Good day, fellow investors. The financial environment has gone completely crazy and that's something completely normal. Bubbles are a natural part of the humankind, are a natural part of how we are wired because we always tend to overreach. We always tend to let's make more money, let's invest more, let's grow bigger, bigger, bigger and bigger and that usually always goes up but then falls down and then goes up and as long as we see higher highs and higher lows everything is okay but some bubbles can be innocuous like blue apron bubble and home delivery meals of something like that but where if the company pops a little bit of investor lose money but it's not significant a little bit more important because a lot of people will get burned and will never invest like cryptocurrencies when that bubble pops a lot of people will lose their money and take it out especially from Nigeria or some other countries like that which are the highest investors in cryptocurrencies and there are extremely dangerous bubbles. Bubbles that are created over 75 year long debt cycles and that can have extremely negative consequences, depression consequences on a economy on the global environment and so on. Let's dig into a little bit of bubbles to see their impact their possible impact on the economy and how dangerous those bubbles are because the things are that are going on in the financial environment are completely crazy and somebody has to start telling the world that things are crazy it might well be me let's start first let's look at the Fed's financial stress index it has never been so low since it measured this means that it is very easy to get to money you know what happens when you have easy money when you have free money people usually get addicted to that money and start doing very very crazy things which is completely normal and happens over and over again in time now this money people have more money unemployment is very low everybody has something extra to invest and then there is a cool story like cryptocurrencies not the bitcoin but in this case i want to talk about ripple or however do i pronounce it if you look at the chart we have seen here ripple in bitcoin prices the orange line and ripple in us dollars the green line in the last month weeks i don't know what happened but in the last year ripple went from not even 1 cent per coin to 3.46 dollars per coin at that moment in time the market capitalization of ripple was 133 billion which made the founder of ripple the seventh richest person in the world worth more than larry page sergui brian of jack ma if you look at this table here and you put the founder of ripple at position five or six you can see that you have probably used products from each one of the companies these gentlemen's here own however you probably have never used ripple to buy anything else and this is example number one of how crazy this environment is however this ripple thing its marginal prices go up and down a little bit of people will lose their money as it always happened the founder will probably make a lot of money because he will be selling it to the ones buying it but that's a different story but it's not dangerous for the financial system now we're going to talk about things that are crazy and extremely dangerous for the global financial system and nobody's talking about nobody's talking about risks it's something very important that everybody should think know about and see how to behave in relation to that first the notion of risk is gone this is the yield that the german government is paying on a five year bond for the past three years it has been mostly negative how can a yield on a bond be negative perfectly distorted financial markets crazy financial markets that's one what's even crazier than those investing in the german five-year bond is the european high yield index high yield means junk and you can see here that the index the yield on the index on the average junk bonds in europe is 2.55 percent companies that have a junk rating means that whenever something happens in the environment some change happens those companies are going to have extreme difficulties to refinance or pay back their debt and that's something again crazy that you invest in something that offers just 2.55 percent which in the next recession goes bankrupt as you can see on the chart the yield in the last recession went up to 25 percent which means that you will be able to sell those bonds at 25 percent of their current value if not even less so extremely crazy environment now this abundance of money is making people all around the world take debt and people that have the money printed by the ecb the fed and bank of japan bank of china they lend it all around the world chasing yields because you cannot get the positive yield from the german bonds so you look around the world where you can get it if we look at the spread between the us treasury and emerging markets us dollar denominated bonds so from countries that are emerging risky everything can happen the spread is just 1.67 percent so you can invest in a us 10 year treasury and get a yield of 2.44 percent or you can invest in a country like indonesia or something like that and get a 4 percent yield in the case of trouble as you can see again on this chart the emerging market yield goes quickly to 10 percent because people will rush to the dollar for safety perhaps you never know but it's extremely risky and that spread is crazy what has happened that all that money all that abundance of money has entered every corner in the whole world so which means that when there will be a problem with liquidity in those corners it will affect the whole system and then there might be very very big trouble but this emerging markets again it can be dealt with corporations okay we can save them something can happen there is a lot of growth okay but there is something even more dangerous and there is something so dangerous that that it will affect people in the next 50 years let's see the ultimate chart of risk and how crazy this environment is this is the us federal government fiscal and debt forecasts from the bmi research institute you can see that the us federal deficits are piling are bigger bigger and bigger and that the us federal government debt to GDP ratio will approach 100% in the next 10 years and that's something again normal interest rates have been going down for the last 45 percent and governments have been high on borrowing everybody wants the best everybody wants to do the best and the easiest thing to do more and more and more is by taking more debt debt debt and debt however that long-term cycle of piling up debt without seeing further than the next four years without seeing higher interest rates with how without seeing who will repay that debt it's the craziest thing that is going now in financial markets so we have to a say okay it's crazy and be prepared for it we can see huge inflation because governments will need to print money to pay back those debt if things go wrong we can see a recession a huge recession even worse than it was in 2009 because when people lose faith in money then we have a problem and in higher interest rates will force those governments that are just piling money to pay more money and that will again destabilize countries continents and who knows what so the financial environment is crazy the best way to be protected is i think to be hedged so from a personal perspective what will happen in the environment you never know however estimating the risk and rewards always works well even if nobody's looking at the risks now which is the craziest things of all thank you for watching looking forward to your comments


From San Jose in the heart of Silicon Valley, it's theCUBE, covering Big Data SV 2016. Hi everyone, we're welcome back. This is George Gilbert. We're at Silicon Valley Big Data. This is running alongside Strata and Hadoop World. And we have a very special guest with us today, Costa Tsumas. For those in the know, he's the key guy behind Data Artisans, which is the company that is behind Flink, which is on everyone's tongue, sort of where Spark was a couple of years ago. Welcome, Costa Tsumas. Hi. Nice to be here, George. So tell us, give us a little background on the company. How it got started, the mission, where you are in funding, so we get a sense of where you are in the life cycle. Yeah. So the company is actually breathing you. We started Data Artisans about one and a half years ago when we basically saw the need for a very high performance stream processor in the market. So there was a certain gap, at least in the open source space. So we started one and a half year ago with a seed financing round from a European VCB2V partners. And actually, yesterday, we announced our series A round led by Intel Capital. Intel Capital. Okay. They don't get much more blue chip than that. So, all right. So tell us stream processing has been on the top of everyone's mind. Absolutely. Let's start at the top. Why is stream processing so topical now? One, what's made it possible for it to be such focus, and then why is it so relevant? So if you ask me, actually, I wonder why stream processing hasn't been around for a long time. So to me, stream processing actually enables the obvious, which is continuous analytics on data that is produced continuously. So if you look at most interesting data sets out there, they're not static. They don't have a beginning and an end, right? They are enraged and their records added and there's new events coming up all the time, right? So look at user behavior, click streams, logs, sensors, reading from sensors, etc., etc. So the very logical thing is to actually do the analytics on these continuous data sets continuously. Now, the funny thing is that until recently, the assumption of all the tools in the infrastructure was that data has a beginning and an end, what people used to call sort of bots. So I think what is different now is that number one, the rate of change is much higher, so especially with new data sets coming in like Internet of Things and lots of user activity, the rate of change is much higher, and a lot of companies want to move to real-time decisions on demand, so they really want to give answers to the customers immediately without delay. Is it fair to say that we've sort of had two modes of computing for 50, 60 years like batch or request response, meaning the online? That's right.  What made it really sort of take off? Was there some catalyst for it that you can identify? Like every technology, a lot of companies have been doing this for a while, so a lot of this comes from companies like Google or LinkedIn, Twitter, Amazon, etc., that have embraced this for a while. Banks have been doing this for a while and telcos, and now the rest of the world is following, so now stream processing is becoming extremely mainstream in the whole enterprise. And I think one of the key factors is the maturity of the open source software for stream processing, so until now, the open source tools were not as mature, if you wish, as they're batch processing counterparts, but now this is actually changing. There are a lot of open source projects out there, including Apache Flink that we're developing, Apache Kafka, Apache Beam that recently came out of Google, that are basically pushing this level of maturity to the point that they can cover all data processing with streaming. So, okay, give us a nice concise example of how you would go about using stream processing in conjunction with a broader application, and then what that might look like if you go about it for batch processing, because, I mean, programmers would know that, but maybe not all our audience would. Sure. So, I would like to actually take an extremely simple example. I will actually be talking about this at the Strata conference right after I leave here, and that is counting. So, we want to count things, things like social media analytics, user behavior, aggregations on metrics, on infrastructure, like logs, et cetera, things like that. So, let's say that we want to do continuous counting. Doing that with batch tools is possible, and people have been doing this with Hadoop and Spark and other tools, but it's very problematic. So, you need to glue together a bunch of systems, you need to write out files, and then, hopefully, you schedule a job that hopefully starts when this file is ready, hopefully finishes when the next file is ready, et cetera, et cetera. So, it's very brittle. There are too many moving parts. You cannot do much. This boundaries between these batches that you schedule are very rigid. It's unclear what is in the first boundary and what is in the second, and this becomes just natural and simple with stream processing, because the only thing you need is actually a stream processor, and all the important code about the application is actually in the stream processing program.  Okay, so you've set us up with a good example. Now, tell us, the whole world's now focused on Spark. It's the shiny new toy, and within Spark, everyone's focused on Spark streaming as the answer to this need for continuous computation analysis. Help position Flink against Spark. What are the sweet spots? When should you use one? When should you use the other? Or is Flink so much better that people should sort of migrate to that? Well, I'm obviously biased, right? That's okay. So, the way I see it is the following. So, both Spark and Flink share a commonality that they're both very broad. They want to cover a lot of types of analytics. So, in Flink, we're not doing only stream processing. We're also doing batch processing. We're also doing a bit of machine learning. We're also doing graph analytics. Spark, the same thing. So, this is the commonality. But that said, its project has its, you know, really strong points, and the market is usually focusing on the strong points. So, Flink is a true stream processor in the sense that you can do event-at-time analytics with very low latency, a few milliseconds. So, you can get an event and immediately publish an alert on that. You can do very, very rich stream analytics that actually take into account that the streams that you are getting in do not arrive in order. So, things that you cannot very easily do with other stream processors. So, I would say the really strong point for Flink and where we see most of the adoption is companies that, for which streaming is not just a little add-on. So, if you're doing like a lot of Spark and you want to do a little bit of ingestion, then you probably would like to do that in the same framework. But if stream processing is really at the core of your infrastructure, then you really need a proper high-performance stream processor. Do we need, if people are really to take advantage of continuous processing and stream processing, do they need to rethink how they build their applications end to end? Or is it just like how one part might communicate with another part? So, there is a level of rethinking in the infrastructure from storage to compute. So, there are two main differences. So, with streaming you embrace the fact that, let's say the ground truth is the history of the world. So, it is all the events that have come since the beginning until now. And it is not, let's say, the state of the world. So, you're not trying to capture the state of your business right now, your whole business. But your ground truth is the history. And then every isolated application, microservice, etc., is building its own local state. That is the first thing. And the second thing that you need to embrace with streaming is the explicit management of time. So, a lot of these things are time series. So, inside the application code, you actually need to define things like time windows and say that I would like to aggregate the last 10 minutes worth of data. So, this is now part of your application code. You're managing it in the application. I would say that these are the two main things. So, it will take a little bit of education for people to get there. But it is happening. And once people do that, they realize that it is actually a very, very natural way to interact with data. Okay. So, in the last 20 seconds that we have, tell us what we should expect from Flink and Data's artisans over the next 18 to 24 months. All right. Yeah. So, that's a very good question. So, we're working on a lot of stuff on Flink. So, for example, we're working now finally on adding SQL on both static and continuous datasets. We're working on making Flink programs scale dynamically and respond to what is happening in the input streams in the world.  And, you know, obviously, from Data Artisans, we're growing a lot. We're growing the team. We have our primary location is in Berlin. In Germany, we have people here. We're growing both locations, so Berlin and San Francisco. We are going to offer a lot more to Flink users and generally take the project way, way forward. All right. Costa Sumas. Great to have you here. Flink and Data Artisans, I'm sure we'll be hearing a lot more from you. We're in San Jose at Big Data Silicon Valley, running concurrently with Strata and Hadoop World. This is George Gilbert. We'll be right back after a short break.


So, welcome to the very first episode of our new workforce identity developer podcast, recorded in March of 2023. I'm Emily DeHumans, eDunham to computers, and today I'm joined by Mega Rastogi, an Octa Product Manager in Access Management. That's quite the job title, Mega. What does it mean? Yeah, thanks, Emily. So as a Product Manager in the Access Management team here, I work on understanding the customer needs around multi-factor authenticators, more specifically our workforce customers, their needs, and building out great user experiences for them. We want to make sure that they're able to set up their authenticators, use them for recovery, enrollment, authentication properly, and be able to be protected from all types of threats. So if you've heard about this podcast, you've almost certainly heard about Octa, and maybe even heard the company's top-level goal is about freeing anyone to safely use any technology. Workforce refers to a subset of that audience, a problem space that's characterized by this unique set of challenges and requirements. So, Mega, what do you mean when you talk about workforce identity? Sure, yes. So we talk about workforce and consumer. So focusing on workforce, when we say workforce, we're referring to the enterprise customer needs. So what that means is protecting their employees, business partners, and contractors. And we want to make sure that we have provided them with secure access to their resources within the organization. Yeah, so when we talk about workforce customers, we're talking about those particular challenges of identity within a business. So identity is part of any business's infrastructure. If the right people can't access the digital or physical assets that they need to complete their tasks successfully, that's terrible for the business. And similarly, if unauthorized people are able to use the business's virtual or physical resources, the potential problems are limited only by the attacker's imagination. So Mega, are there other considerations that are unique to workforce identity? Yeah, absolutely.   And what they want to make sure is that they can protect you, your identity, as well as all the resources within the organization. So that's why security is super critical for workforce use cases. And which is why admins generally control the authentication preferences and access for their users. So they'll set up policies around who can access which application at what level, and that's all controlled within the organization compliance and regulatory requirements. Okay. What are some of those compliance requirements that people might have heard of? Yeah. So generally, there are different assurance levels that companies need to meet their compliance standards by, and actually NIST or National Institute of Standards and Technology, they have defined very well the authentication assurance levels and what they mean. And a lot of times when more sensitive apps require AAL2 or 3, that probably means they need things like phishing resistant authenticators. Yeah, I've heard that AAL1, that's just you have to have memorized a password, referred to as pre-breached authentication. So it seems though, like it wasn't that long ago when memorizing a long, secure passphrase, maybe sticking in your password manager was the state of the art in digital security. What changed? Yeah. So passwords, which are just secrets, they are, as you can imagine, very hard to memorize for a human brain, right? So either what you're going to do is create a very easy, secret password, perhaps like one, two, three, four, five, six, which is the most common password. Or what you're going to do is create a really complex one and you're going to forget it. You're going to write it down and it's going to get compromised. And you're probably going to reuse that same password for ease of use at multiple sites. So once it's compromised in one place, the attacker will have access to all your accounts. So it's easily, very easily compromisable. Yeah. And even if someone does use a password manager, there's various ways to intercept it. And then when you have their password, you are them. In the ops space coming from a DevOps background, we have had an alternative to passwords, which is public key authentication. I could generate a new key pair locally, then give someone my public key, and then they could use that public key and some math, of course, to test whether someone claiming to be me really had my private key. So this was more secure than passwords, but there's some major problems. Like if you lost your private keys, you were just locked out. And if someone got a hold of your private key and the passphrase to it, they could impersonate you just like they had your password. So pub key auth is actually still how you bootstrap your way into a new cloud instance on most cloud hosting providers. But it was never practical for most people to use in accessing most systems. Most people were stuck dealing with passwords. Right. And to address these problems of passwords, we developed actually multifactor authentication. That's why we have MFA. And what that means is actually, there are three types of factors.  So multifactor authentication requires two, at least two of those factors. So something like a password plus a t o t p or a push would be a legacy multifactor authenticator. Yeah. And we call them legacy now because attackers figured out a way around the legacy MFA systems. If you have a user's password through any of the ways that you could get passwords when it was just password auth, all you needed to do was trick them into giving you that temporary one time pad or authenticator code. And now you can impersonate them. That's right. So we had first just secrets like passwords.  So that was a bit of friction for the attacker. They found something they, that's what we call phishing. They actually got very sophisticated with social engineering techniques. And also in the market now we have phishing kits that actually intercept OTPs. So phishing attacks are very easy to implement now. And actually the most concerning form of phishing is using real time phishing proxies or what we call adversary in the middle attacks or you may have heard of middle man in the middle attacks as well. So in these attacks, what's happening is the user is getting tricked into entering their credentials into an attacker control phishing site that acts as a reverse proxy. So the user thinks they're entering the credential on the right side, on the legit side, but they're actually on a phishing site. The phishing site takes their credentials. They might even send them that push notification. The user will go ahead and accept the push notification. And that in credential and acceptance is related to the actually the legit site. And what is happening here is the attacker will go and steal that session cookie and the credentials. So that session token and the credentials are stolen and return to the user's browser. Oh yeah, there's all kinds of ways that can go wrong. It's like that set of scams where you can tell that they had scams because they started adding never, ever give this code to anybody in those text messages from your bank when you're logging in and also making a malicious domain name look just like a legitimate domain name. It's easier than ever now that there's Unicode support because there's so many different Unicode characters that'll all look the same in most fonts to most people. So even if your user is doing their best trying to visually check that they're on the domain they think they are check. Oh yeah, there's a little green lock. There is a certificate that was issued to the domain that I'm on. They can still be fooled. So that brings us to the modern identity landscape and today's topic fishing resistant MFA. So to start with the hard question, why do we only call it fishing resistant and not fishing proof? Oh yeah, so fishing proof is kind of absolute as you can imagine. So resistant is sort of relative and there are always going to be things outside of the organization's controls. So for example, with fishing resistant authenticators we can control the attacks on the identity, but what do we do about the end point-based attacks? So if your device has a malware and your session has been hijacked in other ways, that's something beyond what an authenticator can solve for. But fear not, that's why in security we have the layers of defense. So their fishing resistant authenticators is one line of defense. Beyond that there are policies, there are workflows, there are monitoring tools, there are EDR tools, all of these build up to help protect you overall from fishing attacks. Yeah, absolutely. Like if you don't want to get hurt in a car crash, you have to not only maintain your vehicle but also wear your seat belt, just those layers. So this fishing resistance is going to make it impractical for attackers to execute the types of attacks that cause those problems with passwords and with legacy MFA. So WebAuthN is a standard that's developed by FIDO or the fast ID online alliance. So to use WebAuthN what you need to have is a hardware authenticator and a supported browser. So Mega, how does WebAuthN put those two things together? Yeah, absolutely. So FIDO to WebAuthN actually utilizes that public key cryptography to verify your identity. So users can authenticate using their biometrics factors such as your fingerprints so you can do a touch ID or facial recognition or by actually tapping a security key like a UB key. So what this does is this provides a more secure way to authenticate users because it requires that physical factor like your touch ID but and this cannot be easily replicated or guessed as well. So how all of this is actually working is there are two parts to any authenticator. One is the enrollment and one is the verification. So let's talk about enrollment. So when a user is trying to enroll into a WebAuthN MFA for a particular service, the user's browser that they're on generates a public and a private key pair. So this private key is actually stored securely on the user's device. So that could be your laptop or if you're using a security key, it's on the security key while that public key can be sent to the server. Now, the public key also, the server also keeps track of the public key, the user and the domain that it's tied to. When the user tries to access the app, so this is when the verification step is happening, the server sends a challenge to the user's device. It says, hey, prove that it's actually you. Now the user's device uses the private key it actually has on the device to sign the challenge. So only the device has that private key is unique to that user. Once that challenge is signed, you know that user is that user and that they're trying to access the site that they were actually enrolled for. So this creates a response. It's sent back to the server. Now the server has the information it needs. It can verify the response using the user's public key. And if it matches, voila, you're in. So that's how the enrollment and the verification for WebAuthN is working. So as you can see here, the credential key pair is cryptographically signed and the private key stored on your TPM, the hardware key, security module of your laptop, and also a credential key pair is tied to the specific domain, the website or the app you're trying to reach. So this mitigates that phishing attack because imagine like you accidentally fall into that phishing trap, right? No one is immune to that. So the user has your username password, they enter that, but they don't have your fingerprint. They don't have access to your device or the private key. So they actually cannot complete that authentication and their attack would be blocked. Yeah. And this is very familiar to me coming from the ops space, having used SSH keys a bunch, because public key crypto is really old and really well tested. Ops people have been putting up with the hassle of using it by hand, ever since SSH was invented back in 1995. And so just to make sure we're on the same page with the listeners about how public key cryptography works, you get this key pair that say a public key and a private key. What you do with it is that if someone knows my public key, I can use my private key to mathematically prove to them that I'm the same person who created both those keys together. But if that person only knows my public key, they can't use that public key to pretend to be the person who has the private key. So how it works is that there are some operations where it's really easy to check whether it was done right for a given set of inputs, but it's hard to guess what inputs would give you that output. The classic example is that it's easy to multiply two big prime numbers together to get an even bigger number. But if I just give you that really big number, it's really hard for you to figure out which two primes went into it. So historically, if you were using public key crypto, the systems that you use would handle some of that for you, but you were still responsible for generating your key pairs by invoking a command line utility with the correct options. And then your private keys were files on the disk and you had to keep those files safe. Most utilities encrypted your private key with a passphrase. But if someone got ahold of that file and they could get the passphrase like they did any other password, they had the whole key. Adding more keys also added to the work of memorizing more secrets in order to use your keys. So it made this really strong temptation to just use one key for everything. Just let's get everybody sharing a key. And then when somebody leaves, oh, let's just not think about that. So what I'm hearing from you, Megha, is that WebAuthN is making this public key cryptography super easy to use, often even easier than memorizing your password. Because instead of protecting the private key with a passphrase, this hardware security module is managing it. And it's only using that key when the owner does a physical thing in the real world, like pushing a button, touching a sensor, typing in their pin that tells them, yeah, it's time to use this key. And also, it's no more work to manage 100 keys like this than it is to manage one key. So WebAuthN gets to use a separate key pair for every single domain. And if you go to a domain you've never been on before that looks familiar, and it says, hey, you want to make a new key pair? Then that's a huge clue to the user. Oh, something fishy is going on here. Right, exactly. So you can see how complex things get, right? So imagine doing all of that every time you come into the office and you're trying to log in. So really, WebAuthN is all about creating ease of use and also high security because the phishing resistance is an authenticator, which is what makes it very unique and actually enables those passwordless experiences. So it hides all the details from the user.  It's as simple as that every day. But WebAuthN in itself is actually split in two variations.  But also the second one is security keys like your UB keys and Google Titan keys. So they work in slightly different ways. They have different assurance levels. And now with the introduction of past keys, this has become a little bit more complicated. And we are moving towards the concept of single device credentials, which are tied to your device and multi device credentials, which are actually in the cloud. So WebAuthN itself is evolving. Yeah. And with your hardware factors, your single device factors, you have the same headache we always had with SSH keys. You accidentally lose access to it. You misplace the little physical thing.  You accidentally delete that file. And you were the only person who ever had your private key. You lose it, it's gone. It failed secure. So when things go wrong, nobody gets access. So I wonder if we can use this tech to fail safe instead, when striking more of a balance between convenience and security is appropriate. Could we use systems like this to share a user's private key across several devices or to help them retrieve their credentials if they accidentally locked themselves out? Oh, yeah. Good question. So for a long time, like you're saying, WebAuthN had not the best adoption for reasons because it was not easiest to onboard. Since for every new browser you moved to, you needed a new WebAuthN enrollment, a new device you moved to, you needed a new WebAuthN enrollment. And the same goes for recovery. Like you said, you lose it, you're out of it. So this is where pass keys came in. And last year, the Fido Alliance and a lot of the big camaraderie came together and said, we're going to solve for this. And what we are moving towards is something called a multi-device pass keys. And when we're talking pass keys, we're generally meaning multi-device here. They're just Fido2 WebAuthN credentials and a secure phishing resistant replacement for passwords. So that's key. It's supposed to be a replacement for passwords while keeping the security property of phishing resistant. Now, let me explain a little bit about what this multi-device means here. So what this does is that it means that your credentials can be backed up to a cloud provider like iCloud or Google Cloud and that it can be synchronized across devices. So here, your onboarding and recovery story just lights up. You can enroll once on my MacBook here and I can sync it to my iCloud. I can go to my iPhone and with the same iCloud, I have access to my WebAuthN credentials. And this makes it really easy to go from one device to another. And also, if I lose my laptop, it's okay. I can actually still access it from my iCloud. Now, what this means is we're preserving that strong key-based phishing resistant authentication model, but we are trading off some enterprise level security features such as now these keys are not device bound. So they're not stored on the device. They can be taken to the cloud and the attestations that we were getting from the authenticators before. They're not available in many of the cases. So as you can understand, we talked about how workforce use cases are around security and we do need a lot of these features like device bound and attestations there. Yeah, because if you have one key pair per device, then you not only know from the user side, this really is the same domain, but you know from the domain side, ah, this is not only the same user but the same piece of hardware that's logged on before. Yeah, because that trade-off for the convenience of pass keys is that the attacker can use those same channels to regain lost access that the real user would. So to get your pass key onto that new device, you've got to prove that you're you to that cloud provider's satisfaction, which to be fair is often good enough. Yeah, so that's right. Multi-device pass keys are still phishing resistant and way more secure than either password authenticators or legacy two-factor authenticators. So yes, they are good enough in many, many, many cases and if it might be the easiest way to move to phishing resistant. However, for more secure, secure conscious organizations, this is probably not going to meet the bar and this is where our security keys or single device credentials would come in. And also keep in mind that these multi-device pass keys are just as secure as your cloud provider access because you are going to store them there. Yeah, like if there's any workflow where a cloud-based, let's say email might be the single factor for gaining or regaining access, then as secure as your cloud provider already sets an upper bound on how secure that system's ever going to get. There's times when it's appropriate to not even allow self-serve email reset, which we see a lot in the more higher security, more corporate settings. But then there's also that trade-off of giving users quicker access to the things that it's worse for them to be locked out of than for them to take a little bit more risk on. So I definitely see how there's circumstances where that slight risk in favor of the huge convenience boost is worth it. And there's times when it's not. Yeah. Yeah. So if I'm a developer who's working with Okta, how can I add WebAuthn or pass key support to the applications that I'm working on? Yeah, so this is the same workflow as you would add any other tenecutor, which is to our Okta admin dashboard, or using our factors API. Both of those would allow you access to these tenecutors. This does fall in the MFA, AMFA skews. So you would have to have those skews as well. And can I restrict access to resources to require unlimited phishing resistant authentication? Absolutely. We have some great policy level constraints around our app sign on policy that has a great constraint that says you can only allow phishing resistant tenecutors, which will allow either WebAuthn or any other phishing resistant tenecutor. And you could even go more into saying that I want hardware protected and phishing resistant tenecutors. So we do allow that kind of flexibility in our policies. Okay. So that's all going to be at the policy level for whoever is managing identity at your organization. And as the person working on the app, it sounds like I barely have to think about it as long as those policies are right for the application. And I'm curious, Mega, is there anything that you're hoping will change within, let's say, the next few months about WebAuthn and PASCIs? Yeah. So multi-device PASCIs has been great for balancing security and usability. And I think it's great to get users into that passwordless phishing resistant journeys. However, it does, like we talked about, it does have some security concerns specifically for workforce use cases. So what we are hoping is to see PASCI providers to provide more controls and give access to attestations and metadata that help us discern more into if that authenticator is valid and if that device, which device was registered with that authenticator. So this kind of more information will help a lot of our workforce use cases and security controls. Oh, yeah. I see how the cloud provider will have a very, very good guess about what device the credential is coming from. Although, as you add that, you're of course adding another surface for attack. So there's going to be trade-offs for them and implementing it, for sure. And are WebAuthn and PASCIs the only phishing-resistant options that we've got available? No, actually, Okta offers a variety of phishing-resistant authenticators. So we talked about, obviously, WebAuthn and PASCIs, but we also have a smart card authenticator that we recently introduced, which is more for federal use cases, customers that use PIV. But we certainly have something called Okta Verify FastPass, which has all the properties of phishing-resistant authentication. It is our phishing-resistant authenticator, but on top of that, it also offers device context. So it's a great way to have the user context and the device context in one. So definitely meets a lot of needs for a lot of customers. Thank you so much. Thank you for joining us to share your perspectives and your insights into these technologies and how they're being used. Are there any other resources that you'd recommend for someone who wants to learn more about WebAuthn and PASCIs? Absolutely. Okta has some great blogs out there around phishing-resistant and WebAuthn and PASCIs, but we also have our Okta Help documentation, which has both the developer and the product documentation. So it's a great read to follow through on how to use WebAuthn. If you'd like to learn more about standards, the phytoalliance.org has specs around WebAuthn and more information around that. We also have the NIST standards, the National Institute of Standards. So that's a great read as well for assurance levels. Awesome. Thank you so much. So if listeners would like to discuss this podcast or if you have ideas for what you'd like to hear about in future podcasts, there will be a thread on the Okta Developer Forum linked wherever you found this podcast where you can talk about it. Thank you so much for having me. This is great.


Great, thank you very much for coming to my talk, wherever you are in the world. And I'm talking to you today about structuring oils by enzymatic acerolysis, which is a recent development we have done in our lab. So a little less scattering oriented, even though there's a little scattering for the scattering heads among you. But before I do that, because you're so focused on this kind of thing, let me tell you a little bit about what we're doing in our lab with scattering, since scattering is so important for Lund and for the rest of the gang in here. So just three quick projects. We're doing a pre-nucleation structure of liquid triglycerides in the melts. The whole idea, again, I know it's again, again, again. Whether the triglycerides in the liquid state has some sort of structure before they become solids. We're using sand sacks and atomic scale molecular dynamics.  We were doing some milk coagulation work you may have seen published recently. We're trying. And we also put cheese in a U-sands at NIST and see what we get, whether we can tell a good Gouda from a old cheddar from the neutron scattering profiles. I don't know if I have an answer for that. And the other one that got me very excited is this little machine that you see on the right. That's an U-sax machine, ultra small angle X-ray scattering that is inexpensive Bench-top. It has a Germanium 220 monochromator. It's a Bond's heart camera, as you can see there. And you can go down to cues of maybe 10 to the minus 3, X from to the negative 1. And it plugs to the wall 110 volts and it has an air cooler. Would you believe that for a U-sax machine? Anyways, hopefully we'll get something useful out of this thing and it won't be a waste. Quick things about the liquid structure triglycerides, a story that I told you on the left hand side here. You can see a tripolmitin triglyceride and in collaboration with Giuseppe Milano from University of Naples. He went from Japan to Naples back. He's an expert in ASNB, one of the best I've seen and a pleasure to collaborate with. It's a project with Gianfranco Mazanti, Derek Russo, Amarangoni and Giuseppe Milano. And he had some simulations of tripolmitin crystallizing, as you can see from an isotropic melt into some sort of lamellar crystal.  And of course, with the judicious use of some coarse-graining, these are the glycerols. And we discovered that it's actually the glycerols agglomerating that have a role to play in the structure formation prior to forming the lamella. Very interesting stuff. And so combine that with some very good work done by Gianfranco at NIST, in which we of course scattered the neutrons off this still-not-crystalline material. And look at this. This is for you, an artistic rendition of clusters. Of clusters of the blue ones are the glycerol groups, these clusters of between five and eleven molecules per cluster prior for it to be coming lamella, showing it's not There was another group, of course, from Marjorie and at Al, who showed that by using X-ray scattering that this was the case. We actually see clusters. We do not see lamella. So really cool. Hopefully we'll get this published soon. And this is the cheese stored. Would you believe that those are different cheeses in the different ranges? We're trying to figure out what's going on with the different power slopes. These structures are a little bit complex to understand. So hopefully one day we'll be able to tell you what a good old cheddar is versus a younger cheddar. But going back to the task at hand, let me talk to you very briefly about enzymatic glycerolysis of oils for structuring it. Let's start from the very beginning. You know that they're solid fats and liquid oils. They're both trisor glycerol molecules, glycerol with three fatty acids. Of course, the melting point of solid fats is higher. So they're solid at room temperature. You see here coconut oil, cocoa butter, and this I guess is palm oil or maybe palm kernel oil. You see the little palm fruit with the kernel and the flesh, right? Palm oil comes from the outside and palm kernel from the inside, from the seat. Now the liquid oils, they have unsaturated, polyunsaturated fatty acids that give the triglycerides a lower melting point. Hence, they're liquid at room temperature. We know this dynamic. Here's an example of solid fats versus liquid oils and their saturated fat content. Just FYI, so that you don't think that a liquid oil is completely unsaturated. Coconut oil 80 to 90% saturated, palm kernel oil 80, cocoa butter about 60%. You would think it'd be higher, right? Palm oil about 50% saturated fat. But look, surprisingly, a little bit, cotton seed oil is 30% saturated fat. Coconut oil, like you call it coal-sized things, 7% saturated fat, where soybean oil, soybean oil is high, 16% saturated fat, and peanut oil and rice bran oils in between. There's a variety of saturated fat contents even in the liquid oils. This saturated fat content leads to crystallization of the material when you're undercooling it, of course, below the melting point or the highest melting triglycerides. You have an actually a profile of solid fat content, the amount of crystalline mass usually determined by NMR as a function of temperature, right? And the unilever where the pioneers in this area of using this parameter to relate to some sort of functionality of the fat.  So here you have, for example, coconut oil, here you have palm oil, and even with the liquid oils, they're mostly flat and they don't have any solids as a function of temperature above refrigeration temperatures. But some of them do. For example, look here, peanut oil and cottonseed oil, they do reach about 8-9% solids as a function of temperature. And of course, there's all kinds of functionality associated with this.  So the more liquid oil you add, the more you lose this solid fat. Now this is a very like thermodynamic view of things, amount of crystalline solid. We've done a lot of work in the area of crystal structure, and this doesn't take into consideration crystal structure whatsoever. You can have the same amount of crystalline mass and have a large crystal or a small crystal and you get completely different rheological and or functional properties. But that's a story, I guess, from years past, even though we continue working on that. Now margarine shortening, I mean, you've heard a lot about trans-fat-free. People do not want hydrogenated fats. So we all went in 2006 and replaced all the hydrogenated fat and partial hydrogenated fat with palm oil, ended up with food products with a ton of saturated fatty acids, which may have deleterious effects towards cardiovascular health if consumed in very large amounts. And so is there like a next step? How can we then, if we do not want to have so much saturated fat and we do not want to have palm oil, which has become a driver these days, what do we use? Well, and let me just add another aspect to that. Now we have this whole area of the vegan food products, the plant-based foods. For example, you have three examples here of some common brands, there's a lot of these going on. If you want to add fat to these things, do we add a lot of palm oil to it? And then we have issues with sustainability. But do we want to put a lot of saturated fat into these allegedly healthy food products that go to fat for all of these guys is coconut oil, maybe mixed with a little bit of liquid oil. Everybody uses coconut oil because they want to avoid having the sustainability issue of palm oil. And it's the only solution right now and it's cheaper than cocoa butter or shade butter, which could be another option for these things. And so let's talk about health and sustainability a little bit, because this is driving a lot of the research that we do in our lab right now. We talked about saturated fat, so you're going to put coconut oil in it, or maybe you put palm oil in it. But at the end of the day, what have we learned from saturated fat?  It will also raise your total cholesterol, but it also raises your good cholesterol. So when you take the ratio of good to bad cholesterol, it's fairly neutral in that respect. Now, the monounsaturated fatty acids do not really affect the bad or total cholesterol. They raise some of your good cholesterol. So this negative trend from the monounsaturated fatty acids is considered to be beneficial in terms of a decreased risk of getting cardiovascular artery disease. That is, and if you replace the carbohydrates with fat at isocaloric levels. One man's thing told me that you have to qualify these things.  If you replace it with protein, you may have a different story, but this is how this is done. And the polys were always good. Polyunsaturated, linoleic, linoleic decreases your bad cholesterol, decreases your total cholesterol, increases your good cholesterol, and this decrease in this ratio of good to bad is, sorry, bad to good, is good, is even further. So polys have always shown from 1957 with quiesce's work, this has not changed. And this is what men's thing finally wrote for the World Health Organization in 2016. There's really no issues with this concept that have never been. The only issue here is that saturated fatty acids, when you take the ratio, are fairly neutral. So are they bad? The question is, if you have high cholesterol above 200, I would say milligrams per deciliter in your blood, you do not want to eat anything that raises your cholesterol. Because you're outside the range where you should be concerned about this ratio. You should be concerned about your total cholesterol levels as well. If you're within a normal range, sure, let's talk about this. So this is where the saturated fatty acid story comes with health. You know, nutrition is always complicated. Now what about sustainability? We spend a lot of time thinking about sustainability these days. We do not want to do in our lab research any more that doesn't contribute somehow to trying to prevent some of these things happening in the world. We know I come from Latin America. You know that the whole Amazon is being cut down to put pastures for beef.   We cannot lose the Amazon to like us because we're going to eat steaks. We just can't do that anymore. I mean, we can't do it, but we won't be here in 100 years. And so the last rainforest, which are the cradle of biodiversity, the Amazon rainforest, the Congo Basin, some of the Southeast Asian forests, you've heard about Borneo, that there's very little left of this. I don't think there'll be much of the Amazon left in the next 20 years. And so this is how, for example, Malaysian Indonesia looked. There used to be a lush forest and let's not be like stand on soapboxes here. In North America it was done, in Europe it was done. The native forests were cut and they put in whatever. But right now what is happening in the tropical world, this is happening right now and that's where biodiversity is and it's a bit of a crime. Now, on the other hand, for example, palm oil, even though you're cutting the trees down and putting the palm plantation, it is the most productive plant. So per hectare, it produces, look at that, 3.3 tons of oil per hectare versus any of the other guys, which are, for example, soybean oil, 0.4 tons per hectare. So you need a lot more land when you're growing soybean for oil than for palm oil. So that's on the posited side. On the negative side of palm oil here, there's a complex graph, okay, is species threatened per million hectares of palm plantations. And it's about 17. So look at how negative the effect of having a palm oil plantation is on biodiversity for the threat to species. Now look at coconut oil, it ain't that much better, I mean, yeah, it is three times better. But it's still quite a bit worse than, for example, olive oil and some of the peanut oils and some of the other ones around. So because the tropical rainforests are the cradle of biodiversity, if you put anything in it, a road, a building, a palm oil plantation, you're going to be threatening a lot of species. So this is at the core of the problem of palm oil and why Europeans particularly have been very active saying, no, we don't want any palm oil. Whether that's right or wrong, that's not my place to say, I'm just pointing out to the fact that it has a strong effect on biodiversity, even though it is the most productive plant for that. So you would think it'd be good, right? So we don't want to have saturated fat because of the health issue. Let's say we said that palm oil is a bit on the wrong side of the fence here. We have done a lot of work in the area of fat nematics and that's still a very, very important area these days in which we take liquid oil and we structure it using self-assembly mostly and we explore using the techniques we love to use. So I'm not going to talk to you about this either, but this is a review we did in Soft Matter recently and you can have direct oil structuring. So you put in the liquid oil, the magic sauce, and it could be a polymer like ethylcellulose or maybe chitin. This is a network of ethylcellulose polymer that traps oil within. That's sort of a physical entrapment within something that looks very much like a hydrogel, the ethylcellulose oleogel. Or you can add these magic ingredients. You've probably heard about the hydroxylated fatty acids. For example, brincinolate acid or hydroxysteric acid. They form these fibrous structures, some of them, the very famous cytosterol, cytosterol and oryzenol that Aaron bought at Al developed at Unilever again. They have a patent on it, but there's many others, right? So there's fatty acids, partial glycerides, natural waxes are big, fatty alcohols at low levels, let's hope less than 5%. And you get something that looks like a fat, whether it's edible or not, or allowed, that depends on the regulators, whether it's cost-effective, big problem, whether it has the rheological properties, another huge problem. These things still need quite a bit of work. Are they natural? That's arguable. They could be. You can have structured by-facing systems. So if you have an emulsion, for example, a gel emulsion or a structured emulsion, we have worked a lot with monoglyceride gels, which are basically monoglyceride mesophases, which trap oil within, which are dispersed within a water medium, and they look like a fat. That's another option. Or you can have indirect oil structuring via solvent exchange, or by using emulsion templates or foam templated, for example, polysaccharides can be used in order to physically trap oil. So lots and lots and lots of things that, so you could think that this could be the fat of those vegan foods. Sustainable, natural, who knows. No, it's a complex question. But what about giving you another option? What about glycerolysis? This is nothing new because glycerolysis was invented a long time ago to make monoglycerides and by-glycerides. What is glycerolysis? You take a triglyceride, you add glycerol, which is a trihydric alcohol. You can add a chemical catalyst or an enzyme. In our case, we used Candidantartica lipase. You can use Mucormehyde lipase. But in the end, what you end up making is, notice how the fatty acid at position three, now it's located here. You have what used to be the glycerol, now you have a monoglyceride, and you're left behind with a diglyceride. Eventually things will go to their equilibrium state. And yes, there's a lot of acyl migration. We have tried one, three specific lipases. They do not look that good, just because of acyl migration issues. That's another question. But look, you end up with a mixture of diglycerides and monoglycerides. Let's not cover this. So what is important about monoglycerides and by-glycerides? Think about this. We have the same fatty acid composition. We still have R1, R2, R3. R1, R2, R3. It has not changed. Hence, you don't have to label it as modified because you haven't changed the molecular composition. Isn't that a loophole or what? And secondly, monoglycerides and diglycerides have higher melting points than triglycerides for the same composition, sometimes over 20 degrees higher. So what have you done? Even though you may have a liquid oil with some saturated fat, you convert it to the partial glycerides, and now you have a fat because you've raised the melting point. And you don't have to tell anybody that you did. So in the first part of our work, we just try to optimize the reaction conditions. We were lucky to get this into nature, into nature food. You know that nature has a new journal, which is part of the actual nature family. And here we did, and they want it. They're very aggressive about this. They wanted to have that long title that we want to replace palm oil and food products. We just wanted to make glyceralysis products, but propaganda is good sometimes. And so we try to optimize the reaction. So notice how in time, this of course is like 72 hours of reaction, but we do that because we had low enzyme loadings. You can optimize this. You can go into a plug flow reactor or semi-batch if you wanted to. Don't focus on that. But look at the conversion of diglycerides, how they change in time. Monoglycerides, how they change in time. And as a function of reaction time, but also the triglyceride to glycerol molar ratio. Always we found that a one-to-one was the way to go in case you're interested in doing this reaction, one-to-one, multi-mol glycerol. And the commercial enzyme, candidate Antarctica, mix, mix, mix, mix. You can do it probably at home. And you obtain the reaction after just a few days. I'm meanable to do in a small scale. Interesting, interesting technology. What else? So here we actually monitored actually the formation of mono-oldian, mono-palmitin, mono-lenolian. That's 18-1, 16-0, the saturated, and the 18-2. Again, as a function of reaction time or glycerol to triglyceride ratio, notice the expected increase as a function of time of the three species as the reaction continues. And interestingly enough, a bit of an optimum, again, at this glycerol to triglyceride ratio of one-to-one. And why not? Of course, we measure the solidified content as a function of time and notice the expected increase both at five degrees and at 20 degrees. I did not tell you, this is cotton seed oil. And again, the glycerol to triglyceride ratio, the solidified content peaks, actually. In the case of five degrees with plateaus, 20 degrees for some reason right now that become obvious in a bit, we actually have a peak. For this, again, showing that the one-to-one ratio is the way to go. And by the way, the reaction was run at 60 degrees Celsius. So at 60 degrees Celsius, the enzyme is at its optimum and you're not overheating the oil and you're keeping it also safe. Notice just a basic practical test on oil loss as the reaction continues and you make the monodiglystrides, the ability of the fact crystal network to trap oil increases. And you have here this plateauing after about 48 hours under the conditions we did. Interestingly, the glycerol to triglyceride ratio at the one-to-one ratio, we had no measurable oil loss based on, we put it in a little centrifuge tube and spin it for about 30 minutes at 10,000 Gs and see how much of it separates up. And this is how we do this at room temperature. So it's harsh conditions. If we do a differential scanning calorimetry on this again as a function of reaction time or glycerol to triglyceride ratio, this is cotton seed oil. Notice this in a differential scanning calorimeter. Notice the onset of crystallization at around five degrees and as the reaction proceeds, we see the growth of a peak associated with the monoglycerides. So you get this high melting peak coming up as the reaction continues and there with the glycerol to triglyceride ratio, interestingly enough, the one-to-one has the arguably a little bit higher crystallization peak as well. So there's definitely an enhancement of the minimal enhancement over the other glycerol ratios at that particular glycerol to triglyceride ratio, which has been corroborates all the other information that we got before. There's nothing fancy schmancy here. In terms of isothermal crystallization, we fit the crystallization curve to an Avrami model. Derived the rate constant and the Avrami index, but combined the two in a half-life or a half-time of crystallization. And notice here how let's just look at one temperature, let's say 10 degrees Celsius. At the particular ratio of one-to-one mole to triglyceride to glycerol ratio, we have the lowest half-time of crystallization. So it goes faster. It all points out to higher melting peak, lower half-time of crystallization, higher rate constant of crystallization. What would we expect at the one-to-one ratio to have more numerous small crystals for the this amount of glyceride, by the way? This is the glycerol is a soil. It makes sense that you have a greater nucleation rate at that particular glycerol to triglyceride ratio and the photograph say you don't need to analyze this. We did with a box-counted fractal dimension and of course the space fill, that's all that number says right now, by the way, I'm not implying fractality, but just the space fill and it's more filled, right? With crystalline mass. So that's good. It's good for actually making industrial fatty products to have a lot of nucleation and not have these gigantic crystals, which then can actually stay separate from the liquid oil. And let's look at some of the optimized conditions for cotton seed oil. Look at the enormous difference between the liquid oil and the cotton seed glycerol as it's brought up, we're up to 30% and it has a very gradual decrease to about 55. That is remarkable. I have not added any saturated fat, any trans fat. I have not hydrogenated. I have not added some weird self-assembled molecule that nobody wants to approve. It's just transform it from the monos and from the triglycerides to the partial glycerides. A bit of a picture of the structure and then here for you some sacks and wax of these things, you actually see the lamellar, the formation of a lamellar structure for the monoglyceride. It's a lamellar structure. So you get one, one-half, one-third, one-fourth. And then the wax scattering pattern is characteristic of a triclinic crystallizer. So it's in the thermodynamically most stable form. So you don't have to worry about meta-stability. You know the one, the beta prime, beta thing they talk about fats, it's not going to a beta prime. It's not going to our therombics, not going to meta-stable. It goes directly to the most stable form, probably because it's diluted in oil. So phase two was to, the project was to go into the why not try all the oils in the universe? That's a bit of an exaggeration, but here we have cotton seed rice bran, tiger nut.  Check it out. Peanut, sesame, soybean, olive, canola oil, high oil lake, canola oil, high oil lake, olive oil. We emptied the shelves in the lab. And here's the saturated fat content of these things from super high to low and your lake acid contents. And let's rip through them, the enzymatic glycerolysis reaction of all of these. The gray lines are the original liquid oil. The black lines are the glycerolysis product. Let's not look at it too care, like too much, but look at how black has a high melting, high melting peak, high melting peak, high melting. They all get high melting peaks. Even oliveing has a nice, now, olive worked fairly well. That should get some people excited about having olive oil spreads. Of course, they have different microstructures. We just put them there for the record. But difficult to, right now, there's not enough time to talk about the relationship between microstructure and properties. And again, from the X-ray scattering patterns, all of them 4.6, 4.6, 4.5, 6, 4.6. They're all in the triclinic crystallized in the triclinic form of the monoglyceride. And you can see here the lamellar structure of the monoglyceride. So it makes a lamellar crystal, all of them related to their average fatty acid composition in the triclinic form. But do not forget, we also have a secondary peak for diglycerides as well. So we actually have monoglycerides and diglyceride crystals present in these mixtures. We'll get back to that in a second. Here are the solid fat content versus temperature profiles for all those oils we looked at. Here's the tiger nut oil, up to 35% solids. Very interesting. It's a grass, by the way, with a, maybe you've seen. Here's the difference between the liquid and the solid. Notice the increase in peanut oil. The very, very interesting increase. And cotton seed oil, we saw that before. Look at rice bran oil goes up. Olive oil went up to 25% in refrigeration temperatures, maybe not solid enough at room temperature for a fancy margarine. High-lake canola oil, soybean oil increased an interesting extent. Maybe we get enough crystals for pickering stabilization under refrigerated conditions for your little dressings, right? And multifire-free stuff. And sesame oils, canola, and the high-lake algal oil didn't go up at all. But notice, potential for very different ones. Maybe once we develop a greater understanding of more molecular detail on these, we can actually design one with the most amount of solids. Also notice cotton seed, how it has a gradual reduction in solidified content as a function of temperature, while peanut and tiger nut have more for two-stage. We were coming to some sort of conclusions about that. What defines this two-stage melting versus the more gradual melting? And here we have a very high-lake acid content, and here we have a low-lake acid content. So the ones with low-lake acid, more gradual. The ones with high-lake acid, more two-stage. And rice bran oil is somewhere in between of the two. Is that completely true? This could be one of those correlations that are not cause and effect. So don't put too much emphasis on that. It's just a preliminary step. So the important points to take from this is that saturated fatty acids contribute to the solidified content of five. It's good to have an oil with high-saturated fatty acid content. Olake acid contributes to SFC at five when saturated fatty acids are present above five percent. So olake acid is good, but you need to have some saturated. And very high-lake acid contents are required to achieve a high level of solids when saturated fat content is less than 10%. As we said, that goes along with the second point. But if you have more olake acid content in your mixture, you have the more abrupt two-stage kind of decrease of SFC as a function of temperature. Now, we also have this. This is complicated to talk in a short talk like this. Molecular interactions between the species present there. And I was just going to point out to a few of these. For example, if you get monopalmitin-16-0 and monosterin, you have ideal solution behavior. You have ideal mixing between the two solid states. We're talking about solid states when it's in the alpha polymorphic form. But in the beta polymorphic form, you have eutectic defense. So you have now a decrease in the melting point. So you have to keep in mind how much CaT monoglyceride versus C16 monoglyceride you have. If you're wondering what on earth this is, this is a subalpha phase, another strange phase that the monoglycerides do. Monoleon and monolinoleon. Well, let's not talk about this little one here. They have fairly ideal mixing behavior. There's mixing in a solid state. Monopalmitin and monoleon, what's the deal with that? Notice here, this is the melting point of monoleon. And you can actually incorporate up to almost 20% monopalmitin in the monoleon crystal. And beyond that, they completely separate and you see two melting peaks. And you have the appearance of this high melting peak. And we believe that the appearance of a high melting peak is positively correlated to enhanced structuring.  That's what we learned from these two. So we can actually begin thinking about designing a fad this way. For example, it gets more complicated. Dioleon and monoleon also have very complex mixing behavior. And notice that they're miscible. I mean, as you're adding more and more and more monoleon to dioleon, they form a mixed crystal. And then after beyond a certain point, 50% call it, the monoleon separates out and forms that high melting peak. So we should probably be above a ratio of 50-50 weight by weight of monoleon dioleon. Sorry for not putting in terms of more. And again, you have dioleon monopalmitin. I'm probably completely confusing you here. Again, showing similar solubility at certain levels and then separation of the two species, which is what you want in order to get enhanced structuring. So it's important to understand the interrelationships between monopalmitin, dioleon, and then, of course, a different species. You should know all of them. We have done this with two, with three, and with four components as well. Hopefully this will be published in crystal growth and design soon.  But we can learn some rules about it on the way. Very quickly, applications. That's a tiger nut. It's a bit of a grass. And that's a tiger nut seed. And that is a margarine, a commercial margarine made by Script Surface Seed Exchange. Here is a commercial margarine of five degrees. And here is butter, this light line here. And this is our commercial tiger nut glycerolus' margarine. No emulsifiers and no saturated fat, other than the one that used to be in the tiger nut. And here it is. This is a back extrusion, by the way, profile. So when you extrude it into a cylinder, it comes out. And it's a good idea. It gives a good idea of plasticity and of hardness. What about the melting behavior? I think the commercial margarine and the tiger nut margarine look very similar. And solidified content versus temperature profile. A tiger nut crashes a little bit before the commercial margarine, but that may not be bad for a fridge-ready kind of product. The solidified content is relatively low for both at refrigeration temperatures. So maybe if you're in a warm country or maybe a hot day in the Netherlands, you will maybe become a little fluid liquid. But we, you know, in combination with other, maybe a little cotton seed oil, will give you the profile you want. And this is after 10-month storage of the tiger nut margarine. We still see the, you know, the regular structure of a margarine product without too many changes and with the required stability.  Here you have 50-50 coconut sunflower in the open triangles. Here you have tiger nut glycerolus' product. I would say it's even more advantageous than the blend. So we're beginning to incorporate these in all the vegan products because I think it could replace coconut oil without being palm oil, without being hydrogenated. And look at this, stabilization of peanut butter. So if we take peanut oil and we do a glycerolus' product, now we have something with salt that doesn't separate out and you don't have to add hydrogenated fat. The only problem is that you have now the oil and the flour. So we actually ran the reaction in ground peanuts and it worked, but it takes a long time. But it wasn't impossible to add glycerol and the enzyme right into the mixture. But you do not have any more separation. The annoying separation of natural peanut butter is obliterated by using the peanut glycerolus' product. So in future work, we need to define the critical molecular species necessary for maximal structuring. A bit of those phase diagrams that I probably will board you to tears with. We're going to start throwing this thing in the USACS machine because we'd like to understand the nanoscale structure of the glycerolus' product like we did for the triglycerides, if you remember our work. Are there nanoplatelets of these things? So what is the nanoscale structure of these? And then relate that to rheology, right? To both South and Laos like we did for the triglycerides as well. And just keep on exploring more applications in all food categories, in relationship to itself, emulsifying properties, pickering, stabilization. And I would like to see how it works in lamination, hence plasticity, but I'm not holding my breath there. That's almost a shangri-la of all fat work. Can you make a good croissant with that? And with that, I thank you very much for your attention. Thank you, Alessandro, for a very interesting talk. There is at least one question here, and I want to know how much free fatty acids is produced in the process. If you get that, do they influence the melting behavior? One percent at the end of the day is the one left. So we try to keep it within... We also did a work in which we removed it. And actually once with the free fatty acid enhanced structuring better. But so you can leave them in at one percent if you want to, or you can do a little bit of physical refining some bleaching, if you want, some bleaching earths. But we didn't find anything negative with having one percent free fatty acids. Okay, thank you. I have a quick one also. So, Alessandro, your glycerol and triglyceride ratio of one that's optimal and so on. I suppose it's a basic mechanism of things have to find each other, so it's more kind of a geometric effect. But then in your slide number 26, wouldn't you then expect the numbers to be symmetric when you have half to one and one to two ratios? If you get my point. Yes, the only thing I have to say about this reaction is that we still do not understand the dynamics of the reaction completely. And remember that we only saw it for a certain amount of time. So there's a lot of change that happens before and afterwards. One could argue that maybe water accumulates and you start getting hydrolysis. So you have competing hydrolysis reactions as well happening. So yes, they should be symmetrical. They should be symmetrical. And you have this acyl migration issue, which is gigantic, right? And so we didn't know we would expect with the other equilibrium, but you could also be impartial. You're not reaching the complete equilibrium for the positional distribution.


Hi, I'm Peter Burris and welcome again to another CUBE conversation from our wonderful studios here in Palo Alto, California. Another great topic to talk about, we've got Robert Schmidt, who's the chief IoT technologist at Deloitte. Welcome to theCUBE, Robert. Thanks for having me. You also have your own video cast. So, why don't we get that out of the way? What is it? Yeah, every Friday at 9 a.m. Pacific, I do a show called Coffee Chat with Mr. IoT and Misconnected. I just actually added a co-host. I thought I needed someone to help me. And we talk about IoT, it's on YouTube, you can find it on the channel, and it's really odd for me that you're going to ask me the questions and I'm going to have to answer. So, I'm going to try to eat my own advice here and be short. Well, maybe someday you can add one of the Wikibon folks in your podcast, or video cast, we'll have to do that. All right, let's start here though. Deloitte's a great name, been around for a long time, associated with customer value in very profound ways, complex applications, that certainly characterizes IoT. What's going on with IoT and Deloitte? For us, we started a whole practice around IoT and I'm leading that practice, but the thing for us was, there were a lot of science experiments going on around IoT, technology-based, but we really wanted to bring it to, what's the value behind IoT? So, we really focused on use cases and today we see that the most focus is on industrial IoT, though we spend a lot of time around connected products as well. I personally actually today work on a project in a factory in Chicago, on a shop floor, connecting machines and measuring data and providing value. I work with an airline at an airport around their travel, so really helping guide you throughout the day. Interesting fact, you know, we swipe away a lot of notifications without actually doing anything with it, but when an airline tells you, please come in 10 minutes early, the TSA wait time is long. I know you and I- You pay attention. Yeah, we got to be there early, so we actually react to those notifications, so I work on that and I work with high tech companies around their platforms, how do we make their platforms better, so. You know, you've raised a lot of really, really important issues, but let's start with this notion of use cases because a factory floor with a lot of PLCs, spinning out information, mediated by individuals or users and the data, where does it end up, and that's real different from an airport where a lot of the data's being generated by a human being as they move places or as intended to be consumed by a human being. What kind of common patterns are you seeing in these use cases that brings them all under this notion of IoT? I always think of IoT as taking sensor data and making decisions based on those. And what's interesting to me is that it creates this really interesting dilemma that we thought we knew what goes on with users, how they work and what they do. We do surveys just to find out what they're saying. The survey is actually probably not what they do, but now we sense as we know what they do, all the way to machines where we have decades of people having experience about, this sounds a little odd. The machine doesn't sound right, but then they don't know what to do with it and now we can measure that. Because really at the end of the day, vibration isn't anything else but sound, right? And so for me, this is all about and what's common about this is that we really take that we think we know to we actually know, because we cannot measure with sensor what goes on in that area. So it's almost like taking a lot of that time, motion, analysis, operations, research that we used to do periodically, episodically, with human beings doing their best to record stuff and then bringing a lot of that discipline continuously and in real time so that it can better inform overall decisions, right? Yeah, I mean, almost near real time in many of these cases and that's a really interesting scenario for me, right? Because now you can actually see what happens in the factory when I tune the mix or the blend of my raw materials, what happens to the product that gets made at the end of that. So as we think about the challenges or the changes that we foresee going on, is there a difference in thinking about humans as users or humans as consumers of a lot of this data and machines? I know there is, but how is this, because kind of the machine side has always been associated with SCADA, OT and the disciplines and the approaches for that side seem a little bit different than what's coming out of the mobile world, which is still very, very closely associated with how we utilize or how we deploy these systems to inform decisions in either case. Is that right? I don't really know if we do so much about decisions for machines. I think at the end of the day, many of the decisions are still made by humans. So I mean, I think of this like, we have an overheating element running over.  But there's still automation that takes place. Absolutely, there's automation. But automation takes place today. Sure. None of this is particularly new. I mean, OT has done automation forever, right? I think the interesting part is now, taking the learning and connecting the different data points together. So I talked about the factory floor. I just showed actually at the show, we created a virtual factory line, life size. You can download it. It's the virtual factory by the Lloyd. If I get my phone going, I can show you, but it's not right here. I call it the Internet of Rubber Ducks. And the Internet of Rubber Ducks. The Internet of Rubber Ducks. It's kind of cute. You have these little yellow ducks and if you load the app, you can see them being made. But it's actually really what goes on at the factory and it really shows how when you change the blend at the beginning of a production line, how it affects at the end of the factory line, the outcome, how much scrap you have, so what's the scrap, what's the overall equipment efficiency, OE, and so forth. So what happens is now we can connect data from the very beginning of the factory line with the very end of the factory line and then combine that with contextual data, such for example as temperature or the vibration on the machine or the current, which we haven't done before. So this whole time series of data that we not correlate becomes really critical and I don't think that's something we've done really as much before that has not driven automation. So if we think about it, we're talking about sensors, which as you said, GATE has been around for a long time and it tends to automate very, very proximate to where that sensor tower might be, but a lot of the information that went into decisions was actually then generated by a person, perhaps a shift supervisor or somebody else or a machine operator said, I heard a rattle, but there's no time, so it's difficult to correlate and now we're talking about up-leveling a lot of that information, so it becomes part of the natural flow out of the machine, but still for human consumption to make decisions. Yeah, very much like that. As I said, I talked about the blend of the materials that go in and then now we can correlate that particular part of the sheet. We can look on video and see how it looked and check the quality and then see at the end how many pieces of productivity we produce. Actually, in that particular case, it's really fascinating. It wasn't so much about reducing cost, it was actually increasing output. For them, each line cost about 10 million and with the findings we have and what we're doing with them, we can actually give them the ability not to build another line, but actually produce more lines because they can sell more, which is a great position to be in. So you can actually impact the top line rather than just the bottom line. Well, productivity fundamentally is a function of what work you can perform for what costs are required to perform that work. And if you can improve the effectiveness of something, keep the cost same, but get more work out of it, that's a big, big plus on the bottom line. And they have to market to sell it into, right? Absolutely. Because even if you just make more and you can't sell it. Well, there's that too. Yeah, which is really the great thing about that. But to talk about how, for example, you noted that they can look at a video of how the plastic or the sheets coming off, the machine off, set of rollers perhaps, but how does AI start to be incorporated into this IoT discussion? And what kind of use cases are you seeing becoming appropriate or more appropriate or made more productive by some of these new technologies as we bring some of the analytics and some of the IoT elements together? So we find that we do a variety of theories. We go in and say, how about this? How about that? And then we have our data scientists go and look at models for that and see what goes on and then put machine learning in. And then we take those machine learning models and feed it back into, we talked before a little bit about this, but edge processing is really something where we now process some of those models on the edge. So the algorithm development and all the analysis, we send that to the cloud. We do number crunching there and we really take advantage of the unlimited capacity. So a lot of training happens up at the cloud. A lot of the training happens in the cloud and then whatever models come down, we load those on the edge and we actually do make decisions right there on the edge or we give the operator the choices to make the decisions right there on the edge. So training up in the cloud, but the inferencing actually is approximate to the actual action. So there's locality for the action based on what's in the model and there's a lot of training that can happen. Quite frankly, we don't have to underwrite the cost of the infrastructure to do it. Exactly. So that suggests that there's going to be a fair amount of change in the industry over the next few years and this notion of moving from O-T to I-T or skated to I-O-T, this is not just a set of technology issues. There's some fundamental other questions that are going to be important. A lot of people just kind of assume, oh, we'll throw a bunch of general purpose stuff at these I-O-T related things and it's going to be the I-O-T industry or the I-T industry all over again. Or is really the expertise associated with the use case going to be more important? How is that use case going to be ultimately realized? Is it going to be a bunch of piece parts or is it going to be more of a holistic approach to really understanding the nature of the solution and making sure that the outcome is the first and focal point? I'm going to come back to your question a second. I just always, I have to smile because so I have a master's in petroleum engineering. And so when I studied, I built really fancy models like differential models, integral models and you know, I simulated fracturing process controls built with that stuff. So I lived a good part of my life in I-O-T and then after I came out of university, I really moved more and more into I-T. So I've spent most of my career in information technology including being a CIO. And I always thought that the most fancy math we'd ever do is percentage calculations and that was pretty fancy. And so now I find myself in this awesome place where I can bring together some of that O-T, some of that real deep data science work that I did early on in my life. Now with some of the process and the system implementation expertise and practice that I come out of I-T and they really come together. I don't think one takes over the other. I think there's this real sort of like meeting each other and going like, wow, okay, I guess we really got to work together. And so that's really fun. About your question around what solutions do we see today? I see a lot of very vertical, very one use case oriented solutions that go all the way from the sensor to edge to cloud to hopefully integration to the back office systems because without that you can't really take good action. But they're very narrow. And so like in the good old cloud days when cloud became really big, there were really good point solutions and the good cloud providers sold to the business user right then and then and ran around I-T. And I see the same in I-O-T happening right now. You get a very good solution for temperature control on a truck, for example, which is a very narrow solution. But the moment you want to start doing something with your warehouse where you have other sensors and you need a horizontal platform, those vertical solutions fall short. And so that's what I think is sort of like the interesting dilemma right now. You have these vertical pillars and you have the horizontal platforms that the big providers have. And so it'll be interesting to see when we're going to see some consolidation in this space when some of the vertical solutions going to get bought up by the horizontals to provide better use cases. It's a little bit like the ERPs who did every industry and then eventually they realized we need industrial, industry focused solutions. We'll see the same in the I-T space. Well, the I-T industry has always supposed that we can transfer knowledge we gain in one domain into other customers, into other use cases. And it almost sounds like what you're saying is we're going to have that vertical organization of expertise, which is absolutely essential to solve that complex core business problem. High risk, high value, high uncertainty, often bespoke, never done before. But over time we will see a degree of experience sharing and diffusion so that over time we might see better, more applicable platforms that are capable of providing that foundation for a broader set of use cases but that's going to be a natural process of accretion. Is that how you kind of see it? Yeah, I mean, we're all going to need streaming capabilities, we're all going to need capabilities for machine learning, for cognitive, for video analytics. We'll all need that but I think it'll be specific to the individual use case in a sense of, I'll give you an example. I just had a scientist show me how he started looking at 20 year old scientific research on gearboxes. What frequencies happen in gearboxes specifically to certain scenarios? That's not replicable from a gearbox to a pump. You know, you have different, so there is specific things and yes, it might be the same gearbox in one factory that produces, I don't know, rubber ducks to another factory who makes metal sheets but it's still gearbox specific, right? And so I think this is the specificity we're going to see around models, around learning and around sensors in a certain extent. Excellent, Robert Schmidt, chief IOT technologist at Deloitte, thanks very much for being on theCUBE. Thanks for having me Peter, it was a pleasure. Thank you.


Intentionally, a little bit further.  Keep that glutes on, right? Glutes are on. What's going on guys? John's about to kill me here. So these are the Nordic curls. It's the end of the exercise. I'm dead. And enjoy for my pleasure. My pain for your pleasure. What is good? Today is all about the Nordic curls and how you can do them correctly and what I learned. So let's go! You happen to have just landed on a goldmine of information. John coaches me through the Nordic curl and the things he's telling me as I'm doing it will help you so much. And we also do RDLs. But just Nordic curls and just RDLs today fired my hamstrings, my glutes. I have so much work to do. And I really hope you guys take a look at this video and really learn from it. Because there's so much knowledge here and I hope to bring you these videos every single week.  And you guys can learn from me so you don't make the same mistakes. But you can also make the next level progress we're all trying to make. So let's go! My goal of this year is to just get healthy and then to go higher than ever at 43 inches max vertical. I have a ton of videos on my channel. Check my dunk tips and tutorials playlist. If you want help on your technique, your footwork and anything jump related, I probably have a video on it. But either way, leave a comment. Let me know what you want to see. If you have any questions, I'd love to help you guys out. That's what it's all about, baby. And lastly, stick around to the end. I'm going to give you my insights, what I learned from today and the things I think are going to be keys going forward. So just today I had my session with John Binford. I've been working with him for the past couple of weeks. He's really helped me so much and I'm really excited to break it down. He coaches me through this exercise. He shows me how to do it correctly and teaches me. It's really tough to get it correctly. Let's get into it now. No more. Because I have this yoga block in between my knees and I'm squeezing it tight with my adductors, I'm going to be able to find and utilize more of my hamstrings because of that activation of your adductors. So from there, we've seen that your hamstring at a more lengthened position is weak. We've suffered a partial tear at some point and we're recovering and building strength back from that. And you're at a point where it's at a performance grade where you can perform but not optimally, not at your best level. So we're going to try to find that injury of the hamstring or that length of the hamstring where it's nasty and weak and we're going to train the shit out of it with isometrics first, then eccentric, then eccentric and concentric and explosive type exercises. But we're building foundational strength with isometric holding like a plank. If you want to build core strength, what do you have to do? Practice your planks really initially. So I've got my block, squeezing my block. From there, I'm going to work to pull up on the pad. I'm pulling on that pad the whole time in this isometric exercise. I'm trying to engage everything in my hamstring and I'm testing a shortened hamstring right now. Lengthened is what bothers you. When your hamstrings are lengthened and they try to contract, they're weak. So we're going to find a halfway lengthened hamstring and hold and squeeze. 90 degrees, I'm trying to keep my butt tucked in, glutes are tight, adductors are squeezing and I'm just holding. Pulling that thing off the rack. Pull, pull, pull, pull, ten second hold. Five, four, three, two, one and release. At that length, then we're going to see how that feels, test, make a hypothesis, test the hypothesis, make a conclusion, okay, how that feel and then we'll test it again. A little bit lower, a little bit lower, a little bit lower until we find that hamstring where it feels like we're getting stronger and getting better. But if we do any more, we could possibly get injured. You're trying to find that, you know, yeah. So we get into the hamstrings by bending the knees and now we're shortened because the knees are inflection. So you're crushing the block, well adductor, right? So that's telling you again, groin, got to address the groin. So keeping our glutes tight and we're coming down a little further, right there, good. And we're just past 90 degrees of knee flexion, right? And now we're thinking about pulling on the pad, light hands, glutes are tight, right? Ribs are tucked in and glutes are in, right? Hold and pull. 10, 9, 8, 7, 6, 5, 4, 3, 2, 1. Relax. Good. Come on out. Any couple RDL's in between? Come on out. Just some light reaches. Don't strain the hamstring because of the length, right? Because we've saw that lengthening is strenuous or just trying to kind of reset right now. So not even to the point where you start to feel that ham, just give them some length. Give them some love. Think about what we're doing with the RDL, right? We're bending, finding length in the ham, and then hip extension, right? Now you're hip extension and you're adding knee flexion.  And I'll hit about 5 to 10 RDL's and then I'll come back in. All right? So let's do that. Let's grab a kettlebell. We're going to hinge, hinge, hinge, maintain the shelf, push the butt back. Good. And back up. All right. So last time, our knee flexion was about 100 degrees of knee flexion, right? Which is like an obtuse angle. Right now you're at a right angle. So we want to create an obtuse lengthened angle. Go ahead and start to walk out. And you're pulling on your pad as you're walking out. You're crushing your block as you're walking out. All right? And we're going to go just a little bit further. Intentionally a little bit further.  Keep that glutes on, right? Glutes are on. Glutes are on. Abs are on. And pull as hard as you can, ramping it up about 50%, 75%, 100%. Hold. Eight. Seven. Six. Five. Four. Three. Two. One. Down. Great job. Awesome. Come back out. Hit one more set of the RDL. Woo! Cramping muscles are deconditioned muscles. Not necessarily weak, but they're weak with that amount of load and work on them, right? Let's go. Chest out. And that's coming from where? Lats. Lats. Lats. Keep those lats locked in. Push your butt back. There. Good.  Come back up. Good. Starting to bend through the spine. Keep bending through your hips.  Now just one breath. All right? Airing at the top. Go down. Stand back up. Keep this closer to you. I want this going down like back here. Try to push it back between your legs. Keep more lats on. Chest out. Go back again. Now you're trying to push the kettlebell between your legs. Good. Back up. That's better. Good job. Keep thinking about pushing the kettlebell back to your heels. Two more. Chest up. Up. Good. One more.  Keep the lats on. There you go. Yeah, keep pulling the lats in. Up. Good job. I'm down. All right. So that was just one exercise of today's workout. So I'm going to show you more of the workouts as these videos go on. But I just wanted to get that Nordic curl out there because it's so important. What I learned is just how tough it is for me to keep my glutes tucked. Just to keep my hips and my body and my spine in the right position so tough. And just, I love the way he had me find that end range of what my injured hamstring can do. I can talk about my hamstring more if you want to know how I injured and things like that. Let me know if that's what you want to hear. It's hurt. I pulled it. I over-trained it. And just trying to get stronger in the range that I have is so fascinating to me. And such a great way to approach it. I really think it's just a fantastic way to rehab, get stronger in the range that you have, isolating that muscle. And I think that's the way we all should train, understanding our bodies to the finest detail and only doing what we can handle. Not trying to, not breaking down form and just trying to get into these ranges. Going the range you can. Hammer that home. Get stronger. That's what it's all about. All right, guys. I'll see you next week. Let's go dunk life, baby.