The scene of the crime is presented by Billy Johnson Jr. Attorney-at-Law. Last week we talked to Kentucky State Police Detective Chris Phillips about the fatal hit-and-run here at T. Berry of 60-year-old Larry Hinkel. Today we'll talk to Jody Hinkel, Larry's son and his wife Ashley. A dad happened to be walking away from the house at Moe Creek and was hit by a car and it was a hit-and-run and I really don't have a lot of knowledge of what happened. While there was at least one eyewitness, this witness was not able to help police with information that could lead them to the vehicle or the persons driving the vehicle that night. Larry had a friend walking with him on the road next to their house and all that he can really remember is the lights from the headlights and Larry got hit so hard it knocked him practically 15 feet into his friend and this friend has lost a lot of memory due to this. He's having a struggling really hard with this. Detective Chris Phillips has said that there has been very little verifiable information come to light in this case and they're looking at the community at this time for answers. Somebody's thinking that protecting one of their friends is better to benefit them than telling the truth. Think again, Larry has grandchildren, he has a wife, he has children, he's a daddy, he's a papal and he's a neighbor and he's a friend and everybody loved him and he at least deserves the answers that we are all seeking. Again, if you or anyone you know has any information about the fatal hit and run here in T-Berry the night of July 1st, 2017, please call Detective Chris Phillips at Kentucky State Police Post 9 at the number on your screen. Reporting in T-Berry for EKB News, I'm Polly Hopkins.


It's an absolute honour and a privilege to be here in the India Energy Week. I think there are some important messages that India is sending across the world as it ascends and takes on the presidency of G20. And no less than the Prime Minister himself, as Excellency Shree Narendra, has been sending some very strong and clear guidelines on how to navigate a very difficult and daunting energy transition. Today the session with the CEOs was truly enlightening and I am awestruck and also an admiration of his leadership and ability to sell the concept and also fight for the principle of energy justice, even as the world struggles to deal with energy security and affordability for citizens who need energy as they grow.


Medical doctors in South Africa are batting the worst wave yet of the coronavirus pandemic. At an emergency COVID-19 ward run by a charity in southern Johannesburg, medics will gaspin patients to their beds, rush from room to room with oxygen cylinders, and pass bags of patients in a coughing fit. South Africa is currently experiencing the third COVID-19 wave as the infectious Delta variant continues to spread. The Delta variant has caused enormous strain on the resources in not only private but also public facilities. So every hospital is getting strained, every healthcare worker is getting strained and there is why this disaster management facility has to come up and I hope it comes up in many places.  So I'm very fortunate to have the help that I have here because it's very straining and straining because there's still patients in the community that are still phoning me from the community for help. This wave is a lot more aggressive. It's easily transmitted and patients are now presenting with more severe symptoms too fast. You know this was one experience because you don't know what later had. You know if you look at the news and say how people are going on you know passing, why and things, it was the most frightening experience. I came in coughing, coughing, coughing. Today I'm 100% better. Doctors came to tell me that all my blood works are excellent. So I'm responding well with the medication. My body is fine, I don't have pain in my body. I only had pain in my lungs but it's better, much better now. I'm not being able to breathe talking to my son talking to Dr. Manga on the phone all the time and explaining to him to do this with the machine, change the machine, fetch another machine.


And today I will be discussing about the compaction of soils or in other words the compaction characteristics of the soils. Well, this is a very significant discussion and most of the infrastructure which is being developed in present day as well as in the past it has been mostly by utilizing the properties or the characteristics of the soils when they are compacted. So, it is a very significant discussion in the realm of geotechnical engineering. It is so unfortunate that most of the practicing engineers either they have forgotten the subject or they do not practice it properly in the field and that is the reason that most of the structures are yielding, they are failing and I would say what a shame in 21st century where we cannot even compact the soils properly to make the infrastructure of importance. So, to begin with the compaction process is basically it is a densification of soils by imparting mechanical energy. Now, the whole idea of compaction is when we say densification what it means is that the gamma dry of the material is increasing and the void ratios are decreasing all right. So, this is what densification is. It mostly depends upon the gradational characteristics of the soils or the type of the soil and when we say type of the soil it is a big matrix which has to be defined primarily if I say that this is the fine grained material or the fine grained soils, the coarse grained soils. So, primarily it depends upon the type of soils which you are dealing with it so happens that the fine grained soils can be compacted in different by applying different techniques. Mostly we use temps or hammers. One of the word of caution here would be when you are dealing with the fine grained soils and when you are tamping them and if the soils happen to be saturated the chances are that pore water pressure will develop. So, we have to be careful when we are tamping the whole thing. I hope you understand what are the issues associated with the pore water pressures when they develop in the soils. So, if pore water pressures develop the effective stresses decrease. Sometimes the fine grained soils are also compacted by falling weights. Check it on net some good examples of how these compaction techniques are being utilized for creating different strategic and important infrastructure most of the time the airports and the seaports. We have kneading compactors sometimes the compaction can be done by static pressures. The most classic example of fine grained soils being compacted is sheep foot roller.  If this is a drum on this drum you will find protrusions like this. So, this becomes a sheep foot roller basically there is a philosophy behind creating these type of rollers. This also gives you the kneading compaction you are enhancing the surface area of the compactor by creating these types of you know protruded elements. So, each time when this system enters the soil mass this surface is available for compaction process not only the small a to a by providing these type of protrusions I have enhance the surface area to a, b, c, a. So, in other words the surface area gets enhanced and hence the compaction can be done better. You can impart energy up to great extents to the soils. Sometimes the pneumatic tire are also used so these are different schemes of compaction and the tools which are used for compacting the fine grained materials. The general word of caution is try to understand their limitations and ill effects before you use them. On the contrary the coarse grained soils are compacted not compacted we cannot compact coarse grained material remember we densify them by using vibrations. So, most of the devices which are utilized for compacting the granular materials would be a sort of a compact vibrator. This could be hand held plates whenever you get time please go through the net and realize that why structures are failing because of lack of proper compaction and ultimately the whole matter goes into a code of law. All big companies to small companies are facing this problem and big projects are in litigation and this is where people like you can help the profession by pursuing the case of your client. So, these are hand held plates vibratory rollers sometimes we also use falling weights this is called as dynamic compaction. I have been involved with several projects in the Middle East and in certain parts of Russia in our own country in the deserts where it becomes very difficult to compact the soils and then you have to design a scheme of densification of coarse grained materials which becomes very interesting. By the way Indian army also uses this concept when they do ballistic test of detonators. So, this is a very interesting subject to pursue research on and to help the country and the society. I can do densification by passing the shear waves alright. So, imagine that if this is the soil mass and somewhere here if I am having a detonator or a weapon alright and then if I charge this I can densify the sands. So, this becomes densified sands most of these schemes are taught when you take case advance course in ground modification or soil improvement. So, these techniques are normally applied for ground improvement. So, my suggestion is please go through the videos which are available on modification of the soils by using different techniques. As far as the compaction is concerned what is the basic objective?  So, most of the disasters which I showed you in the problematic soils sometime back in a PPT the main cause of most of the failures could be the lack of compaction or poor compaction. The another objective is to enhance the shear strength of the soil and third objective is to reduce the permeability. You may say this is sort of a treatment which you give to the soils which are ailing.  If I want to increase the shear strength or the bearing capacity of the soils I might have to compact them or if I want to decrease the permeability I would like to compact the soil. These are the objectives. Incidentally the compaction procedure in the laboratory was proposed by Proctor R.A. 1933. Hence the test which is normally done to check the compaction characteristics of the soils is known as Proctor Compaction Test. This becomes a benchmark for testing the soils alright. I am sure you must have realized that I have used the word compaction characteristics. So one of the clever ways of using these characteristics would be to classify the soils. So one of the ways I can classify the soils would be based on the compaction characteristics. So you may say that Proctor Compaction cannot so be used to characterize. So I am sure you must have realized that there are several schemes of classification of soils which we have developed by this time starting from genesis to creation to the transporting agences to the three standard techniques of you know U.S.C.S, I.S.S.C.S, AASHTO, U.S.P.R. and then now I am using the compaction characteristics. Philosophically when you say that I would like to utilize this property to correct to understand how easily compaction can be done. This is the response of the soil to external loading, to external loading particularly external loading could be either static or dynamic in soil mechanics geotechnical engineering one course mostly I will be dealing with the static phase. But when you become a consultant you will be dealing with lot of dynamic issues and one of the dynamic issues I have talked about over here that you know military of the country would like to test the weapons and one of the best ways to test them would be in deserts or the second one could be go to the sea and detonate your weapons and see what is the you know destruction capacity of the weapons. Now the compaction characteristics would depend upon some parameters, here I was talking about this. So densification would depend upon the gamma D, the moisture content, the compaction energy. So E I am using as compaction energy and normally we quantify this as compaction energy per unit volume of the soils, type of soil and sometimes also under the conditions whether it is a lab test which you are doing or a field test. So this is the matrix. These are the uncertainties associated with the compaction process. The compaction would depend upon the gamma dry, moisture content, compaction energy, type of the soil is a very you know big term, it would mainly talk about the plasticity index, particle size distribution characteristics, specific gravity also, organic matter alright and so on, carbon. So organic matter one of the shoots of the organic matter could be percentage carbon content. When you deal with the industrial issues, there you find that carbon is the most notorious thing, what it does? It absorbs moisture and then it creates lot of problems as far as the compactability of the soils is concerned alright. When we say compaction process in the laboratory, there are few parameters on which the entire thing depends. We first say weight of the hammer, this you must be doing in laboratory I am sure and this is the modified proctor. I will talk about this slightly later. So weight of the hammer, height of the drop, number of drops, number of layers of soil, volume of the mold. So basically you are mimicking what can be done in the field by creating the situations in laboratory. It so happens unfortunately that whatever can be done in the field cannot be 100 percent replicated in the laboratory, particularly for the materials which require vibrations. So it is very difficult to do vibration of coarse-grained soils. In a proctor test normally this is 2.6 kg and modified proctor test is 4.9 kg, height of the drop is 31 centimeter and here it is 45 centimeter. Basically why the name modified has got added up to the proctor test because you are modifying the compaction process. So I will show you how the volume energy can be computed and number of drops are normally 25 here. We keep them same and number of layers are 3, 5. So if I am compacting a soil mass, suppose if I have to create a filling alright, this is the existing ground level and I want to create a villa for you let us say which is sea facing. So somewhere here if I want to store water, I have to compact the soil. So this becomes a retaining structure alright. So this is the compacted soil. This becomes the compacted soil mass and in the process what I have done is I have elevated the ground level from some value to x plus y. This is the engineering depending upon the requirements, I can create a situation, I can create a property line for which I will charge to my client alright. The thumb rule is the geomechanics is something which has to be practiced as you remember. So when you practice something you do not practice anything free of cost. This money is involved everywhere. In standard proctor if you compute the total energy which will be equal to m into g into h multiplied by number of layers. If I define this layer as n and number of layers say l alright, number of drops I will define this as say n d and this is divided by the volume. Volume normally we keep constant as 1000 cc. So if you compute the energy is the height of the weight which is falling of this magnitude, number of layers, number of drops, number of layers and per unit volume. So if you compute that you will be getting some numbers like 593 kilojoules per meter cube and in case of the modified proctor you will be getting 2704 kilojoules per meter cube and you need not to mug up all this use. What is the methodology of doing this compaction in the laboratory? What is the interpretation of the results? That is very important to understand. You take the dry soil sometimes oven, oven dried alright. I hope you understand what type of soils have to be oven dried and what type of soils need not to be oven dried. Way it and pass it through 475 micron sieve. Whatever gets passed over we add water to suppose if I use the term Wp or let it be W I think it will give a wrong notion. Let us say this is W prime alright. To this W prime we add moisture content in stages normally we add 5%, 10%, 15%, 20% and so on depending upon the material and then you leave the sample for maturing. This is what is known as maturing of the sample particularly when you are dealing with fine grain materials. So the procedure is that you take the soil spread it on a thin poly thin sheet uniformly use a hair sprayer and sprinkle water. Keep the entire thing in the polythene bag and by using your hands but without touching the soil you rub it. So the moisture becomes uniform and then the whole system is normally kept in a humidity chamber. After 3-4 days we should be performing the test to get good results. So I will be now discussing the interpretation of the results which is going to be useful for understanding the compaction characteristics of soils. It is a typical way of plotting the graphs for the data from the compaction test. On Y axis we plot gamma D gram per cc. Though I am using the word or the symbol gamma D I mean you should permit me to use gram per cc though technically it is not correct. So I can change this to kilo Newton per meter cube also alright. It depends upon what you are using and X axis is moisture content you know how to obtain moisture content. So after 3-4 days of humidity chamber storage of these samples you take them, keep them in 3-4 pads, weigh them, dry them, again weigh them, find out the difference of the moisture which has been lost and that gives you the moisture content. So typically we get some points like this experimental results alright. Point number one most important thing always remember in life whether you are even if you become a CEO of a company you have to appoint a geotechnical engineer to establish your industry alright. Industries cannot be operated in air they have to rest on the foundations and the soil system. So this is where the contractors try to loot you, cheat you because whatever goes beneath the ground nobody knows. Looking at this graph I can get the complete understanding of the material. A typical belt shaped curve alright indicates a material which is quite easily compactable. Remember when we are talking about the PSD particles as distribution you are talking about 3 types of soils, gap graded, well graded, poorly graded and this is where I had used the word the well graded soils only can be compacted properly. So this appears to be a well graded material. The particle packing is such that all the bigger particles will allow the finer particles and finest particles to get assimilated in the matrix in such a way that you get a very good density of the mixture. Mostly this type of relationship is depicted by exhibited by silty sandy clay material soils alright. Clays cannot be compacted so easily. So if somebody asks you to plot the graph putting on the merging on the same graph as of this the relative position of the clays could be something like this. What this indicates is clays cannot be compacted. Why? Electromagnetic forces you cannot bring the particles closer to each other and that to by applying mechanical energy because remember this is the response of the soil when you are applying mechanical energy to this. So by virtue of fine-grained material being a fine-grained material and lot of electromagnetic forces which are active on the surface of the grains of the clays they cannot be compacted. It is going to be a flat curve. Sands very difficult to compact by this type of compaction unless you go for the vibratory system. So proctor compaction curve this is what is known as a proctor compaction curve. Rather than saying curve you can also use the word characteristic. Characteristic is more scientific and technical and I can interpret it the way I want. So this is proctor compaction characteristic. Sands cannot be. So not valid for sands fine. So what should be done? Vibrate them create a vibratory system and densify the sands. Next time when you go to IIT Jodhpur it is a beautiful example of how sands have been densified to layer the foundations in the dune or what we call them as the Aeolian type of soils which I was discussing sometime back. So the point I am trying to highlight is just by looking at the nature of the graph or the compaction curve I can infer that what type of soil I am dealing with and hence this becomes interesting we have to classify the things. Now what is the interpretation of the curve? If I say this is gamma dry and moisture content if this is the graph proctor compaction and I am sure you must have realized the moment I enhance the energy of compaction it becomes modified proctor. So if I superimpose for the same material proctor compaction and modified proctor compaction this is how the curve would shift. This point is designated as WOMC the way you carry an I card the way you carry a roll number and believe me I still remember my roll number of beta days 82062 because this carries lot of information you know why there is a significance of this. So for a material this WOMC is a finger print. Now how you want to utilize this finger print of a material it depends upon you as a technologist clear. So this is one of the finger prints of the material which we have captured. Coming to this one this is what is known as gamma d max clear. So the moment I have described two parameters gamma d max and WOMC I need not to bother about any material about the material properties anymore because these two are the characteristics of the material when it is getting exposed to external loads. As an engineer I am more interested in observing how the soils are going to behave when they are exposed to external loading static loading is this fine. The logic here is when you have enhanced the energy the particles become much closer and the water retention decreases. So this is the WOMC corresponding to modified proctor and what happens is gamma d increases that is okay is this part clear. So if you remember the matrix which I wrote that why you do want to densify the soils the first objective was to reduce the settlements. I have made a system more stronger imagine when you started from this point where the density are extremely low you added a bit of moisture and what water does in the grains of the soil it reinforces the particles provides more strength gamma d increases alright you keep on adding water what is enhancing gamma t total and gamma t upon 1 plus W is gamma dry. So the more and more moisture you add up to a certain extent the gamma dry will get changed it will increase a peak value comes beyond which it drops down fine. In modified thing the water retention drops because the pores have become absolutely small and this water is going to sit in the pores if you remember. So modified proctor compaction is the characteristic of the soil which gives you the minimum possible moisture holding capacity at optimum. However this gives the maximum possible density which a system can attain. Now this is a domain in which you have to work as a technologist as an engineer you got it how best you can compact things at the site would define how best you are utilizing the material are these statements okay are you able to follow this. So the trick is how much I can utilize this material to create a system which is very dense the moment I have achieved this the shear strength is going to be maximum the permeability is are going to be less settlements are going to be extremely less and hence I have done engineering with the soil mass is this part clear any questions yes please use the mic. Sir according to the formula right gamma dry is equal to weight of solids upon total volume so when we add the water the volume increases but the weight of soil does not increase just give me 5 minutes I will clear all your doubts just hold on for 5 minutes alright anything else then we can discuss what are these are the SSK and back. Shear strength of the soil hydraulic conductivity of the soil mass compacted soil mass remember shear strength of the compacted soil mass hydraulic conductivity of the compacted soil settlement of the soils which are compacted okay any other question anything else did you follow the logic what water does to the soil so when you add little bit of moisture the water gets remain into the pores air is expelled out weight of the water is more than the weight of the air the unit weight of the soil water matrix increases beyond a certain point this is also what is known as the maximum capacity of the soil to retain moisture because if you start compacting beyond this point water being incompressible will come out of the compacted soil mass and this water cannot be retained over there. So now answering your question the way we do it if yes does WMC is actually saturation point. No I will introduce the concept of saturation after 10 minutes alright let us go in stages you understand the basics then all these terms will come automatically you need not to bother. So suppose let us talk about his point first at this point normally what we do is we know the weight of the soil alright we know the volume that means we know the gamma t remember no confusion is this okay so the mold which you have taken in which you have packed the soils in number of layers by imparting number of drops clear you have created a dense matrix you know the weight of the compacted soil in volume gamma t is known take some of this soil obtain moisture content by open drying and compute gamma d is this okay this is the right way of doing things is any confusion I am plotting this moisture content with respect to gamma d remember when I was teaching three phase diagrams I had used a word gamma d is a normalized form of gamma t. So gamma t has been normalized with one plus moisture content the tendency of the analysis which normally geotechnical engineers adopt is to filter out the moisture what I have done here by normalizing the total unit weight with moisture I am getting rid of the moisture component and I am talking about the matrix which is in the dry form is this okay fine what was your question I think this is what you have been asking how to compute gamma d but in this case with the gamma t will always be a constant it need not to be constant so remember you started from point one alright point two so what has happened you have added more water and that is what I was giving you the logic in a dry soil when you add water the air gets expelled weight of the water is higher than weight of air so what you are doing is you are traversing on a curve where the gamma t is getting becoming more and more as the moisture content increases. Gamma t is also increasing and rw is also increasing. What is rw? Moisture content. Moisture content yes that is right so from point number a to a what has happened the more and more moisture you include in the system and compact it the total density increases clear and because of that gamma d increases okay at this point when the optimal is reached there is a maximum possible water holding capacity of the soil beyond which if you compact water being incompressible will come out of the soil matrix it will start draining out we call it as a bleeding of sample this is okay for the addition of moisture what is going to happen it is going to reduce the gamma t itself clear because now the soil is becoming more and more dispersed I will use this terms in another five ten minutes clear and hence the gamma t is dropping down but moisture is still increasing so it is becoming a sort of a slurry but not a full slurry it is a semi solid material which is being compacted is this okay. According to our normal formula gamma dry is equal to weight of that solid compacted soil upon volume so as we are increasing the moisture content or volume one minute one minute w corresponds to each observation point of the sample volume is fixed gamma t is known clear corresponding to this state w is known and hence gamma d is known so that means this point is known I can plot it over here in the same soil mass you enhance moisture so what is happening you are traversing on the right hand side of the x axis by adding more moisture this point comes over here weight has also increased now this weight w has become w 2 okay volume remains constant gamma t has increased w has become w 2 and hence gamma d has become gamma d 2 so this process goes on from A to A very close to the optimal is that basically what happens is you are displacing a lighter fluid air by a denser fluid water so compaction is the process by which you are expelling out air from the voids of the soils is this okay by filling the voids with water it is okay so hold on for another 10 minutes and I will respond to your query also and that should give you a complete idea about what we are discussing so typically if I say A to A prime and B prime to B any compaction curve will have 3 parts now this is what is known as dry of optimum A prime to B prime will be near optimum and B prime to B wet of optimum is this okay.


From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. Our research and analysis points to a new modern data stack that is emerging where apps will be built from a coherent set of data elements that can be composed at scale. Demand for these apps will come from organizations that wish to create a digital twin of their businesses to represent people, places, things, and the activities that connect them to drive new levels of productivity and monetization. Further, we expect Snowflake at its upcoming conference will lay out its vision to achieve this. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis and the Head of Snowflake Summit later this month, we lay out a likely path for Snowflake to execute on this vision and we address the milestones and challenges of getting there. As always, we'll look at the ETR data and what that tells us about the current state of the market and to do all this, we welcome back Cube Analyst and contributor George Gilbert. Hey George, good to see you. Thanks for coming on. I did, good to be here. All right, what we really want to do today is get into how we see the new modern data stack evolving and that's what this next chart really kicks off. So we see the world of apps moving from one that is process-centric to one that is data-centric. We've talked about this before with George and the Cube. This is where business logic is embedded into the data versus the way it works today. And there are four layers to the emerging data stack that are supporting this premise. And we start at the bottom even though it's at the top of this slide. That's the infrastructure layer, which we believe is increasingly going to be abstracted to hide the underlying cloud and cross-cloud complexities and make that invisible, what we call super cloud. Then as you move up the stack and down this slide is the data layer that comprises multilingual databases and multiple APIs and pluggable storage. We're going to talk about that today. And then continuing further is a unified service layer. That's the next layer. Brings together BI as well as AI and ML. And then finally the paths for data apps at the top of the picture, which defines the overall user experience. All right, George. Let's get into it. What do we need to know about this emerging stack and how do you see Snowflake's role in evolving it? If there's one big takeaway, I would suggest that we're moving from an era where you built web 2.0 apps that's for really microservices and separate databases. And then you move that data somewhere down a pipeline and try to collect it and analyze it too. As you said, something that manages people, places and things, you worry about things, not strings. And more important, there's a stack that unifies the development of these types of apps. Today in the web 2.0 world on Amazon, for instance, there's 200 different services that weren't really designed to work together that developers try and cobble together. Snowflake's ambition is to essentially substitute all that complexity with one coherent development environment that manages, as they say, all workloads, all data. And since all applications are data apps, this is the new stack for building applications, primarily on Amazon, because that's the ecosystem where the development services are so fragmented. Okay, we'll talk about Snowflake, we believe, is trying to position itself to be the best place to build those apps even better than any of the clouds. But let's move on to introduce the Uber analogy that we've shared before. The idea here is that the future of a digital business will manifest to a digital twin of an organization. The example we use frequently is Uber for business, where in the case of Uber, you got drivers, riders, destinations, ETAs, and all the transactions that result from real-time activities that have been built in by Uber to a set of applications where all these data elements are coherent and can be brought together and composed, if you will, to create value in real-time. So George, why is this such a powerful metaphor? How does it apply? Well, one, by organizing your application around the people, places things and activities in the real world, there's not an impedance mismatch between what a developer thinks about and the entities in the business that he's trying to manage. In other words, this new era of applications, you're trying to orchestrate things in the real world. And to do that, you have to have coherence across all these different elements, all the people, places and things that gets to the semantic layer. But then underneath that, you need a data management layer that integrates operations and analytics. And so once you have that sort of coherence, then you can have AI orchestrate things like matching drivers and riders, calculating a route, estimating a price, calculating an arrival time. All that stuff can be done autonomously because you're not working with siloed applications for different processes. You've got one coherent layer of digital twins of everything in your business. Okay, let's keep going on to the next one. Let me get the pyramid slide here. We think the industry generally in Snowflake specifically are moving to a world beyond today's web 2.0 programming paradigm where you have analytics and operational platforms are separate and the application logic is organized through microservices. We see this evolving. We see a world where these types of systems are integrated and BI is unified with the AI and ML tool chains and a semantic layer organizes the application logic to enable all the data elements to be coherent. George, add some color to this graphic and why does it point to Snowflake's platform strategy in your view? So the biggest challenge is that you can build Uber type apps which we think was the prototypical app for the next generation where you're using analytics to orchestrate things in the real world. The problem is only companies like Uber or Google or Amazon could build those types of apps. You were mucking around in map reduce code. You were slinging, you know, TensorFlow for building models. What you needed is to bring that capability to mainstream organizations in a package platform. And that's essentially what Snowflake and Databricks are trying to do for data applications so that it doesn't require world-class developers. It requires only mainstream developers working with a package platform. That's the big... Is that what Ford is on this chart? Yes. Okay, explain that double click on that for a second. So there were not many people who could build Uber class apps. Like if you, as we will talk to Uber itself some of the developers I think next week we'll find some of the incredible efforts that they had to go through to cobble something like that application together. But to get this down to mainstream developers, I mean, we could think of going back, this might be before, you know, this is one historical example, but originally building graphical applications took the skills that only a few people had to work with like windowing toolkits and deep, you know, see systems programming. And eventually that became something so easy that you could just drag and drop like a user interface layout graphically and just attach code elements to the graphical elements. In other words, it took it from systems programming to citizen programming. And we have to do the same thing here where we have to make a platform as a service that's so accessible that it doesn't take Uber class developers to build an Uber class app. That's what Snowflake and Databricks are trying to build. And we have details on how we think Snowflake is going to do it. Okay, let's get there. Let's take a look at the three enablers that we just laid out earlier. This sort of a setup graphic, if you will, and we'll drill into each of these three areas that we're showing here. So first of all, Snowflake is legit in our view when they say, you know, they're not a data warehouse. Now, of course, the company started as a database player, but they've evolved into a platform. We think the main thrust of that platform is an experience that promises consistency and governed data sharing and it can, you know, even across clouds and it continues to evolve to support any data type through pluggable storage and being able to extend this promise of materialized views, which implies a wider scope. And the really interesting opportunity is building data apps and it's clear to us that Snowflake wants to be the number one place in the world to do this, meaning the fastest time to develop, the most cost effective, the most secure, the most and the most performant place to build and monetize apps, data apps. George, explain the pluggable views in this chart and the importance of being able to update materialized views and Snowflake's iceberg play, which is a bit misunderstood. In other words, a lot of people said, oh, this is just Snowflake, you know, it's a checkbox to try to, you know, compete against Databricks, it's an open source approach. And so how do you see this and take us through the key points on the graphic? Okay, so the core principle is supporting all data types and all workloads. And Snowflake started basically with OLAP data where it's arranged in columns and it's for slicing and dicing. They started to add OLTP data last year with hybrid tables, but they're basically opening up the DBMS in multiple layers so that there's pluggable storage so that you could add support for streaming data, for graph data, which is connected data, for vector data, which is the data type that machine learning models sort of speak natively. And by supporting multiple data types, you have this unification so that it provides simplicity for the developer. And then above that level, an example of simplicity is the materialized views that we're talking about. You could have transactional data coming in and you have OLAP data, which is like historical reference aggregated data. And the materialized view is like a business metric like in Uber, they might be looking at how many rides per hour per city or something like that. And that needs to be updated based on transactional data that's coming in, but it's also historical reference data. And the problem is when you did metrics in the past, you had to extract or at least the BI tools extracted the data from the database and it immediately was disconnected from the live data and it became stale. Now Snowflake in the database engine can incrementally support updating those metrics with live data and that's powerful because it can do it across data types. Because the important thing is you're not moving data to do that, right? You're updating directly into those materialized views. Is that correct? That's a core capability of a good platform is it translates between the things a developer cares about on top and the strings that the database is managing internally. And it hides worrying about moving the data, translating the data, caching the data. Snowflake just takes care of that. Even across data types. And if you go back to the chart, Alex, so the pluggable storage, let's explain what we mean by pluggable storage. You're talking about the ability to support all kinds of different database types and formats, correct? Yes, what we don't know is can they, you know, the level of support for cross database type joins and transactions. In other words, we do know that, like you can join transactional table and an OLAP table and incrementally update the product of that, which would be a materialized view. We don't know the level of support for joins and transactions across these pluggable storage. What we do know though, is that the defining feature of the data lake or lake house architecture was that any compute or analytic engine could read and write data without going through this gatekeeper, which in the data warehouse version was the SQL DBMS engine, which imposed a performance tax or even just a API constraint. Now, iceberg tables are native tables in Snowflake. And so any tool can read and write directly to iceberg tables without going through the SQL engine. And that's a big change. That's a headline change. Yeah, and as well, again, I said earlier, this is a lot of people think it's a checkup item. I don't think, you know, it's pretty clear the Snowflake doesn't want to make this a checkup item or anything, but they want to make it the best place to do, iceberg it will be, you know, with Snowflake, otherwise, you know, what's the differentiation because if it's in iceberg, then anybody can use it. So there's got to be a hook, a carrot correct to entice customers to stay on Snowflake. It's that DBMS execution engine that sits on top of both iceberg, native OLAP, OLTP, graph, they're still unifying everything. You can read and write directly to iceberg tables, but they provide this unifying framework for accessing multiple data types and joining across those multiple data types. That's the value add as well as governance across all of that. The key is you do not have to sort of pass go and pay $200 every time you want to get to an iceberg table anymore. That was the defining feature of the data lake architecture that you could read and write to the data in the data lake without going through some data warehouse engine. All right, let's go on to the next point here in the fundamental premise of the pillars, which is unifying business intelligence and AI ML. This is a critical theme of the new modern data stack, if you will. And that's what we want to talk about here, George. You're the diagram that you shared with me. You're showing Snowflake on the left-hand side, Databricks on the right. And you can see these worlds coming together, which is exactly what's happening in the real world. Walk us through the important points of this graphic and what are the implications for Databricks, Snowflake in the industry in general? Okay, as you'll show in the ETR data later, basically if an enterprise wanted the best of both worlds, the business intelligence world and the AI ML world, they needed both Snowflake and Databricks. Both companies are trying to make it possible so that you don't have to choose both, but you can just choose one. Our analysis is about the progress Snowflake has made in supporting all the AI ML tools and personas that you used to really have to go to Databricks for. The headline here, the biggest headline I think is Python Pandas support. This is the core of working with data in Python. And I've heard statistics that there now may be more Python Pandas programmers than there are SQL programmers. And the problem with Python support is that if you wanna use any of the libraries, especially Pandas, it runs on a single core, on a single CPU. And so then when you wanna do your exploratory data analysis on large scale data or put a pipeline into production, you had to rewrite it, say in Spark and their equivalent was Koalas, which at least as of a year ago was like 60 to 70% coverage of the Pandas API. So there's a company called Ponder that spun out of the Berkeley Rise Lab that did a re-implementation of Pandas that can run on scale out compute execution engine. They've implemented that on Snowflake. We don't know what sort of relationship there is or there will be, but basically you'll be able to take your laptop Python Pandas data science and data engineering code and scale it out directly to run on the Snowflake cluster with extremely high compatibility. The numbers we've seen are 90 to 95% compatibility. So you might have this situation where it's more compatible to go from Python on your laptop to Python on Snowflake, then Python on Spark. So that's an example of one case where they're taking the data science tools that you used to have to go to Databricks for and supporting them natively on Snowflake. Yeah, so. So, Alex, you bring up that last slide. I just want to say probably about four or five years ago we said, oh, there's this new workload emerging. It's a combination of AWS IS plus Snowflake plus Databricks to bring to AIML. And we said, okay, this is kind of the new modern workload. Well, what's happened is both, is that actually all three companies are saying, hey, we see a huge TAM out there, we're going after this. And so those worlds are coming together, George. Right. And another thing, another analytic persona that's being supported is LLM access. So large language models are a way of querying your data in natural language. ThoughtSpot pioneered this based on acquisitions. We can read between the lines that Snowflake is also going to offer LLM access to their data, but not only to the structured data that you would query via SQL, but complex information, whether in documents, images, video that you would embed in vectors that they can also query. So the point is this distinction between business intelligence and data science and machine learning workloads, that really is blurring and that distinction is going away so that either platform can support all the workloads without having to invite the other vendor in. And ultimately we're talking about operationalizing data through data apps. And that is a big change, right? We've operationalized every sort of backend system over time. And now we're entering, we think a new era. Okay, we're going to take a break from George, your awesome graphics. Thanks for those and then come back to the survey data. Let's answer the following question. To what degree do Snowflake and Databricks customers overlap in the same accounts? And this is the power of the ETR platform. We can answer these questions in over a time series. So this chart here shows what the presence of Databricks is inside of 302 Snowflake accounts within the ETR dataset. The vertical axis is net score or spending momentum and the horizontal axis shows the overlap. And we're plotting Databricks that we also put in Oracle just for context. So you see 36% of those Snowflake accounts are also running Databricks. That jumps to 39%. It's not a huge jump, but it jumps up if you take Streamlit out of the equation. And notably this figure is up from 17% two years ago and 14% two years ago, if you take out Streamlit. The point is Databricks presence inside of Snowflake accounts has risen dramatically in the past 24 months and that's a warning shot to Snowflake. And by the way, Oracle is present in 69% of Snowflake accounts. Now let's flip the picture. In other words, how penetrated is Snowflake inside of Databricks accounts? And that number, as you can see here is 48% it's only up slightly from 44% two years ago. So Databricks despite the growth of Snowflake over the past two years is more prominent in terms of penetrating Snowflake accounts. So George, what do you make of this data? I think if I were to distill it, it's that as organizations became more mature with data, they realized they needed both if they really wanted to get the most out of their data. That's why we see increasing overlap. Now both vendors have been trying to address the workloads from the other vendor. And the takeaways from our analysis is that Snowflake is making a lot of progress on addressing the data science and engineering workloads that formerly were really the stronghold of Databricks. And obviously Databricks is not sitting still but the main point is I think if we look in the over the next 12 months we're likely to see less overlap. All right, let's move ahead to the third key point of our simplified platform view which brings us deeper into the semantic layer. So this graphic emphasizes the notion of organizing application logic into digital twins of your business and our assertion is that this fundamentally requires a semantic layer. George, what does that mean for Snowflake and other players like AtScale or DBT or Looker? And of course even Snowflake, do they have to own that semantic layer? Can they partner to get that semantic layer? What's your take? So the semantic layer is starting as business intelligence metrics, things like bookings, billings and revenue or as we were talking about with Uber, like rides per hour. Now the reason why BI tools in the past extracted data to manage that, the support inside the data platform to translate between the metrics and the data and to keep that metric, the data behind the metric live and incrementally update it. That was not there, that will be there now. So what you'll see is companies like AtScale, DBT, Looker, probably not Power BI, they could define the business metrics and store the definitions inside Snowflake and then Snowflake would serve those metrics and cache the data, which is the hard part that these tools had to essentially extract the data and manage it themselves before. That support will be there now. Now this has implications for the full metrics, I'm sorry, the full semantic layer. And let me explain, Snowflake's attitude was they're doing the hard work to support the BI metrics. The tools from third parties define them but they're doing the hard work of translating that down into from things into strings and then caching the data on the way back. That's really hard systems work. Now it appears they think that they can leave the BI metrics to third parties to manage. So why not the full application semantic layer? The full application semantics is not something as simple as turning things into strings. That manages activities and there might be API calls to applications. There's a much more sophisticated process mapping. And we're not convinced that Snowflake has realized that if they leave that to third parties then the entire tool chain can essentially hide the data platform underneath. In other words, they have the mindset of a database management system but have they adopted the mindset of a full application development tool because the application development tool is thinking about things that a developer cares about in the business world and if they want to own the whole stack they have to manage that translation between all the things a developer cares about to all the things that a database manages. It's easy when it's BI metrics. It's not so easy when it's the full application semantics and we're not convinced that they've shown that they're thinking that far ahead. Well, I mean, there aren't a lot of historical examples of companies that are doing that. Oracle, not really, IBM is kind of the exception that proves the rule that it's hard. So, but let's double click on this, George and discuss further what it means for Snowflake in terms of who owns the semantic layer and how to translate the language of things into the language of databases which you call the language of strings. Explain these three layers here and the implications. So, if you wanna organize your application logic in digital twins that correspond to things in the real world and activities in the real world it's no longer, you're no longer just translating, say bookings, buildings and revenues into a database entity like a materialized view. Instead, you might be orchestrating calls to SAP or some other operational app that might sort of match drivers and riders. And you have to wait for the different apps to signal that they've completed their activities. This would be analogous historically to mapping between, let's say the Java application server and the underlying relational database or the object relational mapping layer which mapped the application objects down to relational tables. But this is even more sophisticated and no one in the past really managed that whole stack to the point where by translating all the way from the application entities down to the database entities did they make it so seamless for the developer that this stack was something that could not be cobbled together. In other words, it was not seamless the layers were visible to the developer. If the layers are visible to the developer that means some other development tool stack can abstract away snowflake. Snowflake's challenge is to make that so seamless that you wanna live in their development environment and you don't wanna move anywhere else because otherwise you would be exposed to that complexity. That's where we wanna see what they have to say about that roadmap. Okay, so the point being, if somebody else knows the semantic layer then somebody will be able to more easily extract that data replicate potentially what Snowflake's doing and that suggests that Snowflake may over time want to vertically integrate that although there's no indication thus far that that's the case. All right, let's close by examining the horses on the track Belmont stakes coming up this weekend. It's a grueling long mile and a half race. It's not a sprint. And we're looking at this graph at the marathon runners in the world of cloud data platforms. So same dimensions as before NetScore or spending momentum by N on the horizontal axis or overlap in the 1,171 cloud account. So we overall the 1,700 respondents in this last survey from ETR, we're now filtering that by accounts to say we're cloud accounts that's 1,171. And that red line of 40% indicates a highly elevated NetScore. George Microsoft just announced fabric at Microsoft build AWS they're gluing together its various data platforms. Google's got a really strong product in BigQuery with perhaps the best AI in the business. Comment on Microsoft, your thoughts on fabric you just mentioned probably not Power BI, AWS, GCP and their relative position to where we think Snowflake is going, let's riff on that. Okay, so the big change is we're trying to unify first the data so that first all your analytic data is in one place. There's one source of truth for analytic data. Then one source of truth includes all your operational data. Then one uniform engine for accessing all that data. And then that unified application stack that maps people places things and activities to that one source of truth. So here's where everyone is. Snowflake is unifying the analytic and operational data and operational data in all its forms. What they've done is put it beneath one DBMS engine with multiple personalities. Then what Microsoft has done is they've standardized all their analytic data on the Databricks data format. So all Microsoft analytic engines and all Databricks analytic engines can talk to the same data. Now they have not unified all the analytic engines but it's all one data repository. And in the Microsoft ecosystem, it's powerful that at least there's one source of truth at the storage layer. What they have not done is integrate operational data. You have to sort of synchronize that through essentially same change data capture. So there's some latency getting operational data. And if you wanna execute transactions on this data you go to an operational or transactional database. So they've unified the analytic data. In the case of Amazon, they really still have the Data Lake and the Data Warehouse and you can talk from one to the other but it's kind of through connectors and the Data Lake data is really not native for the Data Warehouse. It's sort of like treated like external data platform. Google Cloud Platform, while you have the full separation of compute and storage and you can put all the data you want in Google BigQuery, it's not the same as a Data Lake. If you have Data Lake data, you're querying that as if it were external data. In other words, they have not unified all their analytic data platforms in a single format nor all the analytic data engines, but they're working on it. And I assume we'll see a lot of progress towards that at Google Cloud next in August. So Microsoft and Databricks have made progress in that they're teaming up and saying standardized on the Delta table format and all analytic engines that either company will use that data. The next step would be to integrate operational data. I think that's further off for them because the Delta table format is just not conducive for operational data. So Snowflake, we were saying when we did the Databricks show a couple of months ago, Snowflake was ahead in that five years earlier than everyone else. They started building a cloud native analytic engine. They've generalized that to include the operational data and not just transactional data, but data in multiple formats. So they're very far ahead in providing a platform for building data apps. What they have yet to do then is that next layer where they map the semantics of people, places and things in the real world to any data format that they're managing. All right, and Alex bring that last slide up if you would. And then last point was on Oracle. You can see they don't have the momentum here, but a couple of points here. One is every company that we're showing here with the exception of Oracle is above that 40% line or just at it. The second thing is they all have ML and AI chops. Oracle, of course, is going to tell you with MySQL heat wave and the converged capabilities of Oracle database that they're already there. George, what would you say to that? Are you skeptical about Oracle competing in this space or is this Oracle on Oracle is what their game is? Oracle is kind of like the IBM mainframe, which is it's the repository for mission critical, operational and analytic data that's mostly on-prem. That the Oracle database was never really rearchitected to be cloud native so that the great big appliances that gave you that huge scalability, which are essentially mainframes, that's how they provide scalable Oracle access in the cloud is they put those appliances in the cloud. Right. You're saying AWS not running Exadata in their cloud? All right. You get the point. We're up against the clock, George. So let's close with the key questions that are going to be watching for at Snowflake Summit and of course Databricks events, which take place the same week in late June, last week of June. We're going to start at the bottom layer of the stack and work our way up, even though it's down on this slide. I sort of was referring to that earlier, but I got it backwards. This is really the slide I was talking about, still on the red eye here. The infrastructure layer, we're talking about managing data outside of the cloud. That's a big question mark that we have for Snowflake and doing, well, whether it's distributed joins if the egress fees were a non-factor, would that be something that Snowflake would do or consider the software layer, which is the pluggable, kind of moving to the next layer here. The pluggable storage engines, when is that actually going to occur? The support for materialized views and cross-engine transactions, what's the detail there and when is that going to happen? The unified service layer, which APIs are we going to support, when? How about DBT, Looker, AtScale, others that define metrics, are they going to do that inside of Snowflake? And what about the semantic layer? We talked about that a lot. And what about large language models and AI? What impact will that have? Snowflake's made some acquisitions lately. We're sure they're going to talk some of that up. And then the paths for data apps, you know, really more than a layer, it's the user experience. How will Snowflake's app store deal with discovery and monetization? How's that all going to evolve? George, kind of a speed date there on the key issues, but give us your final thoughts. This is really a new era for the cloud. We've had like a Unix ethos, especially on the Amazon ecosystem, where it was a couple hundred services like tools in Unix and we had pipes to connect them. That the burden for assembling all this was on the developer. And I think what's happening is Snowflake wants to come in now and say, yes, Amazon infrastructure is great, but if you really want to avoid all the complexity of stitching together 200 services, we'll give you a coherent stack and tool and the convenience and simplicity of a pass, but without the compromises in the past that we saw on the functionality of a pass. So it's really for the first time we're going to see someone say, take Amazon for all their wonderful infrastructure, but do all your development within our framework and we'll simplify it for you. That's their pitch. Yeah, and it's really interesting, right? Amazon continues to add function that its ecosystem has been, they've always been sort of transparent about it. So hey, the ecosystem has to move faster and add more value because we're going to do what the customer wants. They write the customer obsession kind of thing. It appears that Snowflake continues to be ahead of the game there, George. I want to thank you for your time. Great, always collaborating with you and more to come. This is the second in the series. George and I will be up next week with Uber digging in using that as we've used that example many times. So we're going to actually look at that example and how Uber basically in 2015 rewrote its entire stack and created this amazing capability. George, thanks for coming on. Thanks, Steve. All right, many thanks to Alex Meyerson who's on production and manages the podcast as well. Ken Schiffman, Kristen Martin and Cheryl Knight. Shout out to them. They helped get the word out on social media and in our newsletters and Rob Hoef is our editor in chief over at SiliconAngle.com. He comes up with some great editing and better headlines than I have. Remember, all these episodes are available as podcasts wherever you listen. You just got a search for breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com and if you want to get in touch, you can email me at david.volante at siliconangle.com. You can DM me at dvolante or comment on my LinkedIn posts. Please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for George Gilbert and theCUBE Insights powered by ETR. Thanks for watching everybody and we'll see you next time on breaking analysis.


I am here to discuss with you on topic special economic zones that is SAIS. Learning outcomes of today's session are at the end of the session students will be able to define SAIS that is special economic zone and explain importance of SAIS in India's development. Let us see definition of SAIS. A special economic zone is defined as a geographical region that is designed to export goods and provide employment. So this is the definition of SAIS. So what is SAIS? It is a geographical region and what is the purpose of its to export goods and provide employment also. Now remember in order to make the goods manufactured in SAIS at globally competitive prices these regions are exempted from some of the laws like federal laws regarding taxes, quotas these are exempted from quotas. Then FDI bans, then labor laws and other restrictive laws. So some concession is given to SAIS so that the goods which is manufactured will be at a globally competitive price and these are enlisted like this. Now what SAIS includes? SAIS includes EPZ that is export processing zones, FZ free zones, FTZ free trade zones, free ports, urban enterprise zones, free economic zones, industrial parks or industrial estates. So all these are components we can say of SAIS or they are included under SAIS. Now what is SAIS policy by government of India? See government of India is the first in Asia to recognize the effectiveness of the export processing zone EPZ model in promoting exports with SAIS first EPZ setup in Kandla in 1965 remember we have taken initiative right in 1965 in this direction. Now SAIS policy was announced in April 2000 by government of India to overcome the shortcomings which are experienced in account of on account of the multiplicity of controls and clearances right also absence of world class infrastructure and all these things. So EP this economical zones policy was announced in 2000 by government of India. Now what are the objectives of the policy which is announced by government to make SAIS an engine for economic growth supported by quality infrastructure complimented by an attractive fiscal package both at the center and the state level with the minimum possible regulations remember this is the objective of the policy announced by government of India right here you can see a special reason SAIS right constructed. Now let us go for SAIS Act the Special Economic Zones Act 2005 which was passed by parliament of India and this came into effect on 10th February 2006 remember so SAIS Act was passed by government in 2005 and this was started or came into existence from 10th February 2006 right. Now what are the objectives of this SAIS Act generation of additional economy or economy activity promotion of exports of goods and service promotion of investment from domestic and foreign sources creation of employment opportunities development of infrastructure facilities see all these are the objectives of this Act right so promotion of SAIS is important and for that these objectives are kept in front of our country. Now SAIS and export promotion the SAIS and export promotion facilitated the growth of the Indian SAIS remember the main factor for the underperformance of SAIS was poor export policy of India loaded with huge taxes and duties this was the reason why initially there was some underperformance right of the SAIS. So necessary steps are taken in order to overcome these difficulties so the government of India eased the export policy of India to facilitate easy growth of SAIS and export promotion of Indian goods across international destinations right. So this was the step taken by the government of India now which are the SAIS setup in India special economic zone in Mumbai Maharashtra ships SWP's head then Noida special economic zone Uttar Pradesh multi-product then MEP's head special economic zone Chennai Tamil Nadu again a multi-product Cochin special economic zone Cochin Kerala then FALTA special economic zone FALTA West Bengal these are the SAIS setup in India right now what is the provision under SAIS hundred percent FDI for manufacturing sector remember right foreign investment hundred percent then income tax benefit duty free import of domestic goods exemption from CST exemption from income tax on investments enhance a limit for managerial remuneration applicability of labour laws. So all these are the provisions which are facilitated for SAIS so that its function can be smooth now what is the controversy in SAIS in spite of the strong objectives of the Indian government the SAIS policy has the following controversies which are those generation of a little new activity as there may be relocation of industries to take advantage of tax concessions this is one controversy then revenue loss then large scale land acquisition by the developers may lead to displacement of farmers with meager compensation right acquisition of prime agricultural land having serious some implications for food security so these are some of the controversies which take place right now we will see advantages of SAIS what are the advantages growth and development this is the most prominent advantage of SAIS growth and development of nation this attracts FDI remember right then exposure to technology and global markets increase in GDP and economic model gross domestic product right then economic model employment opportunities are created these are the advantages of SAIS which are helpful for development of our nation dear friends let us stop a while here let us pause video here are some review questions for you write their answers neatly first is SAIS is intended for there are four options select the correct option then second is the main factor for the under performance of the SAIS where there are again some options write your correct answer and third question is SAIS act was passed by Indian parliament in write down the correct option come on here are the answers first question D is the correct answer all of these second question also D is the correct option D means all of these and third option SAIS act was passed in 2005 C is the correct answer the government of India right so these are the answers now these are the references for student today's session dear students go through them thank you


This is a LibriVox recording. All LibriVox recordings are in the public domain. For more information and to find out how you can volunteer, please visit LibriVox.org. Reading by Cherie Terrio. Anthem by Anne Ran. Chapter 12. It was when I read the first of the books I found in my house that I saw the word I, and when I understood this word, the book fell from my hands, and I wept. I, who had never known tears, I wept in deliverance and in pity for all mankind. I understood the blessed thing which I had called my curse. I understood why the best in me had been my sins and my transgressions, and why I had never felt guilt in my sins. I understood that centuries of chains and lashes will not kill the spirit of man, nor the sense of truth within him. I read many books for many days, then I called the golden one, and I told her what I had read and what I had learned. She looked at me and the first words she spoke were, I love you. Then I said, my dearest one, it is not proper for men to be without names. There was a time when each man had a name of his own to distinguish him from all other men. So let us choose our names. I have read of a man who lived many thousands of years ago, and of all the names in these books, his is the one I wished to bear. He took the light of the gods and brought it to men, and he taught men to be gods. And he suffered for his dean, as all bearers of light must suffer. His name was Prometheus. It shall be your name, said the golden one. And I have read of a goddess, I said, who was the mother of the earth and of all the gods. Her name was Gaia.  It shall be my name, said the golden one. Now I look ahead. My future is clear before me. The saint of the pyre had seen the future when he chose me as his heir, as the heir of all the saints and all the martyrs who came before him and who died for the same cause, for the same word, no matter what name they gave to their cause and their truth. I shall live here in my own house. I shall take my food from the earth by the toil of my own hands. I shall learn many secrets from my books. Through the years ahead, I shall rebuild the achievements of the past and open the way to carry them further. The achievements which are open to me, but close forever to my brothers, for their minds are shackled to the weakest and dullest among them. I have learned that the power of the sky was known to men long ago. They called it electricity. It was the power that moved their greatest invention. It lit this house with light that came from those globes of glass on the walls. I have found the engine which produced this light. I shall learn how to repair it and how to make it work again. I shall learn how to use the wires which carry this power. Then I shall build a barrier of wires around my home and across the paths which lead to my home. A barrier light as a cobweb.  A barrier my brothers will never be able to cross. I have nothing to fight me with, save the brute force of their numbers. I have my mind. Then here, on this mountaintop, with the world below me and nothing above me but the sun, I shall live my own truth. Gaia is pregnant with my child. He will be taught to say I and to bear the pride of it. He will be taught to walk straight on his own feet. He will be taught reverence for his own spirit. When I shall have read all the books and learned my new way, when my home will be ready and my earth tilled, I shall steal one day for the last time into the cursed city of my birth. I shall call to me my friend who has no name, save International 48818 and all those like him, Fraternity 25503 who cries without reason and Solidarity 96347 who calls for help in the night and a few others. I shall call to me all the men and women whose spirit has not been killed within them and who suffer under the yoke of their brothers. They will follow me and I shall lead them to my fortress. And here, in this uncharted wilderness, I and they, my chosen friends, my fellow builders shall write the first chapter in the new history of man. These are the last things before me. And as I stand here at the door of glory, I look behind me for the last time. I look upon the history of men which I have learned from the books and I wonder. It was a long story. And the spirit which moved it was the spirit of man's freedom. But what is freedom? Freedom from what? There is nothing to take a man's freedom away from him, save other men. To be free, a man must be free of his brothers. That is freedom. That and nothing else. At first, man was enslaved by the gods, but he broke their chains. Then he was enslaved by the kings, but he broke their chains. He was enslaved by his birth, by his kin, by his race, but he broke their chains. He declared to all his brothers that a man has rights, which neither god nor king nor other men can take away from him, no matter what their number. For hid is the right of man. And there is no right on earth above this right. And he stood on the threshold of freedom for which the blood of the centuries behind him had spilled. But then he gave up all he had won and fell lower than his savage beginning. What brought it to pass? What disaster took their reason away from men? What whiplashed them to their knees in shame and submission? The worship of the word we. When men accepted that worship, the structure of centuries collapsed about them. The structure whose every beam had come from the thought of some one man, each in his day down the ages, from the depths of some one spirit, such as spirit existed, but for its own sake. Those men who survived, those eager to obey, eager to live for one another, since they had nothing else to vindicate them, those men could neither carry on nor preserve what they had received. Thus did all thought, all science, all wisdom perish on earth. Thus did men, men with nothing to offer save their great numbers, lose the steel towers, the flying ships, the power wires, all the things that had not created and could never keep. Perhaps later some men had been born with the mind and the courage to recover these things which were lost. Perhaps these men came before the counselor of scholars. They answered as I had been answered and for the same reasons. But I still wonder how it was possible in those graceless years of transition long ago that men did not see whether they were going and went on in blindness and cowardice to their fate. I wonder, for it is hard for me to conceive how men who knew the word I could give it up and not know what they had lost. But such has been the story, for I have lived in the city of the damned and I know what horror men permitted to be brought upon them. Perhaps in those days there were a few among men, a few of clear sight and clean soul who refused to surrender that word. What agony must have been theirs before that which they saw coming and could not stop? Perhaps they cried out in protest and in warning, but men paid no heed to their warning. And they, those few, fought a hopeless battle and they perished with their banners smeared by their own blood and they chose to perish for they knew. To them I send my salute across the centuries and my pity. There's is the banner in my hand and I wish I had the power to tell them that the despair of their hearts was not to be final and their night was not without hope. For the battle they lost can never be lost. For that which they died to save can never perish. Through all the darkness, through all the shame of which men are capable, the spirit of man will remain alive on this earth. It may sleep, but it will awaken. It may wear chains, but it will break through. And man will go on. Man, not men. Here on this mountain I and my sons and my chosen friends shall build our new land and our fort and it will become as the heart of the earth. Lost and hidden at first, but beating, beating louder each day. And word of it will reach every corner of the earth and the roads of the world will become as veins which will carry the best of the world's blood to my threshold. And all my brothers and the councils of my brothers will hear of it, but they will be impotent against me. And the day will come when I shall break the chains of the earth and raise the cities of the enslaved and my home will become the capital of a world where each man will be free to exist for his own sake. For the coming of that day I shall fight. I and my sons and my chosen friends for the freedom of man, for his rights, for his life, for his honor. And here over the portals of my fort I shall cut in the stone the word which is to be my beacon and my banner the word which will not die should we all perish and battle the word which can never die on this earth for it is the heart of it and the meaning and the glory the sacred word ego End Chapter 12 End of Anthem by Anne Rand


Hello, how are you? Namaskar, how are you? I'm good, how are you? Hello, how are you? I'm good. Nice to meet you. Hello. Nice to meet you. Hello. Hello, sir. Hello. So, where am I sitting? Here? Yeah. Okay. I'm sorry, I was a bit late. I'm sorry. That's awesome. I'm Rahul. I'm Congo's president. And I'm a member of parliament from Haiti and I thought I'd have a chat with some young people and get a sense of what they're thinking. So, I don't know if they told you you were going to chat with me or are you expecting someone else? It's not a surprise to us. So, tell me. What's going on? What would you like me to discuss with you? My question is that you come from a Gandhi family in the language of media. And as the current situation in the country, the situation in which you are young, so you don't get tensed about what to do and what to do? No. Why not? What's behind it? You're being accused. If you catch that thing, it will affect you. If you don't catch it, it won't affect you. And personally, my family has been very violent. My grandmother was killed, my father was killed.  So, in a way, a person is taught. The way RSS and Narendra Modi have attacked me, this is a big gift for me. I should learn from this. Actually, really, I should learn. So, this approach, for you people, the youngster should keep this approach. If you say anything wrong, you shouldn't accept it. You listen and move forward. You do your work. Every party has its own manifesto. But what about its implementation? When Congress was in power between 2004 and 2014, we did Narega. We did right to food. We did right to information. We did Shabshik Shah Bihar. So, I mean, it's also not that there is nothing. But the basic idea of today's government is a control idea. They want control. So, when you want control, that means you want to control the Supreme Court. You want to control the election commission. You want to control the VCs. So, that's the problem. And that is their ideology. The tension in the different communities of India, whether they are Dalit, OBCs, upper class, tribals, minorities, Sikhs, Muslims, those who have tension between them, they want to ignore it. They want to ferment it. That means they want to increase it. There are more social problems than politics here. Because we never told them why there is such a thing with Dalits. Because we never taught them in school what the problem is with them. If there is a conflict with them, then why is it happening? We never told them these things. Because we never included in our Pratnik Shiksha. The problem is Pratnik Shiksha. No, that Pratnik Shiksha, people have captured that too. That is the power. They don't want to do it. They know the whole of India. They know the whole of India. They know the whole of Brahmin, Thakur, Yadav, Dalit. They know the whole of Dalit. Don't think that they don't know. Ambedkar Ji told you to organize. Educate. Educate. It's very simple. And then there is another minority that is not being even talked about. The disabled people of India. Which are not even new people. I feel politicians don't see the disabled people as a vote bank. I would say they do not. One, because polling booths are not accessible. This was the first time. I am planning to vote for the first time in the upcoming elections. But polling booths in my area are not accessible. So I don't know how am I going to vote. One of the worst things that the government has done is to not acknowledge that there is a problem. So you must have heard of this word. Divya. What does that mean? Divine body. What do you expect me to fly over stairs and get into hotels? Divine body means that they do not acknowledge that you have a disability. That there is a problem. Firstly, we have to not even differently able. There are certain things that I cannot do differently. How old are you? I am 20. 20. So there are so many things that I cannot do differently. You are pretty impressive for a 20 year old. That's a big compliment. No, you are. Thank you. Sir, that was something I wanted to convey.  There are a few points that I want to convey from my side and something that I have noted down from queer people in academic spaces from whatever limited access I have. Transmen also exist. But how many times have we seen policies for the transmen? It's not even my narrative to give out. Give me an example of what could be a policy. When every single policy or every single approach with the umbrella term transgender it's always a transforming. So can I just make one observation. She said that as far as differently able people are concerned they are not heard. You are saying the same thing in a different way. You are saying there is another group of people who is not heard. He comes from Dalit community and he is saying I am not heard like I should be. But the Dalit community has politically mobilized. Your community has not politically mobilized. Her community has not politically mobilized. So the Dalit community has got together and said we are going to transmit our voice and Kanchiram Ji played a role there. Ambedkar Ji played a role there. Jadjeevan Ram Ji played a role there. There is a series of people. And they forced India to listen to their voice. Now there is two sides to this coin. One side of the coin is the government listening to you. And the other side of the coin is you organizing yourself so that they are forced to listen to you. In today's world numbers matter. Narrative matters. How you do it matters. Coordination. Conversation that is very powerful. But it is a question of starting up. So one thing I just wanted to ask you. So there is a rule that has been brought that 10% of reservation right. So for what extent it seems genuine or it seems good to the nations. Suppose we are sitting at dinner. And there is no food on the table. And we say you know what we are going to do. We are going to reserve what is on the table for all of you. What is going to be the result of that? Is it going to be a fight? First of all if there is nothing there people are just going to look a bit confused and say what did you give us. The bottom line in India today is there are no jobs. So you can reserve anything you want. The fact of the matter is China is producing 50,000 jobs every 24 hours and India is producing 450.  What difference does it make if you give 10% to 6% to 9% you are trying to divide a pie that is tiny. Build the pie. If we are sitting here and we fill the table up it can be divided. The bottom line is you guys are facing a massive crisis. Sir, I want to ask you about Assam. Sir, there is no lack of talent in Assam. So educated and unemployed, it is a big issue. The whole country is a massive issue. Sir, we have heard a lot from Aitya about Kiman Vettan. At least we need to get wages. Sir, I believe that we talk about education and skills. So why don't we talk about equal education and have a strategy for that. My thinking is that minimum level quality education minimum level quality healthcare Indian state should try and give it to everybody. I am thinking about supporting infrastructure. What I think is that we need to help those who are facing education problems. Because the children will lead them. And they will decide the future. You mean you are talking about yourself? Exactly. So we need to give these children proper resources at least for them to grow up. Whatever they want to study it will be easily accessible. See we are building our manifesto and what we are doing is we are having an open conversation about our manifesto. I will put you in touch with the guys who are doing the manifesto. And I am quite interested in what you have said about these ramps and stuff. So I have got it in my mind. And also some of the stuff you have said. So you will see that I will start to push some of these things. I really need to run. Lovely to meet you. Thank you. Thank you sir.