From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. Recent earnings prints from Amazon and Snowflake, along with new survey data, have provided additional context on top of the two events that Snowflake and Databricks each hosted last June. Specifically, we believe that the effects of a cloud optimization are still being felt, but are nearing the end of peak negative impact on cloud companies. Snowflake's recent renewal with Microsoft on its contract better aligns sales incentives and should improve the company's traction, Snowflake's traction that is with Microsoft Azure, a platform that has long-favored Databricks. Google, however, remains a different story as their agenda is to build out its own data cloud stack rather than supporting Snowflake's aspirations. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this Breaking Analysis, George Gilbert and I clarify some of our previous assumptions around Snowflake economics. We'll also dig into the three US-based hyperscale platforms to better understand the footprint that these key data platforms have, Snowflake in particular, in those accounts along with Databricks and ahead of Google Cloud Next, will preview how we believe Google is evolving its cloud and data stacks to compete more effectively in the market. Let's start with some spending data from ETR. Now, let me describe this chart and the colors therein. So ETR has a methodology called Net Score. Net Score measures the following. The lime green is new customer ads. If you go all the way to the right to July 2023, this is the Snowflake spending profile. That's 16% of the customers in the survey said that they were new to Snowflake. 41% that forest green said that they were spending 6% or more on Snowflake in the second half or the remainder of the year. The gray is plus or minus 5%, i.e. flat. The 7% or that pinkish area is spending down 6% or worse. And the red is defecting, leaving the platform is this very small number, but it's there nonetheless.  That yellow line down below is a measure of pervasiveness, pervasion. I know it's kind of a funny word in the survey, essentially the number of mentions of Snowflake divided by the total number of mentions. So now our thinking was that the declaration or the deceleration rather in spending momentum on Snowflake was perhaps aligned with what we've been hearing from some of the customers and partners that we've been speaking with. Specifically, the customers have been telling us that they're doing the data engineering in the data prep rather than doing it inside of Snowflake, they were moving it or doing it outside of Snowflake ostensibly in doing that batch work in Spark, like Databricks or Amazon EMR, and the reasoning given to us was economics. But Snowflake shared data at its financial analyst day that contradicts our original premise. Here's a chart that CFO Mike Scarpelli showed at that meeting. Why don't we let him explain the chart? Please play the clip. No Park is taking its share. What this graph is showing you is two Spark technologies that are running within our customer base. I can see that the blue at the bottom is Snowpark. And you can see how now Snowpark consumption, this is looking at daily credits, what they're consuming has now outpacing Spark number one, and it's gonna surpass Spark number two. And so what you're seeing also is those ones we're growing within our customer base, we're growing much faster than them. So in this chart, Spark number one is probably EMR and Spark number two is likely Databricks. Now, George, we met earlier today with Snowflake senior vice president of product, Kristen Kleinerman, and he explained in more detail why Snowpark was more cost effective than Spark for doing this type of work. And he expressed this very strong conviction that Snowflake is at a good position to capture these data prep activities going forward. But George, my takeaway was that if a Snowflake customer has Snowpark, they'll keep that data inside of Snowflake. And by the way, the ETR data shows that of the 277 Snowflake accounts in the data set, nearly half also have Snowpark. And moreover, on its earnings call, Snowflake said that 63% of its global 2000 customers are using Snowpark on a weekly basis. So George, what's your takeaway on this data? I think the first takeaway is they needed Python programmability as an option for people who wanted to do data manipulation in something perhaps more expressive than SQL. They have that option now. The question is, is that rise that we're seeing in the chart a replacement for the ostensibly or perceived lower cost options using AWS EMR or Databricks for batch work? Or is it people using Python instead of SQL? And the company maintains, as Christian told us, the company maintains that they are able to measure a huge cost savings relative to the alternatives who either have to extract the data or in the case of Databricks, they're running both Databricks compute and Snowflake compute, so it's like doubly expensive. I think we don't know the full answer yet whether customers will, with the Snowpark option, be able to consolidate their entire data estate, doing data engineering, as well as the BI dashboard serving all in Snowflake. I think Snowflake has a story now and an offering, but I don't think the dust has settled as to whether they can consolidate all data the way they would like to. Now, we did ask Christian if this was an Apple's comparison. He assured us that it was. In other words, they weren't including the data movement in and out. If you included that, their advantages would have been substantially greater. As well, we've reported that when you look at Snowflake, remember, Snowflake bundles in AWS compute and storage. It charges its customers as part of the Snowflake build. Databricks does not. And so in some cases, people might be looking at the prices and saying, hmm, okay, that's more expensive to do inside of Snowflake. So it could be a perception issue. Also, there might be customers outside that don't have Snowpark or maybe just getting started with Snowflake and have more experience with Spark and they haven't really done this analysis yet. There are a number of case studies that Snowflake shared with us, will include it in the show notes as well. Now, the other thing I want to talk about is Snowflake in global 2000 accounts. On its earnings call, Mike Scarpelli talked about, and Frank Slutman as well, talked about how some of its largest customers essentially were spending at the commit levels that renewals actually were coming in at the commit levels. In other words, they weren't growing dramatically. Now, we dug into the ETR data called my friend Eric Bradley this morning and he whipped up this chart. This is Snowflake spending in the global 2000 only. So it's the same net score granularity with the greens being positive spending momentum and the reds being negative spending momentum. And that blue line is the net score. And you can see where we've highlighted in that red dotted space, basically a bottoming in those global 2000 accounts. Now remember in the previous two charts ago, it showed that continued decelerating, Mike Scarpelli will often make the point, George, that he likes whale hunting, going after those big accounts that can spend a million dollars or more, 10 million dollars or more, or even more. Now, not all the global 2000 accounts are actually those large accounts, but those are the ones that they want to go after, those are the ones that they really want to nurture. Any thoughts on that from you? Yeah, this is where we need to slice and dice the data for granularity, which you showed with the ETR, which is like Snowflake has a very compelling story for accounts that they're onboarding to say, give us all your data. We can handle all the workloads from the data preparation to the final business intelligence, dashboard, interactive workloads. The problem is what happens when a customer becomes very large and they try and put all their data consolidated in Snowflake, will they see that as economic? And again, I'm not sure that we have the data yet to see that. I think the ETR spending survey indicates that there still might be perceived cost advantages to doing the batch workloads, the data preparation outside Snowflake. And the Snowflake when they talked to us this morning, they emphasized that the cost, there was great cost in taking the data out of Snowflake doing the preparation and putting it back in Snowflake. What we never drill down on is what if you never put the prepared data in Snowflake in the first place, you do the preparation outside Snowflake and you only put the final product, the refined dashboards, dashboard ready data into Snowflake. In other words, I think it's a little ambiguous to decide what has finally happened. And the risk is that Snowflake was predicated on, the data platform is predicated on controlling all the data and yet control, the definition of control is shifting from is it in your database to is it governed by your catalog and the Databricks story can govern all your data even in other databases and the Snowflake story is they govern it if it's essentially in their database. When that's the marketing challenge that Snowflake faces which is why I actually was really happy that they shared those case studies because that's one of the questions that we had you have any examples or economic models and so they were able to readily supply them but that's going to be the international war that they're fighting with not only Databricks but others including Amazon. Speaking of which, let's take a look at Snowflake in AWS accounts, this next chart really does sort of a penetration of the data platform within AWS accounts. So if you look at this data, it's the net score on the vertical axis and it's the penetration or overlap, this is inside of AWS accounts. So we filtered the data, you could see up there in the left hand column it says filter vendors we just put in AWS and then the bottom there in that left hand column, you can see filtered end 926. That means there's 926 AWS accounts that we're filtering on. And on the right hand side of the chart you show the position of that spending momentum on the vertical axis and the end if you will on the horizontal axis of the overlap. And you can see in the upper right, we cite both of those positions. The first column in the green is the net score. Some there's some reds down there for IBM and Oracle. And then the right most column is the shared end the number of mentions. And this is that determines the plots. You can see Microsoft is all the way to the right because they're just so ubiquitous. So in other words, there's a lot of Microsoft accounts inside of AWS accounts. No surprise, the world is multi-cloud. And then you can see the snowflake in the Databricks positions Databricks is actually overtaken snowflake in terms of spending momentum. And you can see the overlap. In other words, within those 926 accounts, 24% have snowflake, 16% have Databricks. And you can see Oracle actually is quite prominent as well. And then kind of IBM, you can see them in the chart. Now, so that's inside of AWS accounts. Alex, let's go on to the next chart, the same data. Now we're looking at these data platforms inside of Azure accounts. Actually, Alex, do me a favor, go back one. I wanted to make one other point. Up in that upper right in this AWS accounts, you can see the spending momentum on snowflake is 50.9%. So 51% with a shared end of 224. Now go to the Azure chart and you can see that spending momentum in the upper right is just about the same, 50%. You can see the shared end goes down. So fewer accounts inside of Azure, but the overlap is still pretty significant. And obviously snowflake with its new alignment and incentives wants to make that go higher. You can see as well, Databricks has spending momentum in the Azure accounts. They've always done actually pretty well there. It's interesting to see the presence of snowflake inside of accounts that are running Azure. This is not, I want to be clear here, this is not necessarily snowflake running on Azure. This is more likely snowflake running on AWS. You can see the presence of AWS all the way to the right, just as it was the reverse with Microsoft inside of AWS accounts. This is, so it's snowflake running wherever, not necessarily on Azure. I just want to point that out. Now, again, keep in mind that 50% in the upper right net score. Now go to the next slide, Alex. Now we're talking about Google accounts. And you can see there's still a big presence inside of Google accounts. But notice the net score drops to 38%, okay? And this is likely due to the fact that a lot of those accounts are going to be relying on BigQuery. I'm going to talk about that in a moment. And you can see some pretty substantial overlap with both Databricks and Snowflake inside of Google Cloud platform accounts, those 489 Google Cloud platform accounts. So George, anything you'd add here, I mean the key point really is the world is multi-cloud as you and I talk about all the time. And there's a lot of upside potential for Snowflake inside of Azure, Google probably a little tougher, your take. Yeah, I think Dave, you're hitting on the key thing which is all cloud apps are becoming data apps. And so the data stack is becoming the foundation for the cloud platform, whether it's built by the hyperscaler or whether it's more like a pass the way Snowflake is trying to build it. And so the reason Snowflake is having more trouble in Google accounts is they're the only hyperscaler with a really good data platform story. And that we'll get into in a minute, which is how we expect that to expand at Google Cloud next week. Yeah, so let's take a look at some comments that Mike Scarpelli made about Google. He said, Microsoft and AWS, we have very good partnerships there. Google is the one we still need to work on. We're open, meaning Snowflake's open to that. They're just not as open to it. And George, they're not as open to it is because they really want to drive a big query. So let's pivot to Google and set up Google Cloud next. And let's talk about Google, its approach, its approach to its data stack to actually use the term data cloud. What will data applications look like George on Google Cloud? Take us through this. Okay, so two big points to make. Hyperscalers as part of expanding their market, they're trying to simplify and democratize the ability to build and run apps. And to do that, they're trying to close the gap they're trying to shrink the gap between the complexity and power of an infrastructure as a service and the simplicity but relative restrictiveness of a pass. And then the second point, the second big point is all the cloud apps are becoming data apps. Now, we think that the big unveil at Google Cloud next will be pervasive use of generative AI really as a code generator to help shrink the gap between the complexity of gap between an infrastructure as a service and a pass. We think that all three cloud platforms are doing it. We suspect Microsoft was furthest along because they started showing GitHub Copilot two years ago. And then Google was next most aggressive and then Amazon coming along. Then as far as the cloud apps themselves becoming data apps this is where we're moving from a world where you had users typing into forms to data being automatically collected or instrumented from people, places, things and activity and that drives intelligent data apps. And that's why Snowflake and Databricks are the real competition for the hyperscalers. And the last point to make is just as an introduction to what we think Google will show we think Looker is likely to be the UI that integrates at the presentation layer all these services. Yeah, so the cloud's getting complicated. It used to be so simple, right? We've been up in some EC2 and stored on S3 and now there's just hundreds and hundreds of services services across cloud, shared responsibility and security across cloud. So how do the layers of IaaS map to pass? And is there a simplification opportunity here? Yes, that's the key question. So we talked about Looker as the presentation layer as sort of the output target for all the application services such as analytics, AI apps, databases, governance can they bring that all together in Looker as the presentation layer? And then there's essentially three layers below that which we'll drill down into. And this is the part where can you put the pieces together better to make IaaS look more like paths? And Red Monk was the first analyst firm to point out the potential for Gen AI to start simplifying this. And so that would be the three layers would be can you integrate the app services with each other and can they integrate with governance and semantics? And we'll talk about what that means. Then the hardest one is DevOps simplification because that's the real Achilles heel of an infrastructure as a service. There's so much code that goes into deploying running, monitoring and remediating things that go wrong. And that whole layer is taken care of for you in a PaaS. So that's the big Achilles heel. And then there's SuperCloud is can they turn cloud from a location, which is the data centers that Google operates into an operating model where it runs software wherever out on the edge in private data centers, in smart devices and can you take a common control plane or essentially take the cloud operating system and have it run everything for you? Then I'm going to come back to SuperCloud but let's go on to sort of the next slide here. How is Google going to attempt to fit the application services together? What do you think that's going to look like? Can you explain that? Okay, there's a bunch of layers here but the first one we think, and it's a question as to how much they'll be able to show next week. But they hinted at duet AI as being the sort of their equivalent of co-pilot the generative coding co-pilot that would be on every programming surface in Google Cloud. And so can they generate a lot of the glue code that simplifies composing and integrating a lot of the services that you would use to build data apps and how, so that's one thing is how much code can they generate? And then the other is how well integrated are those analytics services so that they fit together without chewing gum and bailing wire? Then the second issue is governance. Now that we are living in a data-centric world, you want common governance across everything independent of what service uses it. And so data plex is their service that has so far lineage quality and policy management. But can it cover all data types wherever they exist? And will that eventually cover the operational databases and will it cover like hybrid multi-cloud data like Databricks is Unity aspires to? And then there's the semantic layer because this is where you take technical metadata that says what tables and columns are there and you up level it and you say, what does this data mean in terms of say bookings, billings and revenue? Those are definitions that don't really exist at the technical data level. And so the question is what tools will be able to use the LookML semantic model for analyzing data and can that semantic model work across all data? Not just business intelligence metrics. So it's how far can they extend that? And then lastly, the data itself will there be one system of truth? Now it doesn't mean it has to all exist in one place but it could be one federated repository. I guess the technical term might be one namespace for all data structured semi-structured complex data like PDF images and video where all the services work on just that repository of all the data. So not just BigQuery but like Vertex AI, the big data services, the streaming data services. Those are the key questions we're looking to see answers to. Yeah, and to the extent that they answer those questions, George that's a pretty ambitious data stack that Google is going after us. And it's likely that's the direction that they're headed. It's a great point and that's why on Google Snowflake's opportunity is probably a lot less. Bob Muglia himself has said that if BigQuery existed on AWS or I shouldn't say existed, if Amazon built BigQuery, Snowflake would be a much smaller company. And that means the opportunity on Google Cloud Platform is probably a lot smaller for Snowflake because they have a great stack that is built around data. Yeah, thank goodness for Redshift if you're Snowflake, which you remember the history there was an on-prem database that Amazon refactored for the cloud. Anyway, you've said to me that DevOps is the Achilles heel of IS. Explain why and how will Google evolve and simplify its stack for DevOps pros? And you mentioned the Redmonk commentary before what will be the role of AI in this regard? Now, this is where I didn't read much from Redmonk on this topic. I think they didn't talk about this but this has been a theme that I've been interested in for years, which is we are moving from this model that Werner Vogels, the CTO of AWS, famously said that the ethos in the cloud is you build it, you run it. And if you look at this diagram in the lower left, it's like that old Verizon cellular commercial where someone's moving around with a cell phone trying to get service and he's got 100 guys behind him and he's like, can you hear me now? Can you hear me now? The equivalent cloud version is there's a hundred DevOps professionals following you around when you build an app trying to figure out how are you gonna deploy it, run it and remediate it when something goes wrong. None of that really exists when you're using a pass. That's why there's a huge tax for using infrastructure as a service. However, if you have a coherent application model which means do your pieces fit together in an opinionated way, are they designed to fit together? That means you can build operational intelligence into AI that can understand when things go wrong how to diagnose problems and with high confidence how to suggest remediation and you can set it so that if the confidence is high enough it automatically remediates it. So the question is how far along that spectrum going from you build it, you run it to autonomous AI ops where it runs itself. How far can Google start moving on that spectrum? They announced and talked about a whole bunch of services built around Anthos or Anthos that sits on Google Cloud build, deploy, run, monitoring and a whole bunch of diagnostic services. Can they build in enough intelligence to figure out when something goes wrong, how to remediate it and essentially to make DevOps engineers much more productive. We don't know, but that's a critical thing to watch for. Less guys running around in lab coats. Right. Makes sense. So AI in, I mean, that's likely gonna be an evolution where they may be sort of reduce the false positives or maybe help you prioritize or narrow down maybe with the human in a loop and then eventually when you can build up trust that automation occurs where it's a system of agency actually taking action for you. That's really where you see it going. Exactly. And I would add one thing. Google and Microsoft have an advantage here because they built their services to be opinionated which means they, they've erred away from giving developers power and choice with let a thousand flowers in the form of services. You know, Bloom, Google, I'm sorry, Amazon built hundreds of services many of which are overlapping for different use cases but then it becomes much harder to stitch those together. So both all three can do it but Google and Microsoft should have an advantage in being able to understand how those services can fit together and simplify the DevOps and let's see how far Google can get on that spectrum. So Snowflake was one of the first companies that we pointed to when we started thinking about this notion of super cloud. And they are, we think in a good example of that it's a single global instance that spans multiple, not only multiple availability zones or regions within AWS but multiple clouds. So that gives the capability of both abstracting the underlying complexity but also the potential of data sharing. Snowflake actually, I think announced, I guess announced this past Snowflake Summit, the ability to apply Snowflake credits to any service on any cloud. So you're running AWS and Azure which we see many customers doing. We talked about that earlier with some of the overlap data. You can use Snowflake credits for whatever if you're in their marketplace across clouds different cloud services, not just one cloud. So that's kind of interesting. So what is Google's super cloud play? Are they going to go there in your view? Okay, so with Google, they've been talking for a couple years now about starting to extend some of their application services to run on other clouds like BigQuery Omni which runs on Amazon and Azure. But then they also talked about having some of their control plane or the equivalent of the cloud operating system run outside Google cloud. So the goal here is eventually as Dave, you sort of pioneered this concept with super cloud which is having programmatic services that run beyond the cloud because your applications essentially are going to be running everywhere and you need a common control plane for those. So the question is how much of the native Google cloud control can you deploy on-prem on the edge like in a factory or even on a device where you can still control it from the cloud and then have that autonomous operation that we aspire to. Anthos was a key part of this. It started out as I think basically just stateless containers which meant a very small subset of workloads but they've been expanding that. So the question is how many more workloads, how much control and how close is it to the type of control you get running in Google cloud itself? Yeah, well it seems like this is an opportunity for Google. We know we've covered extensively. Everybody has it, they're in a distant third place. So they potentially have more motivation to build these cross-cloud services. We were just at VMware Explorer this past week. That's a huge thrust of VMware and under Broadcom, Hoctan has talked about that as a growth vector. So maybe Google could catch that wave as well and bring some added momentum to that marketplace. It seems at least at this point in time Amazon's not interested in that. And I think perhaps Microsoft is a little bit more interested but that's really not the main thrust. All right, let's end with some expectations on Google Cloud Next. Let's see, no question we're going to hear a lot of GenAI. I mean, it's going to be GenAI AI AI AI AI all day long. We talked a lot about BigQuery in today's breaking analysis. It's the underpinning of Google's data cloud is a true cloud native data platform, database data platform that has gotten very high marks in the marketplace. And as George was describing this sort of and we've been reporting since earlier this year there's new breed of data apps emerging, people, places and things with a coherent semantic layer. We think Google is in an interesting position to do that. This is also the first Google Cloud Next since Mandiant closed. I think the acquisition might have been announced last year at this time but Mandiant is obviously now part of Google. So you're going to hear a lot on security and it wouldn't surprise us to hear something about the retail cloud. We had in SuperCloud two, we had Jack Greenfield on from Walmart. He talked about their triplet model where they combined an on-prem open stack instance where their crown jewels are and they're using Microsoft for a lot of their collaboration apps and Google for a lot of the data apps. Of course, a company like Walmart who's a retail competitor of Amazon is not going to use Amazon.  And finally, the cube will be there. We've got a set and John Furrier is leading. Lisa Martin will be there, Rob Streche. And one of our newer collaborators, Greg Xandavall is going to be there as well. So do you stop by and check that out? George, any final thoughts that you want to share? The only thing I would add is to emphasize on the retail cloud, Google, when Thomas Kurian came in, he made it a point of trying to differentiate Google Cloud from Microsoft and Amazon by emphasizing solutions. And they've bounced around as to where those solutions are coming from. Could they build pieces of them to they partner for them? Let's see how the story evolves this year. But that was the original differentiation. It was basically the data cloud coherence and solutions. So I think you're right to emphasize not just retail, but all industry solutions. Yeah, great. George, thanks very much. Again, always appreciate the collaboration. Love your slides and the deep insights. I really appreciate it. Thanks, good to be here. All right, and thank you for watching. I want to thank Alex Meyerson who's on production and manages the podcast. Kent Schiffman, who's in the air and not here today, but thank you, Ken. Kristen Martin and Cheryl Knight, they helped get the word out on social media and in our newsletters and Rob Hoth is our editor-in-chief over at siliconangle.com. Really appreciate that great editing. Remember, all these episodes are available as podcasts wherever you listen, just search breaking analysis podcast. I said last week we cracked a million downloads last month, really proud of that. So thank you, please subscribe if you haven't. We publish each week on wikibon.com and siliconangle.com and you can email me at david.volante at siliconangle.com or DM me at dvolante or hit us up on our LinkedIn post. Please comment, it helps. Even if the comments are negative, bring it on. And please do check out etr.ai. Great survey data. Thanks for their help today on short notice. Eric Bradley, Darren Brabham, our friends over there. We get awesome enterprise tech focus. This is Dave Vellante from theCUBE. Insights powered by ETR. Thanks for watching everybody and we'll see you next time on breaking analysis.


This game is not yours to win. It's mine. All you need concern yourself with is your ability to act when I tell you to. Without you, I am nothing. Atlas is only Salem's current target. Once she's done here, she'll move on to the rest of Remnant. There are people here who need us right now. We need to warn them! Let's go for both. Get Amity up and running and evacuate me.  I have questions for you. But first I need the one who can show me how. Bring him to me.


Hello friends, I am Sanjay Gupta. In this video, I am going to demonstrate you how you can use parametrize constructor in inheritance in Java.  You can subscribe my YouTube channel by clicking on the link which is appeared at bottom right corner of this video. You can download my app Techimus that is available on Google Play. Now coming on to the topic, I have implemented a code for demonstrating the use of parametrize constructor in inheritance. You can see class A which is having an attribute X. This is constructor of class A that is parametrize constructor. It is receiving a value inside A that value is assigned into X. Then show method which will show inside A and value of X on console. After implementation of class A, class B is defined. Class B is inheriting class A. It is having an attribute that is Y. Then it is constructor which is parametrize constructor. This constructor is receiving two values inside A and B. With the help of this super, value of A is passed to superclass that is A and value of B is used inside subclass into Y which is declared in B class. So friends, if you want to pass information to superclass, then you can use super. If you want to use information inside subclass, then you don't have to use anything. You can simply assign value into the variable. Now show method. It is calling show of superclass first and then with the help of system.out.println statement inside B and value of Y will be displayed on console. So this parametrize constructor of subclass is linked with a parametrize constructor of superclass with the help of super and show method of subclass is also linked with show method of superclass with this statement. Now third class is test which is defining main method. Inside main method an object is created of B class. So when this object will be created, two values 10 and 20 will be passed into subclass constructor. So friends, here you have to remember that control will be transferred to subclass constructor first and then it will transfer control to superclass. Then superclass constructor will execute first and then subclass. Then obj.show this calling statement will call show method of subclass. Then inside show method of subclass super.show is called which will call show method of superclass. So in both cases constructor and function execution of superclass will be done first and then execution of subclass will be done second. Now I am executing this code. You can see the output inside a value of X equals to 10 is displayed first inside B and value of Y equals to 20 is displayed after the output of superclass. This way you can implement parametrize constructor in inheritance. Remember that parametrize constructor of subclass is responsible for passing values into superclass constructor because whenever we create object of subclass, constructor of subclass is called first. So it is responsible for passing values to superclass. And if you want to call methods of superclass inside subclass methods, then you can use super keyword. This way you can implement parametrize constructor in inheritance in Java. Implement this code at your own so that you can understand the working of a parametrize constructor in Java. If you want to watch more programming related videos, you can subscribe my YouTube channel. You can connect with me by following these details which are available in front of you. Thank you for watching this video.


This is SAP's annual conference where all their business gets done, they showcase their technologies. I'm John Furrier, this is Silicon Angle and Wikibon's exclusive coverage of Sapphire. Now this is theCUBE, our flagship program. We go out to the events, extract the ceiling from the noise. We'll go wherever there's a story to be broken. Every, any action, we will go there on the ground. We are live in Orlando and behind us, you can see they're getting ready for the press conference for mobility. Really we had Bill McDermidon, a lot of sports athletes, tech athletes as we say. This is theCUBE and I'm here going to break it down so far here at Sapphire. Day one's halfway in the books. I'm going to bring in analyst, co-founder David Floyd from wikibon.org and big data analyst Jeff Kelly from Wikibon to break down the analysis, the breaking analysis here at Sapphire. We've heard some messaging. We've heard some vendor spiel. We've heard some customers. David Floyd, you're out in the ground. You're squirreling away all those nuts of value and signal from the noise. Break it down for us. What's the signal? Break away the signal from the noise for us. Okay, there's two pieces that we could look into and I think the more interesting one is the potential added value. Where does SAP go next? So what I think is the strategy that their ambition here is to start connecting different companies together. For example, the supply chains through, just to take one example, the car industry. How can they provide SAP not just for one organization but for a series of organizations pulling the pieces together, creating extra additional value by going across different companies? I think that is their long-term strategy and they're obviously struggling with it, how to get it out there. And one of the struggles that there is for them implementing this sort of thing is just the size of the database, the response time across all of these different things. So they have to start designing the system in my view in a different way. HANA is the start of it, but they have to really start thinking about database design in a completely different way. And again, with their acquisition of side-base, et cetera, they could be in a position to think in a different way about how they can go across company, how they can, for example, have mega-data centers where a particular industry is focused on it. Those are some of the exciting things that I think are in the future for SAP. I want to get Jeff Kelly in here and I want you guys to riff on the following concepts and discussions from Bill McDermott, the CEO, the co-CEO on stage said they want to know what, customers want to know what their customers are thinking, what they're interested in, what they're doing so they can be in sync and be real-time at the speed of business. That's the innovation agenda. That's essentially speed and analytics. Obviously, the cloud's going to enable that guy, so you've got big data, Jeff, there to talk about. But also, David Floyer, the cloud is under flux. We saw SAP three years ago on a path and strategy, not a lot of strategic changes for SAP. However, the force major, as they say, is HANA, has changed the game big time, the acceleration of how relevant HANA is in the equation. And also, the on-demand pressure from Amazon. So you've got the business model pressure of licensing, deployments, and integrations. At the same time, you've got the cloud is evolving and SAP doesn't have a clear cloud vision based on my certain early opinions as of today. What's your take of the cloud? And then, Jeff, I'd like you to talk about big data. Well, it'd be just to comment on the cloud thing. They've tried to have SAP as a service and to provide it. But the fundamental architecture of their system means that they have to have a separate instance for every single customer. And it becomes very little different from putting it on their own piece of hardware. So they haven't got multi-tenancy built in. They haven't got a database which really scales and becomes a true software-as-a-service type offering. And that's really what I was alluding to in those remarks about the database. They need to really start restructuring it so that they can provide that sort of service across organizations as a cloud service. That's the area that I really think they need to focus on. And if they don't do it themselves, we're going to see some of the other cloud providers come into that space. And that, to me, is the biggest risk that SAP has. If they don't change the fundamental structure of their software, they're going to have problems. So, Jeff Kelly, you commented earlier that based upon your data and talking to customers, and then recently validated by the New York Times report where Visal Sika was quoted saying that they're running clouds bigger than Salesforce.com's entire operation. That's SAP quoting. That's New York Times quoting some of the data that you've been finding the same thing. I mean, you have kind of an identity crisis. I'll see the analytics on the mobility message, home run, love that. However, the clouds influx, where that's going to fit in, it's kind of a jump ball right now. Where does Ana fit in and big data, et cetera? Right, well, I think a couple of things. One, to comment on what David was mentioning, so what we're hearing is that SAP's new HANA cloud service is really they're actually pricing this kind of similar as they would with an on-premise license, which suggests to me that you're right on, David, they don't really have a true cloud offering in that sense, that it allows you to scale up and scale down and take advantage of those real cloud capabilities for the reason people go to the cloud. In terms of big data fitting into their cloud strategy, I think, again, what we're seeing in the industry, with having talked to a lot of customers in both the Hadoop space, but also in other big data platform spaces, is a lot of companies will often start big data projects in the cloud. It's easy on Amazon, for instance, to spin up a Hadoop cluster and start doing some experimentation. What we find next, however, as we're kind of moving from that early adopter phase to the early majority, is that more and more organizations want to then take that early in the cloud deployment and move it back in-house. For a variety of reasons, it could be security, it could be just the scale of the economics actually does make sense to bring it back in-house at some time. So I think perhaps SAP is recognizing this trend and realizes we've got to make it easier for customers to get up and running with HANA quickly, so that they can start doing some of that experimentation with the analytics and predictive analytics. And then if we can bring them back in-house, great. But we really got to give them an opportunity to get up and running quickly, rather than going through the entire traditional sales cycle and getting it on-premise deployment. So I think that's really where they're trying to go with this. Again, the challenge is going to be the actual, the pricing model and the business model. There's a technology component as David talked about. And people, when they want to go in the cloud, they want that scale, the elasticity, and it doesn't appear from what I can see and from the customers I've spoken with that SAP offers that at this moment. So I was really impressed, David. I want to get your take on this because obviously at EMC World last week, we heard the transformation message. Obviously it's a big deal, IT transformation. We heard Jed York up on stage talking about how they've transformed IT. He's saying things like, we wanted to build things like in Star Trek, Star Wars. We want to eliminate the back office hardware, focus on the software to give that user experience to the fan in the billion dollar stadium that they have. But in a way, IT is looking to transform. What conversations have you had early on here with Sapphire and what's your analysis of the SAP landscape for businesses? As business tries to transform, what are you finding? Obviously virtualization of SAP, something that you've looked at recently. I know you're going to present that tomorrow. But what have you talked to any customers and practitioners here and what have you finding? Well, we talked to a number of practitioners and customers and on the infrastructure side, most customers have a strategy of moving towards IT as a service. They've woken up to the fact that if they don't do it, they're going to be outmaneuvered by the Amazons of the world and the new cloud service providers. They've got to do that. They've got to show that they are competitive with those offerings and that they can offer the same sort of deals, the same sort of services quickly, effectively, on the in-premise circumstances. The other interesting thing is that when you look at the cost of SAP on the infrastructure, et cetera, the most expensive component of that is the database itself. It's the Oracle database or even if they use the Microsoft SQL database, it's the database itself. And they are looking for ways that they can reduce that cost by consolidation. So there's a big move towards consolidation of SAP databases, consolidation of the SAP instances, getting SAP instances off old hardware that's been around for ages. I was talking to one of the customers who uses a HP 3000 system still. They must be cannibalizing the spare parts for it. So bringing those in, reducing the cost of the number of software licenses, the database licenses. Those are areas where you can save on infrastructure cost. But equally, they need to consolidate all of those SAP instances because there's opportunities to save business costs as well. By reducing the number of, for example, by having a common financial definition across all of the SAP instances and being able to reduce the number of financial analysts that they have. Okay, we're here live. This is SiliconANGLE, looking at exclusive coverage of SAP behind me. You could see the action going on. You can see that. There's a press conference. We're in the Global Communications Center. We're on the ground live in Orlando. We're going to hear from Sanjay Putin and a bunch of other execs, the president of the company, talk about mobility. We're here with David Fleuer and Jeff Kelly, senior Wikibon analysts breaking it down. I got to ask you, David, and Jeff to comment on some of the messaging. Obviously, Bill McDermott's a showman. We're going to have Schnabe tomorrow to put some more meat on the bone. But obviously, SAP loves to try out the customers. And one thing I really love about SAP's conference is it's very relevant. But they let customers do the talking. And ultimately, in my view, you're always judged by the company that you keep. And that's certainly an indicator. And they have some good customers. But up there, Jed York from the San Francisco 49 has said, we are software-driven. Now, I'll convert that to say software-led. And David, you've been doing all the seminal work around software-led infrastructure, AKA software-defined. But he was referring to, I'm not relying on the hardware. I want a software world. I want to be nimble. I don't want to build a big scoreboard for $60 million because I can guys have their own phones. They're spending the money. I'll put that money into other things. It could be analytics. But when he says software-driven, kind of take that to the next level. For customers out there that are looking at this, what does that mean? They want the flexibility of creating the infrastructure. We've got a one-minute hook here, adapting it so that they can, from a software perspective, separate out the hardware itself, the disks and the servers from the way that that's all orchestrated together and use much cheaper services, keep the hardware for longer and keep the services above it, software-led, as opposed to having standalone services and arrays. Okay, we are going to go to the live press conference now. Let's go to start in a few minutes, but we want to take a break here. We're under tight deadline by the SAP Global Communications team, we want to respect that. We're here exclusive coverage, SiliconANGLE, looking by breaking down the analysis. David Floyer and Jeff Kelly, the analysts on the ground. We are going to go where the stories are. We are here in the Global Communications Center. This is where all the access happened and talked about the top execs. Deputy Commissioner of the NBA. We have top execs from the SAP and customers right back and we're going to carry the live press conference here at Sapphire now and we'll tune in and keep watching.


On a country road, starring Howard Duff and Ida Lupino, a tale well calculated to keep you in suspense. Come on, get with it. Relax, David. We're not going to move any faster, so relax. Who can relax in a traffic jam like this? Bumper to bumper at 20 miles an hour.  This keeps up. We'll get caught in the rain.  Hey, remember that shortcut? Which one? The little country road that goes across through center moorages and comes out on the highway on the other side of the island. Oh, yes, you mean the one we took last summer? Yeah, yeah, I'll turn off there and duck this jam. I might be at home before that storm hits. Turn on the radio, will you, honey? Mm-hmm. See if you can get the news in. Police this afternoon issued two warnings to all residents of Long Island to be on the lookout for Nellie Goller, a middle-aged woman described as dangerous and insane. What's that? She escaped this morning from Restview Mental Hospital after fatally butchering a doctor, a nurse, and a ward attendant with a meat cleaver. Ha-ha! My mother-in-law. Just a few minutes ago, the decapitated bodies of an elderly man and woman were found by police near center moorages on Long Island. Well, at least we're not the only crazy people on Long Island. They're here for one not to open their doors to strangers. Motorists are cautioned to stay off lonely roads and not pick up any hitchhikers. All persons are asked to be on the lookout for this woman. Here is her description again. She is tall, gray-haired, has broad shoulders, and long arms. She is believed to be armed with a meat cleaver, which she used in her escape from Restview Mental Hospital. Turn that off, Dorothy, will you? But you wanted to hear it. Well, that's enough of that. This shortcut will save us a lot of time. David, didn't you hear what the radio said about staying off lonely roads? Well, honey, we just have to go across to the other highway. It won't take long. Are you sure? Of course. We go past center moorages, then take the left road and come out right by the highway bridge. Center moorages is where she just killed those two people.  What can happen to us while we're driving? Besides the whole island is full of men looking for this lunatic. I'll catch you. Well, here comes the storm. David. What? The gas gauge says empty. Well, there's still a couple of gallons left and it points to empty. How long has it said empty? Well, that I don't know. I'll get gas when we get across to the other highway. Darling, I'm scared. Relax, honey. Funny rain and wind always makes you jumpy as a cat. I wish we'd stayed on the highway. If I'd known you'd act like this, I would have. Well, it's not my fault. Honey, it's not mine either. I'm having trouble enough just trying to see through this storm. Oh, darling, what are we fighting about? Driving through these woods in this kind of weather is enough to give anybody the willies. I've got dark so fast. I can hardly see even in the headlight. There's something ahead. Well, don't stop, David. Oh, it's only a sign. That crazy woman could be around here. I'm not picking up anyone. I just want to know where I am. Please don't get out of the car. I'm not. Now, calm down. Center marches to the right. I'm going to take this road here. Center marches. Well, this must be the road where she killed those people. I'm sorry. I'm jumpy. Oh, David. That woman, she could be anywhere in these woods. But not necessarily where we are. Wouldn't you know? That's that. We out of gas? Oh, no. You mean we're stuck here? For the fine thing, anyway. That woman is in these woods.  She's nowhere near us. Calm down. David, quick.  Why? Did you see something? I don't know. I don't know. I'm scared. You should have gotten gas back in the main road. Oh, turn off the lights, please. Why? She'll see us if you don't. Oh, for Pete's sake. Please, David, turn them off. Listen, Dorothy. Darling, look at those headlights poking into the darkness at all the wet bushes and trees. Please, David, turn them off. Okay. All right. All right. There. Now they're off. Feel better sitting in the dark? If only the thunder and lightning would stop in this rain. Look, Dorothy, I'm going to walk up the road a bit. Maybe I can find a house. You're not going to leave me here. I won't let you go. Dorothy, we can't sit here in the middle of nowhere for the rest of the night. But we're safer here than out there.  Oh, come on, Dorothy. Why should she be right where we've run out of gas? Quick, lock the doors from the inside. All right.  She's nowhere in there here, I tell you. Please don't be mad at me. I know I'm being silly, but I can't help it. Darling.  Please forgive me. Would you turn on the radio? Maybe they've caught her. Okay.  Over 100 police are combing the wooded area. Turn it off. All right. Now, look, Dorothy, we can't sit here all night. I'll run up the road. There might be a house. I know, David. Please, please don't leave me. Listen. Hmm? Do you hear that? What? I don't hear anything. Listen. There it is. I don't hear... It's a dog.  David. Maybe it's lost, or maybe there's a house nearby. Call that woman. Ah! David! She's out there. Don't forget the back of the car. It's her. Is the door locked on your side? Yes. Yes. But what if she breaks the window? She has a clean... That's a flash. A lightning. I saw somebody. Is that her? I can't tell. It's lying on the road. Can you see her? Is she still there? It's too dark to see. I have to wait for the lightning. She's getting up now.   Calm down. Calm down. Please, please. What is she doing? I don't know. She must have been running. She didn't see the car. She ran right into it. Ah! Look at the window, David, look at her. Wait for that window. Like a gun. Worst. Dorothy, in the club compartment. Quick. I have it. Here it is. She's backing away. I'll keep on going before I start shooting. David, she's gone. She disappeared. I can't get out now. All we can do is sit here all night and wait for help. Can you see her? Where did she go? I don't know. I don't know. She's out there somewhere. I'll be planning on how to get into this car. Darling, what are we going to do? She's the one. I know. Our face was completely insane. You saw it. Yeah. She's probably back there looking for that Cleaver now.   Dorothy, Dorothy, stop. Please.  Yeah, you're crazy. We know all about you. I'm... Dry yourself off of her. Oh, darkness. And the rain. That was enough to drive me out of my mind. Just take it easy. Do you live near here? I live farther out near Resview. Resview is the mental hospital. I'm used to the crazy people, but not at night in a lonely place like this.  Do you have a rescue? No. David, we can't sit here not knowing. We could just get to a phone. A phone. Why? To call the police. Get some help. No. No, don't do that. Why not? Why don't you want the police?  But you'll be killed. What do you mean? That woman. She'll kill him if he goes away from this car. David, listen. There's that dog again. Do you hear him? Somebody's out there. Might be a house nearby. He was barking to be let in. It senses somebody. There's somebody out there. That's why it's barking. That woman's out there. Quick. Let's get away before we're caught. David, look out. Don't worry. What are you trying to do? Start the car. Quick. We'll get away. We're out of gas. Out of gas? We just can't sit here. We've got to get away. I'm not a magician, lady. I can't make gasoline out of rain.  Do you hear me? Now, let go of me. There's nothing I can do. Try something, anything. Don't just sit there. She scares me, David. Stop her. Cut it out. You'll have us all in hysterics. All right. All right, I'm sorry. Ayala, look. I've got an idea. Give me your gun. What? Give it to me. What's the matter with you? I can't sit here all night. I'll go crazy. Give me the gun. I'll go for help. I won't be afraid in the dark with the gun. No, no. Sit back. Go on. I'll go right now. Just give me the gun. We don't have a gun. It's only a pipe. Dorothy. A pipe. A smoking pipe. Oh, David. I'm sorry. Then... Then you're... You're unarmed. I'm sorry, David. It's all right. It's all right.  Why does it make a difference to you whether I'm armed or not? I don't know you. Maybe you two are more dangerous than the crazy woman. I'll tell you what I think. I think you're the crazy woman. The way you grabbed David when you wanted him to start the car. Dorothy, Dorothy. Stop it. Don't excite her. Don't excite me. Why? What can I do? Just an old woman.  We're not afraid of you.  And remember that I'm watching you all the time. David, the storm's dying down. The room's coming out. Maybe now's the time. The time for what? What are you going to do? You took her up to something? My wife meant that now is the time to go for help. No, I didn't. I meant now is the time for us to escape. David, can't you see it? She's the one.  You're not... Stop it. Sit back in that seat. You haven't got a gun. No, but I've got a knife. David... It's my pocket knife, but it's sharp. Now don't you try any tricks. All right. Threaten me. Watch me while that woman is out there. Oh, you'll make me sick. All you can do is sit and wait for her to make the next move.  It must have been on the same road. They were in their car too. They must have let her in. Oh, stop it. We'll leave then. The three of us will walk. We'll stay close together. See, we'll be quiet. Anyone hiding in the woods won't hear us. Don't get out of the car, David. We'll be killed. Easy, dear. How would we find our way? It's too dark. We'll find our way. Look out there.  Get her out of the car, David. I don't know if she wants to kill us.  If you want to go, why don't you leave? You don't need us. We're willing to stay here until help comes. I'd be helpless alone if I had a gun or something. It's your knife. Why doesn't somebody come? Where are the police? I can't stand this. Nobody will come. There's no one in these woods. No one but us. David, in our hand. I saw it in the moonlight. It clitted. What? No wife is hysterical. No, she was holding it. A long piece of broken glass, long and pointed.   David, she's going to attack us. Don't be silly. It was all right for you to have a knife. Why can't I protect myself? Give me that piece of glass. I will not. You couldn't protect us from anything. Give me that piece of glass. I won't. I won. You give it to me. Stay away from me. Once I've left the car, you're going to stab my wife with a broken glass. No! No! Give me that glass. You're crazy. Give it to me. Let me alone. Hold her, David. Hold her. Don't let her free. She's strong. She's got my arm. You? Kill me. Drop that glass. Oh, drop it. She dropped it, David.   I wasn't. Don't let her get free. I can't hold her much longer. She's strong. If you have a knife, glass will hold her, David. Hold her. I can't. Not much longer. I'm not crazy. I'm not. I'm not. She's got my throat. She's joking with me. Your knife? It's a part here. It's a police at last. Well, you see, officer, we ran out of gas. A night to run out of gas?  Officer, we want to tell you... Farmer at the road call. This said his dog's been barking at something. We caught her. Who'd you catch? The crazy woman. She's in the back seat. Dead. All right? No, no. She's still alive. Badly hurt, though.  Well, we'll get her to the hospital. You come with me.   I don't know who this lady is.


Hi everybody and welcome to what I believe is some kind of desktop tour or like UI or setup or I think workflow tour of the Denchi video YouTube channel or at least my personal desktop. I have KDE Plasma as my desktop environment on Manjaro KDE which comes pre-installed with KDE Plasma, I use various programs, most of which I detail pretty much all of which I detail in my JustUse series so obviously OBS, Resolve, Audacity, I'm not Steam, but Acreta and such they're all really good softwares obviously and today I'm gonna be showing you basically my setup not in terms of how to use these softwares I already made it like a 40 minute long video on that instead this is a setup like this is like showing how I have the UI setup what plugins I use I try to avoid using plugins and software like I only really use plugins in OBS and KDE that's pretty much it and the only KDE plugin I really have is like the theme of these things and also this audio spectrum visualizer all these things besides the audio spectrum are all stock so first thing I'll take a look at is exactly that my KDE like desktop setup so I have this isn't actually a dock this isn't like a latte dock because latte dock uses tons of resources do not use a latte dock unless you really really want latte dock and you don't care about your RAM this is actually not latte dock this is a panel from KDE Plasmacy it's it's a panel so you can right click on it edit panel it's a panel what it is it's just a panel and I made it really tall so I increased the height a lot and I just got a panel from there just putting it back I made it like restricted I made it so it goes down every time I'm not using it so you know as you can see I can go here and then it goes away like that it's like kind of like a dock that's that's pretty cool exactly the same as a latte dock but you know doesn't use like a million billion gigabytes of memory I have my weather over here yes I live in the united Arab Emirates not like that's not literally been revealed due to the fact that my channel's location is set to that CPU load monitor got my hard disk IO over here got my audio spectrum visualizer I have to set to desktop audio although if I wanted to I can go here and configure it so it just takes just a built-in this one so as you can see now I'm talking and it's just using the actual microphones input I'll keep it that way for the rest of the video because it looks cool having this little audio like visualizer this instead this is a network monitor this is basically just you know my use of network this is my memory how much memory I'm using that that's it in terms of these widgets over here got my folders the way I make my videos is I have a project data queue and a project data s folder so this is project data queue I have all look at this spoilers I have all my videos I'm working on here and each one gets its own folder all it's just like an assets like for example I've got a funny intro here got like a pippy the movie to review coming up soon hopefully with comms got still got my mlazr stuff just use bone marrow that should be deleted I already finished that video I already I always delete stuff when I when I'm done with the video because keeping project files like they occupy quite a lot of space I use mega to back stuff up so as you can see I use mega it's a very useful like um backup utility mostly because it's it's it's far more secure probably than then google drive it probably far more private actually you know cares all stuff it's actually good yeah so all my videos just get their own dedicated folders I just put stuff in here and yeah this applies pretty much all my youtube channels I have some kind of like a folder set up specifically for that channel so I would have a project data queue folder this is only really this is for all my videos on all the channels and then what I would do is I would have a specific a folder dedicated to a specific channel so then she video that's his own folder in the folder itself all the assets I use then there's like specific folders for specific things that are loads like thumbnails this is every single thumbnail of all my videos ever unused branding old branding that I don't use anymore then she is then she like the actual icons effects these are all the audio effects that I use music that I use in my video all Heinz key slaying you know modstein the it's always sunny and filled up in music scripts I don't really write many of these most of these are like already done videos or videos I'm working on this is actually a really long script I wrote quite a while back I actually recorded it and deleted the recording because I didn't feel it was uh it felt like a really long video I'll make that one later anyway so I have all my you know all my stuff that I need for actual channel stuff here I got a weird screenshot for some reason discord screenshots that's that's pretty much it in terms of then she video then she song which is the the alt account for only music I keep all the assets here got right I get a folder dedicated to each like kind of quote on quote album or like playlist I have on the channel so for example um this over here called singles is actually the virtual singer covers the first edition this is minstrel wave is another playlist on that channel musical fix it's just generic songs and re-instrumentations and another playlist of all the playlists on that channel got their own folder um then we also have a denture space program this is a little bit more marin got my thumbnails for videos I'm still you know planning to make real-time mun mission I'm working on that um still have images for the website for the space program and like stuff like this so you know like assets and such I'll quickly show you how I have my obs stuff set up I believe if I create a new instance of obs here we go this this is obs that I'm recording with obs that's kind of a an obs exception someone got obs recording and it's recording obs but um this is obs and um the way I have it set up is I have a screen recording mode for you know just in the it's recording the entire screen everything on it specific recording for basically I can just select like a window so I can go here I don't know this is the plasma if I want to select I don't know obs for example it's gonna record my obs window in it uh and I can also add more windows so I can only record specific windows if I need only a like a certain window recording and I don't want to record you know the entire desktop a black screen just black screen that's basically all it is and I have two audio waves I have audio wave and audio wave auxiliary the way these work is that I have an audio spectrum this is a spectralizer made a video on that and um I haven't actually shown this in the latest videos on the channel but I have uh well I added a thing where it scrolls because it looks cool and fancy and cool um so I added like a filters on it you can go here and click on filters it got a crop a scaling aspect ratio so it actually looks good and a scroll so as you can see it scrolls and it looks pretty cool got all the mark for like the song that I need to do I haven't actually made that modular you have to manually select the album art but you know it's still good you can't just load a song and it works uh maybe I'll get that working the reason I have two is because I have one that's consistently only for the virtual singer covers because that's gonna keep on going for a while then I have a second one dedicated to just random music that's just inconsistent and not specific so I can just have you know just this setup I just need two so I can you know have the memory of one and then the memory of another one so I can switch between the two these are different completely independent like things the only thing that remains the same between these two different setups is the media source because you're not gonna be recording two songs at once so you only really need one song between the two so that's why I haven't set up that way so that's my OBS setup I also use tuna as a plugin to show the album art although um it's not really like uh I haven't set it up yet so it's not really working I don't really have anything special in terms of vintage resolve stuff like I don't really set it up in a different way neither with crita or gimp because you can't I mean you can't kind of customize things and move them around but I I just keep it to the stock just in case I need to reinstall stuff and I don't I mean I guess that that applies to the desktop environment as well I modified that but that has to be reinstalled at some point but yeah I don't really modify things in those programs that's pretty much my entire setup I'll just show the final step of making a denture video after after I render everything in divincy resolve what I need to do is I have this little special text file called scripts so what I do is I would go to DaVinci Resolve and I would render my video and then I would it would go to my desktop as render.mov and these are some scripts I think it's there's like a site you can download like you can copy and paste this from I modified them a little bit so they would you know um encode my videos better but basically what I do is because DaVinci Resolve is kind of stupid on Linux what it does is that it can only like encode stuff into uncompressed editing formats and it can only decode video that's in uncompressed editing format so any video I need to work with I mean first of all rename it to input.mp4 rename it here then run this command in the same directory as where the video is and to encode so basically once I'm done with the video it will come up as render.mov on my desktop and then I would open up my terminal and I would go cd maybe not capital letters but cd desktop right and then I would run this script over here so I'd copy and paste this and obviously doesn't do anything because there is no render.mov as you can see render.mov no such file or directory but if there was what it would do is it would basically encode it would encode um the video and compress it so then I can upload it to YouTube because uploading raw editing format video to YouTube is probably not a good idea because it would probably take like the like half a day basically it's it's I'm talking like at some point like I think my pippy review that was around almost 40 gigabytes in size it was huge it was like undoubtedly large so anyway um I actually once I'm done uncompressing them uncompressing the the stuff I put them I basically I have a I have a drive over here named alexdisk where basically what I do is I keep um well I actually have a quick link to it here where it gives you it brings you to um then she output the folder which is basically this folder where I put every single finished video that I make I have three folders these are just random videos I made for other people so this is like videos I made for other people I have three folders over here one's then she space program on my then she space program videos other one is then she song on my then she song videos so they're all there then she video videos so these are every single then she video video not all of these are actually up not because I haven't uploaded them yet but because I haven't actually you know I've decided to take them down or something but yeah I have the then she video folder so once it's done once I've finished doing that script thing I talked to her before I just move the video to here and I have mega set up so basically what it does is it backs this entire folder then she output to the mega so I can just if I actually for some reason I lose my data I always have that mega backup although I this is a local backup so I have like a local backup as well of all the stuff so I don't actually have to you know worry about losing stuff but it's important to back things up in multiple locations um as a final thing I'll go over through like my icon pack that I use and the theme that I use um I'll just show you my theme I use um uh lion it's called lion or leon I don't know I pronounce it it's lion I don't know I use that as a theme and as an icon icon pack I use something called tele tele blue it's really really good because I really like the color blue and it's really bright and colorful and I like things that are bright and colorful like this audio spectrum over here that that looks pretty cool don't know why this keeps showing up though uh that's never happened before I'll fix that later probably because I'm recording or something every single other icon pack I have no really care for like breeze it look there's too many gradients it's ugly tele blue I like it because it's flat it's nice and it's it's it's just generally visually appealing so that's the icon pack that I use uh besides that I mean that's that's pretty much it in terms of dementia workflow I don't really do much else I mean I recently added Libra Office to my little makeshift doc over here simply because I want it to start writing more scripts and I will write more scripts eventually to make my videos more interesting but tech videos especially when it comes to like tutorial stuff works better naturally so scripts aren't really required for that kind of stuff I'll only really make scripts for things that require it although I've gone pretty much on a stupid tangent and started talking about scripts instead of talking about what I'm supposed to be talking about which is not our sponsor it's um you know my setup and how I actually use my computer to make videos so if you want actual tutorials on how to make videos I already made a 40 minute long video explaining all of it including how to set up DaVinci Resolve on Debian based distros you can actually easily install DaVinci Resolve in Manjaro by doing a Pamac install DaVinci Resolve and like I forgot the name specifically so I'm gonna use Yay instead which is a package manager DaVinci Resolve and it will search for it it will find those results and I know that's it um yeah it's it's a very good very very very good software I really recommend it it's uh it's it's uh you know it's I really hope they update it so you know you can actually import compressed sorry you can import compressed footage which you can't do like mp4s that are encoded in a certain way like pretty much every mp4 mp3s as well just does not like them compressed stuff just no uh DaVinci Resolve only really works with um editing formats on Linux although on Windows and Mac it just works without meeting all this conversion and stuff which is really annoying I just really wish they would fix that thanks for watching this video about me rambling on about DaVinci Resolve instead of actually showing you what I'm supposed to be showing you um I have a cool notification menu if that means anything look at this notification menu got all my notifications there uh thanks for watching and uh goodbye


Well, it looks like the stock trading ban for members of Congress has failed and it will be postponed until after the midterm elections so what happened they had a Really good bipartisan piece of legislation co-authored by Pramila Jayapal and Republican Matt Rosendale and The Senate even had a companion piece to that House legislation co-authored by Elizabeth Warren and Republican Steve Danes so it seemed like even if this wasn't necessarily everything that progressives wanted it to be it was Sufficient to ban members of Congress from trading stocks now. There were some loopholes It was imperfect legislation, but it was a step in the right direction But it failed and Pramila Jayapal is blaming Democratic Party leadership now is she speaking out against them in a very tattered manner Yes, but is she placing a blame on them nonetheless? Absolutely, and this is important the Hill writes Jayapal said there's been a lot of disappointment that the proposal crafted by the House Administration committee at the request of Speaker Nancy Pelosi was written behind closed doors and Derived just days before the House was to leave Washington last week for the long midterm recess Some supporters of the ban have suggested the process was designed to ensure that no vote could happen in September What we weren't really brought into the process We didn't really know what was happening with the bill and at the end of the day There were things that perhaps we could have headed off at the pass or fixed Jayapal said on a press call the way in which it was released and the timing of it and the fact that it never even Got to come to a vote and the fact that some members even of leadership were not supportive of it was very difficult for us She added so what she's trying to say without saying what she thinks explicitly is that? leadership sabotaged this bill and Centrists and Republicans have also condemned leadership in fact to her credit Abigail Spanberger who I'm no fan of Actually more harshly criticized leadership because of their role in sabotaging this legislation So for Pramila Jayapal to speak up as the co-author of this legislation I think that that is important But let me remind you what other Democrats and Republicans said this was published on September 30th in the intercept And they detail how members of Congress think leadership actually deliberately sabotage this legislation Austin Almond explains after house leaders introduced a bill to curtail stock trading by federal officials on Tuesday Democrats and Republicans pointed out the Disrepancies between the leadership approved version and the carefully negotiated bipartisan bills that predated it those Disrepenties led to several members saying they could not vote on the measure without reviewing the text and determining Whether the bill could draw enough votes in both chambers to become law when backlash to the leadership version of the bill emerged Leaders pulled it from the schedule. It wasn't a real process when Democratic staffer told the intercept There were all these things thrown in there that on the surface look good But make it so expansive that you know, it's not going to make it to the president's desk Now that's not the only Democratic staffer to go on record and say that this wasn't a real process The huge sign here that this wasn't a real process is that the Rosendale Jayapal bill wasn't even considered in the process So behind closed doors leadership crafted this bill and it wasn't what they had already Negotiated in Congress the bipartisan piece of legislation that both the House and the Senate seemingly agreed to and according to Rosendale His office believed that that bill could have actually had the votes to pass So what did leadership do well? They changed elements of the bill They improved it in some respects and then they weakened it in other respects So what would be the point of doing this while they're trying to undercut that bipartisan harmony that we see because think about this if Progressives support the improvements, but reject the weaker elements while Republicans reject the improvements But embrace the weaker elements while this is going to break up the bipartisan harmony They already agreed on the specifics So why would you introduce all of these new elements if you're trying to quickly pass something before the midterms? Well, you don't want to pass it. Hence why you introduced these elements Now some of them have already told us where they stand Steny Hoyer, for example, came out against the ban on stock trading surprise surprise and Pelosi She opposed it until she was forced to publicly support it But using her power as a leader She was able to publicly look as if she supports it while covertly sabotaging this by not producing a piece of legislation Using the framework Republicans and Democrats had already agreed to so the question is why would leadership sabotage this? I mean, we shouldn't have to ask this question. It's rhetorical, but it's pretty obvious, right? We have to say it It's because they are corrupt. They don't want to stop Trading stocks because they're getting very wealthy because of this Nancy Pelosi has a net worth of more than a hundred Million dollars and it is very obvious that she is engaging in insider trading Using her husband with the insider knowledge that she has as the speaker of the house as a member of Congress who gets access to information that everyone else isn't privy to She then relays that information to her husband and then he purchases stocks That make them rich and they're not the only ones who do this who engage in insider trading But they currently control the levers of power. So they are able to sabotage it. So That's what happened something that could have actually passed Was sabotaged by democratic party leadership because they knew That if they went with the original bipartisan framework, it would fail and what's bizarre to me is that Usually when we see Massive bipartisan support for any piece of legislation. It's usually negative. It usually You know deleteriously impacts the american people, but in this instance, this was actually good There were some loopholes again. It wasn't perfect, but it could have passed But leadership just didn't want to put it up for a vote because they know it would pass So what do they do? They present this shell of legislation that was already agreed upon Knowing it will fail and since Nancy Pelosi won't put bills up to a vote unless they have the votes to pass They just pulled it They know what they did. This is corruption and this is completely unacceptable But it's predictable for members of the democratic party leadership who are completely invested in enriching themselves This is what Nancy Pelosi does. This is what steny hoyer does and it's absolutely shameful and disgraceful When you have democrats and republicans agreeing on something like this that would curtail corruption To have democratic party leadership sabotage it is just beyond the pale So needless to say there needs to be new leadership in the democratic party or real fundamental changes will never actually happen