulnerability made it easy for threat actors to take control of compromised systems. And it was also difficult to spot. However, the impact was broad and the level of expectation was predicted to not be too deep, unfortunately, in a new post board of incident report by the cyber safety review board, the exploit was at lower levels than they actually predicted. But what they determined was that log four J was quote, an endemic vulnerability, and it's likely to drag on for years or even decades to come. That means it's far from over now, the amount of time and resources organizations spent trying to clean up the issue was extensive.

Louis Maresca (00:06:36):
In fact, the federal cabinet department spent over 33,000 hours, 3000 hours responding to across their networks. Now in response, the tech companies have pledged 150 million amount in the next two years that reduced the gap in open source security. Now, the review board, which the department of Homeland security launched in February is an example of a public private partnership, built to review and assess significant security events. Plus they will offer recommendations for how the public can respond. Now, the log four J report marks the board's first public security answer review. I would expect a lot more to come in the coming months.

Curtis Franklin (00:07:16):
Well, in news, straight from the look for a silver lining department, it looks like cryptocurrency woes are causing some real pain for ransomware gangs. According to data release this wheat from the theft resource center, ransomware attacks leading to data breaches fell 20% in the second quarter of 22, 20 22, compared with the first quarter of this year and have declined quarter over quarter. Other research firms have reported similar declines in ransomware activity at the same time, a blog post from cyber six Guild reported that most of the fly by night cryptocurrency exchanges used to law under ransoms have stopped advertising their services, suggesting that cash outs, surge creating a bank run leading to them, not being able to satisfy demand for cash. And that's just in these above board visible to anyone exchanges, cyber suit skill also reports that out of 34 dark web cryptocurrency exchanges, which typically charge high fees of two to 15% of transactions in exchange for anonymity, none of them continues to advertise any capability to exchange cryptocurrency for cash.

Curtis Franklin (00:08:34):
Now, of course, there could be other explanations for the drop in activity, including a shift in criminal tactics, business, email compromise, or B C as it's known in the industry has always been quite profitable. And since it tends to steal hard currency, instead of asking for crypto coins, it may see a surge it's already seen as slight increase. Other explanations for a drop in ransomware attacks include the disruption of the county ransomware group, which was an associated with an 18% drop in ransomware activity. And Russia's invasion of Ukraine. Since those two countries are home to some of the primary actors in the ransomware scene, but this news as good as it is, may be short, lived since ransomware could recover criminals using the tactic criminals in general are rarely static in what they do. Exchange and price volatility may convince cyber criminals to make the handling of cryptocurrency more flexible in their tool gets the cryptocurrency used in different campaigns could end up as a swappable piece of code that cyber criminals will change regularly, just like they do today with servers, IP addresses and malware signatures.

Brian Chee (00:09:55):
Well, if you've ever tried to do email while you're out on an ocean liner of some sort, you probably noticed that, oh man, that just wasn't cheap. And that's because the satellite connections cost a huge amount of money and they're paying for it, whether the ship is using it or not. Well, Starlink is announcing they're going to do a $5,000 a month rig targeted at oil rigs, premium yachts and probably cargo ships also. So anyway, Starlink maritime service will bring satellite internet service to large boats for $5,000 per month and an upfront hardware purchase of 10 grand quote form merchant vessels to oil rigs to premium yachts. Starlink maritime allows you to connect from the most remote waters across the world, just like you would in the office or at home. The services web page says Starling maritime advertises download speeds of up to 350 Meg per second, and the ability to pause and unpause service at any time, while, while being billed in one month increments, there's also secure fleet management and remote monitoring to manage your Starling fleet from a single portal.

Brian Chee (00:11:13):
The user terminals shown on the Starlink maritime page look similar to those used in Starling's home internet service, but SpaceX CEO, Elon Musk wrote on Twitter that Starlink maritime uses dual heavy performance terminals, which are important for maintaining the connection in choppy seas and heavy storms. The terminals are also ruggedized for relentless salt spray and extreme winds and storms in the deep ocean. Now, I can agree that this is expensive. You know, this is what you're probably thinking right now. And it is, you know, not cheap, but if you ever priced out an internet link for the high CS, say from Hughes VSAT or Iridium they're, they're pretty cheap, you know, upwards of half now. Just to put this in perspective, though, the biggest users of high bandwidth satellite connections on the high seas is actually research vessels and the research community in the United States, especially it's called the ALS fleet.

Brian Chee (00:12:24):
Got so fed up with the insane cost of Satcom connections that I believe they bought a stake in a dedicated satellite just to cover the Atlantic and Pacific. And so they pay considerably less just to put that in perspective. So things are going to be a little different. Keep in mind if you've seen a, a big ship going to ocean and you see a large dome call it about six feet across and maybe four or five feet tall, almost guaranteed. That's a satellite dish mounted on a gimbal. And that's probably what we're going to eventually see from Starlink because that dome does a really good job of protecting all that delicate hardware.

Louis Maresca (00:13:15):
Well broadband standards, regulating acceptable speeds for areas with little to no coverage have been in discussions for quite a while, while it looks like things are about to finally move forward. According to federal communications, commissions, chairwoman, they are aiming to increase the agency's broadband speed standard from 25 megabit per second to a hundred megabit per second on the download side and from three megabit per second to 20 for uploads. Now the 25 3 metric was adopted in January, 2015 under the chair at Tom Wheeler was never updated by former chairman, a J PI during his four year term during his leading competition there now PI decided to decided in January, 2121, that the 25 mega per second download and three up speeds were still fast enough for home internet users. Now, there was a good quote from the chairwoman in which she said the needs of internet users long ago, surpassed the FCCS 25 3 speed metric, especially during a global health pandemic that moved so much of life online.

Louis Maresca (00:14:12):
Now the 25 3 metric, isn't just behind the times, it's actually harmful. It's a harmful one because it masks the extent to which low income neighborhoods and rural communities are being left behind and left off line. The proposal requires a vote and the commission will, is still deadlocked with two Democrats and two Republicans at this point. Now under us law, the FCCS required to determine annually whether advanced telecommunications capability is being adopted or D deployed to all Americans in a reasonable and timely fashion, and to take immediate action to accelerate those deployments and promote competition. If current deployment is not reasonably and timely, let's see if this proposal finally turns into those actions. Well, folks that does it for the blips next up we have the bites, but before we get to those bites, we do have to make a really great sponsor of this enterprise tech.

Louis Maresca (00:15:02):
And that's Cisco. Mera the experts in cloud based networking for hybrid work, whether your employees are working at home at a cabin in the mountains or on a lounge share at the beach, love to be there. A cloud manage network provides the same exceptional work experience, no matter where they are. You may as well roll out the welcome app because hybrid work is definitely here to say, hybrid work works best in the cloud, has the perks for both employees and leaders and workers can move faster and they deliver better results with a cloud managed network while leaders can automate distributed operations, build more sustainable work spaces and proactively protect their network. The IDG market pulse research report conducted for Meraki highlights, top tier opportunities in supporting hybrid work. This is what it says. Hybrid work is a priority for 78% of C-suite executives. Leaders want to drive collaboration forward while staying on top of or boosting productivity and security and hybrid work also has its challenges.

Louis Maresca (00:16:01):
The IDG report raises the red flag about security, noting that 48% of the leaders report cyber security threats as a primary obstacle to improving workforce experiences, always on security monitoring is part of what makes the cloud managed network. So awesome here. Now it can use apps for Meraki's vast ecosystems of partners, turnkey solutions built to work seamlessly with the Mera cloud platform for asset tracking, location analytics, and more to insights on how people use their workspaces in a smart space. Environmental sensors can track activity and occupancy levels to stay on top of cleanliness reserve workspaces based on vacancy and employee profiles also called hot desking allows employees to scout out a spot in a snap locations in restricted environments can be booked in advance and include time based or access. In fact, with mobile device management, integrating devices and systems allow it to manage and update and troubleshoot company owned devices, even when the device and employee by a remote location, turn any space into a place of productivity and empower your organization with the same exceptional experience, no matter where they work with Mera and the Cisco suite of technology learn how your organization can make hybrid work, work, visit morra.cisco.com/twi.

Louis Maresca (00:17:22):
And we thank Cisco Mera for their support of This Week in Enterprise Tech . Well, folks, it's now time for the news bites. Now we've heard of license plate readers before it can really, it can actually help automate a bunch of things, lots, all different types of scenarios, make them easier for organizations to, you know, collect fees and, and so on track data, but they can actually be possibly a double edged sword cheaper

Brian Chee (00:17:48):
What's what's going on there. Yeah, well, there's lots of places, you know, license plate readers are one of those technologies that can be used for good and evil. That's probably a little polarizing, but it can be used for lots of different things. Like one of my favorite is using a license plate reader to recognize the car. So it'll raise the gate for private parking lots. That's actually a chunk of software that can fit onto cameras from access corporation along with facial recognition and lots of other things. So that's on, you know, one of the really cool things. Now, this ours Technica article, I'm not going to say the author's name because it's more than a little polarizing for a lot of different people. But he is saying that it could be used by the police in anti-abortion groups alike is the wording in the headline.

Brian Chee (00:18:49):
Anyway, as more automa automated license plate recognition systems are concentrated in metropolitan areas. They can be also quite common in rural areas. And what it does is if you get a high enough density, the amount of data you can get out of those starts resembling GPS tracking. And that's the that's the syno, you know, that's the BA they basically, that's the conclusion he's drawing. I hesitate because he's got some really interesting points and he's got some really inflammatory points. So anyway, he does quote some people like Dave mass director of investigations for the electronic frontier foundation. And that quote is it's a huge problem that people are sharing data without really being deliberate about who they're sharing it with and why we certainly had a lot of this conversation with Triveni Gandhi talking about the responsible use of AI, cuz that's what this basic comes down to AI and that type of processing has made this quite approachable.

Brian Chee (00:20:05):
In fact, if you are interested, you could actually just do a real fast search on the internet for raspberry pie license plate reader. And there is open source software that will run it on a raspberry pie. Now it'll only recognize the license plate. It won't compare it to a database, but since it's open source it's to keep you from doing more. Anyway the thing is one would hope that you would have to have some sort of legal action, you know, a warrant in order to do this. But the reality is you don't because a lot of these license plate readers are being run by private corporations and they're selling it for all kinds of different reasons. So on the good and bad side, a little of both one of the major uses for license plate readers, especially at state borders is tracking cars that haven't been paid for.

Brian Chee (00:21:05):
So if someone is beating feet out of the state with a now stolen car, cuz they're, they've not made their payments the reposessor want to be able to go and track them down. So good, bad you judge. But the private databases are for sale. You can buy, anyone can buy it as long as the company's willing to sell it to you. And the red flag that he's waving in the air is gee, if it's a private corporation, that means the antiabortion people can go and buy that data. And what they're proposing is, well, if it's illegal in one state and you drive outta state to that has legalized abortion we can, they can track you down and possibly prosecute you for fleeing the state for this. I'm not going to say anything more about that. You can draw whatever conclusion you want.

Brian Chee (00:22:11):
I'm just saying this type of thing can be both good and bad now. This is what, this is not the only large scale data collection that has a double edged sword. And this next question, I'm gonna throw at Mr. Curtis, because as an analyst, he's also been seeing a lot of different things on other data, you know, spending habits. We saw some of it in minority report and various other movies where you're walking through a mall and your spending habits are tracking you through that mall. And the displays as you walk along are changing according to your spending habits. So what red flags do you wanna wave up the air Kurt?

Curtis Franklin (00:23:01):
Well, I think that there are, there are several and they involve two things that are related, but not the same one is privacy. The other is security now led. This is something I was talking about actually earlier today on LinkedIn. Privacy has to do with the unauthorized use of data while security tends to do with the unauthorized access of data. So let, let's take a look at this when you are out in public, driving around, walking around, whatever it is, you have no real expectation of privacy. You expect that anyone who's walking on the sidewalk next to your car can look at your license plate. They could remember if they could write it down or they could have a camera and take a picture. So any one of these is something that could say, ah, this license plate went by this location. At this time, you can draw limited conclusions about that.

Curtis Franklin (00:24:14):
But with AI, especially you start to be able to correlate many, many, many different data sources to draw a complete picture of someone's activities, whether or not you have access to what would be considered private data. You know, you can get a good idea of their travels. You can get a good idea of where they go in terms of stores or you know, restaurants or, or doctor's offices or whatever. And so what we have is in a growing number of cases, the ability to build very complete pictures of someone's activities without having to go in and steal information about their credit card information. So I think what we're going to see here is a growing recognition that there are privacy issues. We're starting to see, for example, the federal government look at different sorts of records that can be kept. I believe that in coming court cases, we will see law firms going after companies that have captured and retained information that they may think of as being fairly innocuous.

Curtis Franklin (00:25:47):
But when combined with other databases and run through the amazing SIV that is artificial intelligence, create a picture of someone that violates their privacy. So what I'm recommending to companies is that they start right now looking carefully at what data they record and how long they keep it. And I think it is worthwhile to look at minimizing both of those, because if you have a policy that says we will capture only the data required to do our business, and we will flush that data after one day, five days, seven days a month, a year, some defined period of time. But if you have that policy in place and you follow it religiously, then no one can come to you in a period of time beyond that policy and say, you should have this data that we want. If on the other hand you wait until after they ask for it to say, oh, darn, we just changed out our hardware and deleted all those messages you want. You can easily find yourself on the receiving end of a contempt or evidence tampering, citation.

Brian Chee (00:27:17):
Wow. Lots of things to talk about. Lots of things to think about, you know, Lou your, your hip deep in the database world. You know, once you start, you know, scanning that many license plates, you know, what's your spin, how easy is it to, to start scaling? What could easily be billions of plate instances spread all over the United States and correlated together? I I've known law enforcement have used still images of people going through toll plazas to track people's movements. But right, when you start talking about a database that big, what are some of the interesting ramifications of something that big?

Louis Maresca (00:28:13):
I think there's two parts to it. I think, you know, obviously you're talking about data coming from a stationary place or stationary area. So like, if, for instance, if you know, we know of some police stations, whether it's in Canada or United States using this technology already as they're driving around their cars you know, for things like you were saying, whether the car's stolen or whatnot, so you have to type it in anymore while they're driving, they just, it just kind of does the detection. And if some normal open source developer, or even just any developer can, can develop a, a deep learning model with TensorFlow and train it with some images to, to produce something similar. And this type of data could be very, very easily acquired by people. And I think what you're saying is, well, what could happen if we have a centralized database of this data now, and now it's kind of flowing through the system.

Louis Maresca (00:28:59):
And it could be a lot of data obviously, but I think obvi it it's manageable because there's a limited set of license plates in wherever country you live in. And, and that datas obviously related to the person who is registered for it. So I think that, that they already have that database somewhere. And you would just be relating to it. I, I think the challenge here though, is like Curtis said is, you know, there there's other data coming from these systems that could be could be privacy nightmares, right? There's there's location information that comes along with it where that license plate was acquired. Where were you at that time? Why were you driving that car? That kind of thing. And you know, and, and if you combine it with other things, like, for instance, you know, insurance companies, they collect data about your driving habits.

Louis Maresca (00:29:45):
And so, you know, now it's, Hey, this person was driving, you know over the speed limit, but they were all, they were driving, not necessarily over the speed limit, but they were driving fast or erratic, but then find out they were in a 25 mile an hour school zone because of the license plate scanner. So like, I think there's, there's lots of ramifications that come along with this that could apply to lots of different scenarios. Now, managing that data, I don't think would be too difficult cause this type of data is very limited in, in set. But I, I do think that the applications could be, you know, they could be useful in some cases, but they could be, you know, also a privacy nightmare in the future. So we'll see, we'll see where it goes. But I, I, you know, you also have to be able to access the data about the registration too.

Louis Maresca (00:30:29):
So you might just have the, the actual ID right now. Like I could drive around with this type of software and collect a bunch of license plates, but that doesn't mean I know anybody who, you know, I don't have access to the data to actually reverse them into the registration themselves. Only, you know, people who have access to that can get that information. Now. It doesn't mean I can't get it, but it does mean that I don't have that just in time access to it. But in the future, that might not be true. I mean, there might be databases of information of that data too, as well, readily available for users. We'll see what happens. Yeah.

Brian Chee (00:30:59):
It sounds like we need to have a lot, lot more conversations on the ethical use of such things. You know, it sounds like we should get Triveni Gandhi back on the show. What do you think?

Louis Maresca (00:31:10):
I think so. I think so.

Brian Chee (00:31:12):
Well, I think we need to go to our guest. What do you say?

Louis Maresca (00:31:17):
Sounds good. Thanks sheer. Well, we, well, before we do get to our guests, we do have to thank another great sponsor of This Week in Enterprise Tech  and that's user way.org. Now every website without exception needs to be accessible. Now, user way's incredible AI power solution tirelessly enforces the hundreds of w C a G guidelines out there. Now in a matter of seconds, user way, AI can achieve more than what an entire team of developers can do in just months. At first, it may seem overwhelming to make your website accessible, but user way solutions make it simple, easy, and cost effective, and you can even use their free scanning tool to see if your website is ADA compliant. Now, if you're an enterprise level website with thousands of pages, user way offers a manage solution where their team can handle everything for you. Now user way is incredible AI and machine learning solutions, power accessibility for over a million websites, trusted by Coca-Cola Disney, eBay, FedEx, and many other leading brands.

Louis Maresca (00:32:15):
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Louis Maresca (00:33:06):
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Louis Maresca (00:34:20):
User way can make any website fully accessible in ADA compliant with user way, everyone who visits your site can brow seamlessly and customize it to fit their needs. It's also a perfect way to showcase your brand's commitment to millions of people with disabilities, go to user way.org/twi and get 30% off of user way's AI powered accessibility solution, user way, making the internet accessible for everyone. Visit user way.org/twi today. And we thank user way, their support of this week and enterprise tech. Well folks, it's my favorite part of the show where, where we actually get to bring in a guest to drop some knowledge on the TWT rye. And we TA today we actually have, KKB a golly CEO and co-founder of Katana graph, founder of CATA graph. Welcome to the show Khe.

Katana Graph (00:35:12):
It's a great pleasure to be here, Lou, thank you so much for inviting me to your podcast.

Louis Maresca (00:35:17):
We're excited to have you here. Lots of stuff to talk about, but before we get to all of it, our audience is made up of a lot of levels of experiences and they love to hear people's origin stories and their journeys through tech. Can you take us through a quick journey through tech and what brought you to CATA graph?

Katana Graph (00:35:34):
Yeah, so I'm the CEO of Katana graph. I'm also a professor at the university of Texas at Austin in the computer science department. I did my PhD at MIT. After that. I was a professor at Cornell for many years. And about 15 years ago, I got tired of the snow and ice in upstate New York, as you might imagine. And my family and I moved to Austin about that time I started working in the graph area. I did a project for DARPA together with BAE systems that involved a lot of high performance computing on graphs and DARPA really liked the system that we built. And so at the end of the project they offered to support us to do a startup about that time. I also met some of my current investors and so that's how we got started about two and a half years ago.

Louis Maresca (00:36:31):
That's a great story. Now. Now I know that a lot of people understand that data is kind of an undiscovered country. However, they, they don't really know how to, to wrangle it and monetize on it. And it's a lot of, a lot of data sometimes kind of sits in storage and they don't know what to do with it. Now I do know that, you know, a lot of organizations have lots of services that might already expose graph APIs, but some of them don't allow you to, to take data from other sources and combine it and make use out of it. Now, I saw an interesting stat on your website just recently that businesses generated about seven septillion megabytes of data per day. That's a lot of data and they just don't really don't know how to monetize on it. Now, graph CATA photograph is kind of doing something different with compared to some of the other organizations out there cause they're layering technology. Can you maybe take us through some of the things that you guys are doing and what makes you kind of set you apart?

Katana Graph (00:37:21):
Yeah, so at Katana graph, what we are doing is building what we call a graph intelligence platform. And what that means is we have a platform that can process very large amounts of unstructured data, very fast in order to extract actionable insights, using very fast algorithms and AI techniques. So what makes CATA graph different? One thing is that most of the data in the world is still stored in the form of tables in relational databases and it's processed using SQL and other such wonderful systems like I'm sure you're familiar with, since you're at Microsoft now that works for things like say payroll applications. So mm-hmm, <affirmative> you build a table where each row of the table is an employee and then you have columns for the name, social security number where they're located salary and so on.

Katana Graph (00:38:21):
And then you can say, give me all the employees who make more than 200,000 and live in Austin, for example, but more and more of the data sets that we deal with don't really fit into that tabular form. And they are heterogeneous, which means the data comes from many different sources. The data is different types. And then the connections between all of the data are very irregular. And so they're best represented as a graph. One example of that was mentioning this project we did with DARPA. So they were interested in doing what's called intrusion detection in computer networks. So you're running a computer network. There are bad guys and good guys using the system. You want to catch the bad guys as possible. So what DARPA wanted to do was to build what's called an interaction graph where the nodes of the graph represent the users of the system, resources, IO boards, and so on. And then there are certain forbidden patterns that you look for in this graphical data. So for example, if every communication from me to you, Luke has to go through Kurt, then we can say, is there a path in this graph from my note, your node that doesn't contain Kurt's node. And so those are the kind of patterns that are relatively easy to discover when you represent data in a graphical form and process it using graph algorithms much more difficult to do using traditional techniques like relational databases

Louis Maresca (00:39:54):
Right now. I, I think it's interesting cause I know a lot of people who have used graph in the past, I, I usually worked on the Microsoft graph team. So I can know that, you know, lots of 'em use it for search capabilities, relating things, looking things up so they can build little small services with that. But one of the challenging things is combining that let's say that data with some other data, like for instance, you might have some financial data or you might have, like you were saying network detection, data or data about a user, and then being able to also query and, and gather that data and relate it together in a, in a, a much usable sense. How, how does Katana do that? How, how are you ingesting this data and making sense out of it all and, and building it a kind of a graph API around it? What, what are the, the technologies that you're layer in there?

Katana Graph (00:40:37):
Yeah, that's a great question. So we can ingest data from many different data sources. So CSV files is the simplest example where it could be Parque files other graph databases, or we can even take in data that's stored in a relational database. And you have to tell us what's called the schema. So roughly how the data is organized, but we have all of these adapters that can take all of this data in and convert it into our internal representation within Catana graph. And that is stored in secondary storage in what we call a resilient, distributed graph, RDG for short. And then what you can do is you can query the RDG. And then when you do the query, we bring in the portions of the data that's required to process the query. And then you can use analytics and AI that we provide in order to get insights from that data.

Louis Maresca (00:41:37):
This is pretty amazing. Cause I was, I was actually reading about catatonic quite extensively this week. And I was noticing that it, it can actually scale up clusters to over 256 machines. It can communicate to different paths using that graph analysis model. So it's, it's very sophisticated making, be able to make you know, the unstructured data for organizations more useful. Now, are you exposing what, what kind of programming interfaces are you exposing? Are you exposing all different types of languages? Are you exposing it, making it easier for people to, to query this type of data?

Katana Graph (00:42:10):
Yeah, so for querying the data, we support a query language that's called open cipher. That's used by lots of people. So that's you can think of it as SQL for graphs, right? And so that's how you can do query. But if you want to program your own analytics algorithms or AI algorithms, we give you two programming languages. So if you are a nerd, a hacker, you can go write C plus plus code, and then we give you certain constructs that you need to use that expose the parallelism and make it easy for us to figure out how to run your program in parallel. We are also providing a Python interface, so that's much more familiar to data scientists of course. And so you can write your code in for your analytics algorithm or AI algorithm. You can write that in Python and then that Python ultimately gets compiled down to C plus plus, and then we run that in parallel.

Louis Maresca (00:43:10):
That's impressive. Now the, obviously you talked a little bit about some of the examples in the industry that are making you of this cause a layering AI on top of all the data that you've ingested into a graph system is definitely adding a lot of value for organizations because that means that now you have the ability to not only query that data yourself and develop your own insights and reports, but then also have other services do. And such as, you know, in such your AI agent develop some insights, analytics, and even produce some events off of that without you having to do anything, is that, is that true? Is there some organizations in some areas of the market doing something like that, where they're, they're using you to, to produce you know, insights into some of their data that they wouldn't have been able to in, in the past?

Katana Graph (00:43:57):
Yes, that's right. So you know, the traditional way of using data is just to do queries on it and that's, what's called descriptive analytics. So you can think of it as basically you're storing a bunch of things that have happened. And then you're asking questions about the past, but where everybody wants to go is to this area of predictive analytics, as it's called, that requires you to take the data that you have and build a model for it. And then you use the model in order to do influencing and predict what might happen in the future. So building the model that's called training. And so you take the data that you have in our case, all the graph data, and then you build a predictive model out of it. And then that predictive model you can use in order to do in and predict what may happen in the future.

Louis Maresca (00:44:49):
That's impressive. Well, we have lots more to talk about here. And of course my my, my co my co-host here also have lots of, lots of questions to ask. So we'll definitely come back to them in just a moment, but before we do, we do have to thank another great sponsor of This Week in Enterprise Tech . And that's Cisco orchestrated by the experts at CDW, the helpful people at CDW understand that hybrid work continues to evolve and that your organization must evolve with it to succeed now with so many options to collaborate remotely, you need a strong and consistent network to empower your workforce and keep them together. Now consider a Cisco hybrid work solution designed and managed by CDW experts to deliver the same quality network experience to all of your offices. Even your satellite ones, connecting your team from pretty much anywhere because Cisco networking keeps things flowing smoothly and securely with embedded security compliance and multifactor authentication that protects collaboration among your spread out team with real time visibility into your distributed application security user and service performance.

Louis Maresca (00:45:52):
Yet a better line of sight into higher network is operating and how better to grow your organization and Cisco networking levels, the playing field, providing an access to flexible high end collaborative experiences that create an inclusive work environment. When you need to get more out of your technology. Cisco makes hybrid work possible. CDW makes it powerful, learn more at cdw.com/cisco. And we thank CDW for their support of this week and enterprise tech. Well, folks, I, I wanna bring my co-host back in here, cause we're talking about a lot of really interesting topics about graph and graph APIs and graph data. I wanna throw it to Kurt first, Kurt,

Curtis Franklin (00:46:38):
Thanks, Lou. You know, one of the things that we, we talk about a lot is data. And when we talk about it, we assume that it's gonna be used almost exclusively as part of an automation sequence. In other words, something for that a machine is doing for another machine to consume, but human beings still spend a lot of time looking at charts and tables and other things, but so much of this data. And especially as you talked about the, the kind of data that doesn't fit readily into a tabular format can be difficult to understand how critical is the whole visualization process for the kind of graph data that, that you are working with and, and what are you doing to help people be able to understand the data as human beings?

Katana Graph (00:47:40):
You know, that's a great question. And that's one of the sore points about big data processing in general, not just for grabs, which is how do you visualize the results? And then secondly, how do you explain the results that have been returned by the system to you in return in response to a query or some kind of a predictive model? So it's visualization and explainability the way that we are tackling this is let's talk about visualization first. So we believe that a lot of data is actually more easily visualized as a graph than it is as a table. Okay. And so the we believe graphs are innately easier to understand and comprehend for people. And I'll give you an example of that. All of us have been on planes and when you get bored, you open up the inflight magazine and you'll see airline map on it.

Katana Graph (00:48:41):
Now the roadmap has dots that represent airports. And then there are lines connecting airports that have direct flights between them. So this is an example of a graph, right? The dots, the cities in this case, they're called vertues or nodes in general for a graph. And then the lines that are connecting different dots. Those are called edges of the graph. And so you can play games like, for example, you can say, I'm in Austin. I want to fly to the bay area. What is the shortest number of flights that I need to take from Austin to get to the bay area on this airline? And that's the kind of query, that's the kind of analysis that's very easy to do. If you have a graphical representation, much more difficult to do, if you're representing it as a table. Now imagine having an airline roadmap in front of you, you see the dots, you see the edges, and it's very easy to visualize and understand what's going on.

Katana Graph (00:49:39):
I can represent the same data as a table. So in a table, each row would correspond to one flight and then there would be the city from which the flight originates and the city to which the flight goes. But then if you had to answer queries, like the one that we just talked about that's much more hard. And also if you want to visualize the roadmap, it's also much more difficult in order to see what's going on. If I gave you the data in the form of a table. So we believe graphical data is much easier for people to understand compared to relational data. Now, the big problem that we face and that lots of other people face in this area is that the graphs that we are dealing with are very big. So for some of our customers, the graphs have 20 billion nodes and more than 200 billion edges, right?

Katana Graph (00:50:32):
And it's really not possible to visualize a graph that big in any meaningful way. And so what we do is we have built a tool that's called a graph exploration tool, where basically you can start at one of the dots, so to speak one of the nodes, and then you can see a small neighborhood of the graph. Some of the age is connected to it, and then the neighboring nodes and so on. And then as you navigate through the graph, you see more and more of the graph that's local to wherever you are in the graph. And so at this point, we can display a few thousand nodes on your screen. The edge is connecting them and also any property data that's on the nodes and edges. And then as you navigate through the graph, we refresh the display and give you the new data that's relevant to wherever you are in the graph, but it's still, it, it is a big problem.

Katana Graph (00:51:22):
This is how we are addressing it. Partially more work needs to be done. I, I think it's really a research area as to how to display that sort of graphical information in a meaningful way. The other issue is explainability. And the way that we are addressing that is that our graph AI system and the graph analytics system, they produce summaries of how they reached the particular conclusions that they have reached. And then that summary is linked to a visualization of the result data. So that's how we are addressing those two problems.

Curtis Franklin (00:52:00):
That, that is very good. I, especially like the part of trying to do summaries and essentially make it simple enough for a human, for even a human to understand. You know, you were talking about the research and, and I've got to say a number of years ago as a matter of fact, the first time I ever put on a Oculus rift helmet was at an SAP conference and we were using the Oculus rift to walk through data sets. Do you think that we're beginning to get to the point where the data sets that we're looking at are going to require this sort of novel display to put them in a form that humans can well, literally wrap our heads around, you know, I, I would put in a thing, I, I remember talking to AI researchers who talked about solving problems in 27 dimensions, and it's tough for me to, to draw a 27 dimensional picture in my head. And so perhaps being able to walk through help. So, I mean, are we reaching a point where first of all, that's going to be required and next our graph database is a good tool. If we are going to move into this sort of, you know, next generation visualization.

Katana Graph (00:53:31):
Yeah. That's, that's a very interesting idea. I think you know Oculus things like that will give you a three dimensional display, right? That you can walk through. The problem though is still the size of the data sets that you have to deal with. And I don't think there is any alternative than to focus on small neighborhoods of your data. And then as you walk through it, you can display more and more data. So I think you can go from a 2d display to a 3d immersive display, but fundamentally the problem is just the scale of the data that you need to deal with. And then the other issue that you are referring to, which is the dimensionality of the data, because in graph data, we have nodes and edges, but then the nodes and the edges have a lot of property data that also needs to be displayed. And how one does that in a explainable way, understandable way to people. How do you summarize all of that data? That's really very exciting area. Lot of research to be done. We have some solutions, lots more to be done there.

Brian Chee (00:54:43):
So I'm gonna ask the academic question, you know, having recently come from academia, one of the projects that was, shall we say more than a little challenging to analyze the data was when we were monitoring photo vol take production across a large neighborhood, but we're trying to correlate that with data coming in from the power companies, data coming in from the weather service and so forth because there was a feeling, a gut feeling that they all were all interrelated, but the mat lab was, shall we say more complex than anyone thought we could tackle? So here's the question. If I was a researcher and I've got lots and lots of time series data that I think are, will correlate to each other, what do I need to do? What kind of homework should I do in order to use a tool like CATA graph?

Katana Graph (00:55:47):
So there are many problems there. Brian, the, the first one is being able to take data from all of these different data sources and then assimilate them, right, integrate them into a single representation that you can then do querying on or analytics on, right? That is the big problem that you would face once you do that. We have all the technology within CATA graph in order to do the querying and the analytics and AI and whatever other pro visualization at the end of the AI pipeline. So the big problem really is being able to take data from disparate sources. And this is something that Lou also talked about at the beginning of this segment and bringing it all together into our system and other systems face exactly the same problem. And so what we have done is to build these adapters that can take data in different formats and then convert them into our internal representation, and then we can process them. So that's the way we would do it. If the adapter that you need for the particular data source that you have doesn't exist, we can work with you in order to build that adaptive.

Brian Chee (00:57:00):
Fabulous. what should someone that's listening to show? What kinds of data should they gather? What kind of, you know, descriptors should they gather, you know, before they start talking to you and how would they go about getting some of your time to see how appropriate their problem was to your system?

Katana Graph (00:57:25):
So you can go to a website which is www.catanagraph.com. It's on the banner at the bottom over there. So that describes our technology, some of the key ideas behind that technology that came out of this DARPA project that I was talking about earlier. So you can see some of the secret sauce that we have within Katana, which we have released to the world. And then you can get in touch with us. So we are currently focused on three verticals because as a startup you know, when I did CATA started CATA graph three years ago one of our investors told me that the biggest problem that I would have as a CEO is learning how to say no to opportunities. And he's exactly right. And so what we have decided to do is to focus on three verticals.

Katana Graph (00:58:23):
So one is health and pharma, where people are using these very large knowledge graphs, and then using analytics and AI in order to do drug discovery, for example. So we work very closely with AbbVie. I can tell you that because we publish papers together about our, our cooperation. So we have a contract from them to use our analytics and AI in their drug discovery pipeline. So medical and pharma is one area. We have a lot of experts within Catana who can work with companies in that space. We also are involved in FinTech and banking. So we are working with the biggest online payments processor in the world to do fraudulent transaction detection as quickly as possible. And then earlier we talked about the InfoSec area. So intrusion detection, security, those sorts of things. So we have expertise in those three verticals. And so if you're a company in those verticals, you can come talk to us and we'll show you what we can do. And if we need to build something especially for you, we are happy to do that as well. If you're outside those three verticals right now, we don't have the bandwidth to work with you, but starting next year, we're going to open up the flood gates work with more companies.

Louis Maresca (00:59:45):
And she fortunate we're running a little on time, but I did have one more question regarding, you know, obviously some of these three, three verticals might have some compliance requirements. They might need to follow things like HIPAA. When you're visualizing data from, from specific data sources, what are some of the things that CATA graft are doing to manage that?

Katana Graph (01:00:08):
Yeah, that's a, that's a great question, Lou. And the way that we are dealing with it is that we have a somewhat simpler problem than let's say a company like Avie does. Right. And that's because when we show them what we have, when we start working with them, we actually work on publicly available data sets. And so that is how we prove to them the value of what we have to offer in our graph intelligence platform, and then what our sales engineers do once they're convinced is they work with people within Qana and then the system itself is run entirely within AbbVie, for example, or whatever other company it is by the data scientists in those companies. And so we don't actually see the data and we don't deal with all of these HIPAA issues and things like that because we only work with publicly available data. And then it's only when we send our system over the fence, so to speak to the other side that they have to worry about it.

Louis Maresca (01:01:14):
Got it. Well, thank you so much for being here. Again, Brian kind of brought it out a little bit to where people can go. Is there anywhere else they can do to, to maybe get started? Can they try things out? Is there a way for them to try things out with, with, with the, can they go for that?

Katana Graph (01:01:30):
Yeah. Go to our website and you'll see demos over there. And we'll soon have a system that you can play with. It'll have a bunch of canned analytics, routines, AI routines. And so you can point to your data set or some of the data sets that we've already loaded with cloud ready. So we run on GCP and AWS. And so you can click on a data set, click on the algorithm that you want and then visualize the results. So that's a quick way of understanding the capabilities of our system.

Louis Maresca (01:02:02):
Fantastic. Thanks again for being here. Appreciate it.

Katana Graph (01:02:06):
Thank you. It was a pleasure.

Louis Maresca (01:02:09):
Well, folks, you've done it again. You sat through another hour, the besting enterprise and it podcast in the universe to definitely tune your podcast or to try it. I want to thank everyone who makes the show possible, especially to my cohost, starting the viral, Mr. Curtis, Franklin Curtis, what's going on for you in the coming weeks and where can people find you and all of your work?

Curtis Franklin (01:02:30):
Well, people can always find me over at dark reading. That's dark reading slash Omnia. They can also find me on Twitter and they can follow me on LinkedIn. All of these are possible can find me in person at the AWS event. That's coming up as well as black hat and DEFCON. And by the way, if you're going to be at black hat, remember on Tuesday of that week, if you've already got a ticket for black hat, we're doing the AMIA analyst summit. I'll be speaking along with all of my colleagues. There'll be round tables, there'll be discussions and it's all at no additional cost. Go to the dark reading slash AMIA. You'll find a registration link there, go and register for that. You won't spend any money, but you will get, spend time with me and my colleagues in the air, conditioned splendor. As you hear us talk about our research and analysis of the industry.

Louis Maresca (01:03:30):
Thank you, Curtis. Well, folks, we all stop to thank everyone, Mr. All folks, we all still have to think are very well. Mr. Brian, G cheaper to what's going on for you in the coming weeks, maybe what you're going to be printing with 3d printers and where can people find you

Brian Chee (01:03:42):
<Laugh> I need, I actually need to 3d print. What are called sprinkler S you stick 'em around your sprinkler heads to protect them from the weed whacker, you know, evil weed whackers

Louis Maresca (01:03:55):
Might need some of those. So many similar.

Brian Chee (01:03:57):
Yeah. And you know, the commercial ones are like four or $5 each. And that adds up really fast. When I think I can 3d print it in PTG for maybe 30 cents. So we'll see how that turns out. But anyway, one of the things I am doing is I'm, you know, writing to various PR people trying to see if we can get people that fit the request from our viewers. We have lots and lots of viewers all over the world and you helped drive the threads and the topics that I try to book. So throwing ideas on Twitter, I am ADV, N E T L a advanced net lab. And we, you throw all kinds of stuff at me. I actually threw a picture that was done for a gentleman that retired at the research corporation university of Hawaii. That is an actual piece of the Laja cabled observatory cable.

Brian Chee (01:04:59):
It's a retired at and T cable called ha four. It was laid in the very early seventies and has since retired and it was donated to university of Hawaii. We now have the Aloha cabled, observatory audit, and looking at all kinds of really weird and wonderful things under the water. You're also welcome to throw me email I'm sheer spelled C H E E B E R T. I have a, we had a Dilbert naming scheme in my lab sheer twit.tv. You're also welcome to throw email@twittwi.tv, and that'll hit all the hosts. We'd love to hear you show ideas. You're welcome to rant. In fact, I strongly encourage our viewers in other countries that throw, throw your request, throw your questions at us. Even if it's in your native language, we will use a machine translator and try and get you a good answer to what you're asking us. Love to hear from everybody and stay safe, everybody.

Louis Maresca (01:06:05):
Thanks, Brian. Well, folks, we also hop to thank you as well. You're the person who drops in each and every week to listen to our show and get your enterprise goodness, and wanna make it easy for you. So to listen and catch up on your enterprise in it news. So go to our show page right now, TWI that TV slash TW there you'll find all of our amazing backup episodes is all of 'em over there. Plus all the show notes, the coast information, the guest information, of course, the links of the stories that we do during the show, but more importantly, next to those videos there, you'll get those helpful subscribed and download links, support the show by getting your audio version, your video version of your choice and listen on any one of your devices or anyone in your podcast applications, cuz we're on all of them.

Louis Maresca (01:06:44):
And subscribing definitely keeps you up on things, but also helps support the show. Plus you may have also heard that's right support the show with club TWI as well, support twit with club twit. So members only add free podcast service with a bonus TWI plus feed that you can't really get anywhere else. You can't get anywhere else. And it's, it's only $7 a month and then you'll get a lot of things with it. You get exclusive access to the members only discord channel, where you can chat with hosts and producers and have separate discussion channels, lots of fun stuff going on there. You can also have and be parts of events, special events as well. So definitely join club TWI, be part of the movement. Be part of the fun, go to twi.tv/club twit. Now club TWI also offers corporate group plans as well.

Louis Maresca (01:07:24):
That's right. It's a great way to give your team access to all of our ad free tech podcasts and plans start with five members at a discount rate of just $6 each per per month. And you can add as many seats as you like. And it's a really great way for your it departments, your developers, your sales team, your tech teams to stay on top of access to all of our podcasts and just like regular members. You can also join the twit discord server and get the TWI plus bonus feed as well. So go to twit, do TV slash club TWI now right after you subscribe, definitely impress your family members, your coworkers, your friends, with the gift of TWI. Cuz we talk a lot about some fun tech topics on the show and I can guarantee they find it fun, interesting as well. So have them subscribe and support the show.

Louis Maresca (01:08:09):
Now if you're already subscribed and you're available on Friday 1:30 PM Pacific time we do the show live. That's right, we're doing it live right now. Live TWI do TV is the site has all of our streams on there. You can pick and pick the one you want, come see how the pizza's made. Come see the behind the scenes, all the banter and all the fun that we have here on TWI and TW on the stream. So definitely join the stream and, and be part of that fun. But also if you're gonna watch the show live, you might as well be part of the chat room as well. Our irc.twi.tv chat room who have a lot of fun characters in there. Some awesome discussions, a lot of great topics get brought up for the show part of the show after the show, before the show in there.

Louis Maresca (01:08:46):
So definitely join this chat room and be part of that fund as well. Thank you guys for being here. Irc.Twi.Tv is the, the way to join that. Now if you, I want you to hit me up on Twitter at twitter.com/lu M there I post all my enterprise tidbits. I love direct messages from people having great conversations with people like you around the show around topics. I had a good conversation the other day around around database cloud databases also hit me up on LinkedIn as well. Louis Breca on there. Oh, we had a great conversation about access. I was really impressed about some of the people talking about access and, and their current usages of it and also integrating to the cloud with that as well. So really cool stuff. So definitely hit me up on there as well. I wanna hear all about it.

Louis Maresca (01:09:27):
Now, if you wanna do what wanna know what I do at my Workday at Microsoft, go to developers.microsoft.com/office. They will see all the latest and greatest ways for you to customize your office experience and make more productive and more powerful for you and your organization. Just definitely check that out and, and be part of that as well. I wanna thank everyone who makes this show possible, especially to Leo and Lisa. They, they continue to support this week at enterprise tech and each and every week. We couldn't do this show without them. So thank you, Leo and Lisa, for all your support. I also wanna thank all the engineers and staff at TWI. I also wanna thank Mr. Brian. She just one more time. He's not only our co-host, but he's also our Titleist producer. He does all the bookings for the show and the playings for the show and we really couldn't do the show without him. So thank you cheaper for all your support. And before we sign up today, I also have to thank our editor, Mr. Anthony, he likes, makes us look good after the fact he corrects all my mistakes. So thank you, Anthony, for all your help. And of course, I also thank our TD for today. The talented Mr. An Pruitt, who he does a fabulous show called hands on photography, and I watch each week religiously. And what's going on on this week's show.

Speaker 6 (01:10:37):
Thank you, Mr. Lou. Well, this week I decided to do a bit of a deep dive into retouching the way a professional photographer would retouch. And I had some I, I think these subject people, they may be important. Maybe <laugh> the Laport family. So they were,

Louis Maresca (01:10:55):
It's only if you do it well that

Speaker 6 (01:10:57):
Well, yeah, yeah. There's that too. There's that too, but yeah, be sure to check out the episode, TWI TV slash hop for hands on photography and stuff there.

Louis Maresca (01:11:07):
Fantastic. Thank you. And I'll definitely check that out cause I need to definitely touch up all of my photos. So I appreciate that. Well until next time I'm Louis Mosca, just reminding you, if you wanna know what's going on in the enterprise, just keep quiet.

Speaker 7 (01:11:21):
Hey, I'm rod Pyle, editor of ad Astra magazine and each week I'm joined by Tarek. Mallek the editor in chief over@space.com in our new this week in space podcast, every Friday tar and I take a deep dive into the stories that define the new space age what's NA up to when will Americans, once again set foot on the moon. And how about those samples from the perseverance Rover? When are those coming home? What the heck has Elon must done now, in addition to all the latest and greatest and space exploration will take an occasional look at bits of space flight history that you probably never heard of and all with an eye towards having a good time along the way. Check us out on your favorite podcaster.