From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. The recent Databricks data plus AI summit attracted a large audience, and like Snowflake Summit, a strong focus on large language models, unification and bringing AI to the data. While customers are demanding a unified platform to access all their data, Databricks and Snowflake are attacking the problem from different perspectives. In our view, the market size justifies the current enthusiasm seen around both platforms, but it's unlikely that either company has a knockout blow for the other one. This is not a head-on collision in our view. Rather, Snowflake is likely years ahead in terms of operationalizing data. Meanwhile, Databricks likely has a similar lead in terms of unifying all data, all analytic data types, e.g., BI, predictive analytics and GenAI. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this Breaking Analysis, we follow up last week's research by connecting the dots on the emerging tech stack that we see forming from Databricks with an emphasis on how the company is approaching generative AI, unification, and of course, governance, and what it means for customers. Now to do so, we tapped the knowledge of three experts who attended the event, Cube Analyst, Rob Streche and George Gilbert, and AI, Market Maven, Andy Turai of Constellation Research. Gents, welcome to the program. Thanks so much for your time. Let's jump right in and unpack the Databricks conference and what it implies for the company's emerging tech stack. The three big themes of the event are seen in the left side here. One, democratization of analytics, Lakehouse IQ, that's the company's knowledge engine. Marketplace is all around data sharing with partnerships like Twilio, Dell, and Oracle of all firms. And Lakehouse apps, which is really around safely running apps. Second line there is a platform for gen AI, app development, the Mosaic ML acquisition, I'm sure we're going to talk about that so you can build your own models. Talked a lot about vector search, model serving, the Unity catalog for AI, where AI features and functions are integrated. And then the third, all important line, governance for everything. The Unity catalog with federated mesh, they talked about data mesh a couple of times and monitoring tools. And then the Lakehouse 3.0, which unifies all the different formats like Delta, Hoody, and Iceberg under Parquet. So guys, the keynote from Ali Ghazi was in my opinion, very, very strong. Matei Zaharia participated as did Nuvine from Mosaic ML and the other co-founders. You had JPMC up on stage for quite a long time. Very, very strong, as well as JetBlue and Rivian, not as compelling, but still obvious good use cases. And that was just day one, which was followed up on day two by Mark Andreessen and Eric Schmidt and other content, so really, really meaty program. Rob Streche, let's start with you. What were your thoughts on the keynote? Yeah, I thought the keynote was excellent in both days. I thought that they really did a good job. I think beyond Ali, I think got a little thrown off with having Satya on stage with him a little bit and kind of hurried through his part a little bit, but had a lot of compelling information about the direction that they wanted to take those three areas in. And I think a big piece of it was how do we own the analytics and start, I think even move up the stack. There wasn't a lot about data warehousing. There wasn't a lot about data lakes or Delta lakes and Lakehouse. It was a lot about how do we go forward from here in the use cases that they were using, not a lot about how they were really looking at the specifics underneath. So I thought that was really compelling. I think when you got into my favorite part from day two was really Eric Schmidt. I think he nailed it. I thought his part of it was just phenomenal. I think their demo of Lakehouse IQ was killer. I think one of the best demos I've seen in a long time out of them. Great, thank you, Rob. And then Andy, how would you generally grade? First of all, do you agree sort of anything you'd add to Robin? How would you grade the AI, gen AI focus, which was a big, big part of day one keynotes? Yeah, first of all, thanks for having me. I appreciate it. For a minute, I thought you called me Raven. I'm like as in Game of Thrones. I'm like, what are you talking about? So in any case, so I agree with Rob about the Lakehouse IQ demo was really, really good. So look, at the end of the day, what they're trying to do is that, as we all know, Databricks is all about going out to the data science people who are trying to build the models, right? So they have the data and then you appeal to the data science audience. You build that. And then also the secondary portion of that, what they had was towards more of the ML engineers or who will try to help productionize that. So they appealing part for them or their user person on a sort of threefold. One is the data engineering folks who will help you with all the data lineage, data governance, data security, accessibility issues and appealing to that folks. And then data science for building and creating the models. And then towards the ML engineers who will help productionize the MS models, ML models rather, AI, ML models. I think they had something for every one of the personas. And then by throwing in the new acquisition of Mosaic ML and the data unity catalog, they are trying to combine all of that and try to put ahead of themselves in the three horse race between them and Snowflake and MangaDB. I think I thought it was quite powerful, though I would have preferred to see slightly more inclusive opportunities, particularly because they are trying to go after the open source modeling and all that. I would have loved to see their integration with maybe hugging phase or with the other data source types like the hyper cloud providers. There are some areas that are weak, but I think overall it is a very compelling announcement. Great, thank you. I mean, we're basically trying to take two events which were over six days and jam them into a breaking analysis. But I want to, George, let's bring back the first chart and take us through this detailed diagram that you created, talking about Databricks emerging technology stack. George was at both shows, Andy and Rob were in San Francisco at Databricks. And so, George, you kind of caught the tail end, I guess, of the Databricks show but enough interaction with the ecosystem. So take us through this diagram. What are the salient points? Okay, so the key point is Databricks took their weakness and made it a strength, which is they're not as strong in managing data. Like there's an analytic data engine, but what they made their strength is this ability to harmonize and unify all data assets and analytic data products. And we've been talking, Dave, a lot of the last couple of months about the semantic layer and how you're going to need that if you want to build digital twins about the things in your business. And something's got to translate that down into the strings that a data manager takes care of. And the company that has done this best is Palantir. And then another friend of theCUBE is Enterprise Web. Both of these build applications on a workflow and semantic layer that allows people to build digital twins. And the Lakehouse IQ product, which Ali called the future of Databricks is it's a backdoor way to start building a semantic layer by having an LLM read the tables, the notebooks, the queries, the dashboards, org structure, all this technical data artifacts it takes and it starts to extract the business meaning of data. And that's what allows you to translate English in the business terms of every particular company into the technical data artifacts and nomenclature and org structure. It's much broader than the BI metrics that we've been hearing so much about like bookings, buildings and revenues, but it's very broad for now. And not as deep. For instance, this wouldn't replace a BI metrics layer like at scale or DBT labs or prophecy has built something similar to go deep and accelerate the development of data pipelines. But what you will do in the future is treat this as a platform for building LLM applications where you talk to the platform in English, let's say, and you're accessing an LLM and you're orchestrating it with Lang chain. These are, this is the gen AI applications that are coming and Unity itself as a catalog, which is the foundation for this, it's more than just a discovery engine, which is what traditional catalogs have been for IT. This is now, it doesn't just track data products and show you things like lineage, but you can set policy for permissions and it can push it down into things like Redshift or Snowflake. It's not there yet, but they announced it and it sounds imminent. Anyway, so this is just to recap really quickly. This is having this knowledge engine is what allowed Microsoft to take their business chat co-pilot and work across all the office documents in an enterprise. Databricks is doing the same for all the analytic data artifacts. So there's an interesting comment in here, Rob, that George has in the slide. Can Databricks SQL use AIML to short circuit Snowflake's performance lead and supporting BI workloads? A lot of skeptics, including us initially around that, but the reports from various companies are pretty positive. And then the other point, Uniform Tables as a universal repository for analytic data was a big theme at the show. Yeah, I think that what they're aiming at is how you bring it all into one house, as they would call it, and how do you see it format it and be able to work with it? Because I think to George's point, Databricks and to Andy's point for that matter, Databricks is coming at it from more of the Python side of the house, whereas really, if you were a SQL person, you went Snowflake. And I think what they're trying to do is harmonize that by showing, in fact, the demo of this, where they showed building SQL, they then transformed it from SQL into Python using Lakehouse IQ. And I think that was showing that SQL first can be a way to go. And even if you don't know the Python side of things, they'll help bring you across. So I think that also helps with the skillings gap that they're seeing from a data engineering perspective within the personas they're selling to. And Andy, the Mosaic ML announcement obviously was big. The reported number, the headline number was I think 1.3 billion or so, but everybody sort of parsed through that and said it's actually at a low, today's current valuation or sorry, the previous valuation was like 38 billion. So you can kind of cut that roughly in half. So it's really a six, let's call it a $650 million acquisition. And then ironically, Frank Slutman sort of answered the question as to whether or not companies like Snowflake and Databricks are overpaying for these assets. And Slutman basically said no in a large part because of the talent that we're getting out of these things. So Andy, your take particularly on the AI and Gen AI component of this sort of emerging technology stack. Yeah, so I got a couple of thoughts on that, but before going there to the point about the Lakers IQ and apps, one quick point I want to mention about that is the reason why some of the data on data warehouse, particularly companies like Oracle has become really, really famous and tell them to the enterprises because they move that the database instead of using it at the data multilevel data level, they tethered that with the applications, which means you just can't untether it and remove it and go somewhere else. And Databricks is kind of trying to do the combination with the Lakers IQ and Laker apps of democratizing app building and moving it to the player. So when your app was tethered to the enterprises, to Databricks, instead of at the model level what they're creating, that'll create a lot more traction. So it'll become a lot more valuable, right? So that's a quick point on that. To talk about the Mosaic ML, I did ask Ali when he was presenting, I did ask that question Rob was there about time and why you're paying this much and what are you expecting? He had some answer to it, but my thought on that is that for a couple of reasons they are getting that, right? One, at the end of the day, they want to show people that what you don't have to, so Microsoft way of doing it is that I'll build a model for you, I'll host it for you and then you come and use it as much as you want, which could get pretty expensive. But the way these companies, the other companies, the data companies or the model companies, what they're trying to do is that I'll give you an option to build your own LLM using your own data. And they are thinking, their way of thinking is that by doing this Mosaic ML that I can tell any enterprise you could build your own LLM much faster way using your own data and run your own instance on our platform, that's a whole idea. I help you train the model, you run on my platform, you also do, people are not talking about the inferencing cost, depending on how much of inferencing you do for the models, that could also run fairly high. So I'm going to give you end-to-end model, that's the goal for Databricks to go after. It's not just the data scientist person as I was talking about that I'm interested in. I have your data, I want you to move your data anywhere else, and I provide the data lineage and model lineages and data governors and security at the enterprise grade level, which they have proven already. And then I hope you create the new model LLMs and inferencing on my data. And then with the MLflow and then MLOps section they got I'll also help you production those models. So they want to become one-stop shop or AI ML model data to model to model governance to end-to-end lifecycle. Yeah, and they're going to achieve that, we'll see. And they made it well, they also made a big point about the lowering, the cost of doing that, democratizing that. There's a couple of schools of thoughts. I mean, Jassy was on TV recently saying that there's only going to be six large language models. I was talking to George Kurtz and he was basically saying, look, these things are going to get commoditized. The real values in the data, bringing your data to those, bringing AI to that data is really where the differentiation is going to be. But you had a thought on this? Yeah, yeah, I think that really back to the whole Delta uniform concept that they came out with in product, I think it was really interesting how they were bringing, not only Trino, another open source project that's used for Datamash into the fold using Delta Lake and being able to bring that into the ecosystem, it was the Oracle stuff. I mean, it was not only Oracle was Adobe and moving from iceberg tables to Delta tables using this process. I think there was a whole bunch of how do you look at the entire ecosystem? And I think they saw iceberg and I think both companies actually saw iceberg as a potential threat to both of them. It's somebody coming out with a product that was an iceberg based product to the point where they did a demo at Databricks showing how they could actually provision iceberg tables faster using their tech than you could within native iceberg, which was pretty crazy in that respect. Well, in Snowflake last year, that was sort of, there was a Delta, if you did it inside of Snowflake, it was faster. Now they're saying they sped up the native iceberg. So this is kind of, you're right. I mean, iceberg is very, very important to both of these companies. Okay, let's bring up the next sort of detailed graphic which describes the differences and similarities between Snowflake and Databricks. So George, we've got the differentiation, on the right-hand side, and we'll stack these in the written piece so you can see them better, but Databricks and Snowflake, now both unifying analytics. George, you use the example of Snowflake dominating like Oracle, which of course Snowflake would bristle at that, but there are similarities and of course the founders came out of Oracle and you have another analogy with Databricks as well. But so why don't you take us through this chart and explain all the beautiful detail here? So Snowflake's belief is that all the analytics in the world aren't great if you can't like then either push a button or have it automatically operationalize in the form of a transaction. So it's sort of like when we had the web and you had fancy catalogs on web pages, but there was no button to buy something. Similarly here, if you want to take just an analytic data product like a dashboard, whether it's a business intelligence dashboard or something that has predictive analytic in it, you would have to embed it in a external application to actually do something. Snowflake is like put it in our stack and you have one database management system that both serves the analytic and then turns that into a transaction that does something. Databricks is saying, well, we'll give you this application layer as Andy was saying with this knowledge engine which is really the semantic layer, Lakehouse IQ, which is very much like Palantir. Palantir sits on top of legacy applications and that's how you build your digital twins. That's at a high level, the different approaches. The key thing is Databricks really has captured the hearts and minds of data scientists, ML engineers, people working with semi-structured unstructured data and you could feel the energy of those people being, those people wanting Databricks to take them into the generative AI revolution. That just that energy and moving people who already are working with semi-structured and unstructured data, even if the Databricks technology isn't five years ahead of Snowflake in that area, it's the mind share that they have, that they've been working in Python for years and they have a fairly mature set of libraries and now tools to add on that. I will say one last thing that some of the stuff about everyone wanting to build their own LLM, you're gonna have these big LLMs that are general purpose that are very high end and it will make sense to have specialized ones that are trained or fine-tuned for certain tasks that are much cheaper to run and it's the tools for doing that that both Snowflake and Databricks are trying to provide. Thank you, George. You know, Rob, I want to bring something up that George and I started to explore and I think you and I have talked about it as well. When you saw the first version of this chart, you were sort of encouraging George to pop the data engineering up to a higher level and I think that's what the dotted line implies but there's a discussion that's going on that I think we started last week which was some of the customers that we talked to said, you know, we want to do a lot of that data cleansing and data engineering outside of Snowflake because it's cheaper and then we'll bring it in. Snowflake says the exact opposite. In fact, I asked Frank Slutman about this. He said, no, no, that's not true. Our early examples are saying it's way more efficient because you get all the governance and you get all this wonderful stuff wrapped in our promise essentially. There's a nuance here which is that Snowflake charges for the, they bundle in the Amazon charges. Databricks doesn't. So we're trying to figure out, okay, is it perception because you don't see the Amazon charges as part of Snowflake? It makes it look really expensive or is it actually cheaper to do outside of Snowflake and then bring it in? As well, Databricks is encouraging people to do it all inside of their data platform, inside of the OneData platform. What do you make of all this? Yeah, I think that when you look at how Databricks architecturally has gone to market using a lot of S3 or object-based storage underneath them, it behooves them to push on this as a cost competitiveness issue because for them, it helps them with those cloud providers. They're selling more capacity, but it's at a cheaper price. So when you start to look at how the stack was built, you know, again, ahead of time by Snowflake, it was really built on EC2, EBS, and some then were begrudgingly going into S3. And so I think they're using, the stack that they use underneath the hood is more expensive just out of the gate. So I think there's really, there is certain things which is why last year they did embrace bringing things together using iceberg tables, being able to push into S3, that gives them that cost competitiveness. And I think that's also a big piece of their whole message this week when the first couple of the keynotes that I caught was really how do you really drive down the cost? How do you look at it not being as expensive? And I think that was the key. It's interesting.  And so it's interesting, George, to hear you sort of talk about Snowflake in an Oracle-like context. It's very unclear right now. And as you say, the market is so big and these personas, Andy, to your point, are so different today that it's not on a head-on collision. There seems to be opportunity for both, particularly Andy, when you bring in the LLM piece. What's your bottom line on Databricks and what they presented from the standpoint of Gen AI and LLM at the show? Yeah, so before going to a quick point about cost, I didn't actually get the feeling that Databricks is any actually a lot cheaper. So I had a conversation with three large customers at the conference. And all three of them actually suggested actually two of them are Snowflake customers, big Snowflake customers. And they are actually moving into Databricks for a couple of reasons. One, building models for them were extremely easy to use Databricks versus Snowflake, what they're doing now. And two, these are customers we are talking about anyway between 200 models and production. So the largest one had about 2,000 models in production. And they were suggesting their cost overall cost was a lot cheaper for them to do. Again, potentially Databricks showcase their customers to talk to me, but that's what I heard. So the cost-wise, I think Databricks might have an advantage when you're talking about machine learning and AI model production getting into the market and so on and so forth. Talking about the Mosaic ML, I see the reason why they acquired them is probably because of a couple of reasons. The one, obviously Mosaic ML is known for their open source LLM as you might know, it's called the MPT LLMs. That actually, I didn't realize that, but that's the most downloaded open source LLM as of today, about close to three and a half or four million downloads. So the user base right up there with four million data scientists or the users, even casual users, bring them into your fold. That's really a good key acquisition because of that. And the second one actually somebody suggested which I didn't realize until I had a deep down conversation. Mosaic ML actually somehow they acquired a group of or have a pile of GPU inventory that they're using it to train ML models that they acquired, or the course of last year or two when they started doing this. That's actually apparently quite a huge number. And that actually goes back to the fact that I was wondering how come NVIDIA chooses to go to the Snowflake conference and announces their partnership with them instead of the Databricks at their own conference. I kind of was a little babbled by that. But now I'm thinking maybe they tried that and because it didn't work out, this is their fallback strategy to acquire some GPU assets. That's another possibility. And of course, the showing the enterprises an easy way to train an LLM, your own models is the way it's going to be going forward because nobody's going to use an existing chat GPT or other models because the inferencing cost could be extremely expensive if you start using them. So if you had to start building your own LLMs what's the best way to go to a company which has a pile of GPU knowledge base that's available that can help you train and then show them how to do it. They're going to execute it based on the numbers but they got, I don't know, we'll have to see. But it looks like a decent acquisition on the surface. Well, and again, Slutman addressed this when he was asked about Neva. He's like, look, it's really hard to get the talent. Then the second point there, George, you saw this is essentially Snowflake's betting on the NVIDIA stack, they're containerizing it and your premise, George, was a hope to sort of leapfrog the ML AI tool chain that Databricks is so famous for, Leapfrog, which is unsupervised learning and try to leapfrog it with supervised learning. Although from what I saw from the Snowflake, sorry, the Databricks event, I mean, I think Databricks is doing that to themselves. They're trying to leapfrog themselves. I think you're right, Dave, that Snowflake saw this as a chance to reset as they called it, as Christian Kleinerman actually said on the CUBE interview with us and with NVIDIA. And I think what's happening is Databricks did a really hard pivot sometime in the last six months. And then the Mosaic ML tools really are, as Ali said, it just works. You fill out a configuration file and it trains. Now, not everyone is gonna wanna train their own model, but what was stunning was to hear Patrick Wendell say that using Databricks existing tools, already 1,500 customers had trained LLMs on the Databricks platform in the last 12 months, I think it was, and their consumption of GPUs is growing 25% month over month. So there is a demand out there to build specialized models and Databricks is trying to say, hey, between what we've built so far and what we're buying, we're gonna be the most accessible and easy to use place to do this training and fine tuning. That was impressive. Yeah, okay. One of the big things that Databricks focused on was sort of reimagining the database. Instead of going after Snowflake, they didn't say that, they didn't have to, but they had a Reynolds Chin up on stage who was a co-founder and database expert talking about how they're thinking, they being Databricks, thinking about reinventing the data warehouse. And of course, they say data warehouse all the time. This is why Snowflake doesn't like the term data warehouse. So you see that little marketing tit for tat. But it basically talking about, Reynolds was talking about, eliminating the trade-offs between query optimization, i.e. performance, cost and simplicity. And his argument was that Databricks has figured out how to give you all three with no trade-offs. And he mentioned some companies search optimization service. Of course, he was talking about Snowflake, although he didn't mention them by name and how they were expensive. That service was expensive, citing the trade-off right there and other deficiencies. Now, the other thing that Databricks stresses is its openness, of course. That's always been their strong card. And I'd like to have you guys listen to Databricks co-founder Matej Zaharia on theCUBE with John Furrier addressing this topic. And then we're going to come back and talk about it, please play the clip. One of the big things we've always bet on is basically open interfaces. So that means open storage format. So you can use any computing engine and platform with it, open APIs like Apache Spark and MLflow and so on. Because we think that will give customers a lot more choice and ultimately lead to a better architecture for their company. That's going to last for decades as they build out these applications. So we're doing everything in that way where if some new thing comes around that's better at ML training than we are or better at SQL analytics or whatever, you can actually connect it to your data. You don't have to re-platform your whole enterprise, maybe lose out some capabilities you like from Databricks in order to get this other thing. And you don't have to copy data back and forth and generate zillions of dollars of data movement. Yeah, great. Okay, so those are compelling statements that resonate. There's two sides to this. You've got the open standards and you've got the de facto standards. And history would actually show that when you think about like an Oracle, they were able to deliver unique value. At the same time, it's a new world. And to Matei's point, things change so fast you want to be able to plug them in. So Rob, on sort of those two fronts that we just talked about, what is your take? Yeah, I'm on record multiple times saying don't bet against open and open source in particular. And I think where they went with ML flow, well Delta Lake is open source which is that data warehousing technology. The Lake House is really based on Apache Spark and ML flow. They actually had two additional open source contributions that they were bringing to that and open sourcing during that whole thing. So I think that was during that open source, Ronald went into a whole bunch on open source. And I think, again, it's putting that barrier up for Snowflake saying your closed source, old school, Oracle-like, we're new source, open source. And we're really trying to put that moat, using it as a moat, I guess you could say, against Snowflake. And now George, of course, Snowflake would retort, well, we're giving you access to open formats, all data types, many, many different query options. Is what's your take on this, George? I think it was interesting to see like how Delta Lake was downloaded, something like, I think 500 million times and maybe Spark, something like a billion. But the big advantage is Databricks isn't just saying, look, we're open source, they're saying, look, we're gonna run it for you. That's the value of the Databricks service. They're running this software for people. And the openness is the format now, the uniform format where it's both Delta Tables, all three Delta Tables, Iceberg and Hoody. That is a big advantage because they're saying, put your data in any of these formats and you can run any compute engine on it. That is more open than Snowflake right now, which is basically just supporting Iceberg tables. And they're either managed by Snowflake or unmanaged, which means Snowflake can access them. It's not a huge difference, but Databricks is still drafting on the coattails of their original open source routes. Yeah, and I got the stats here. So it says Spark is a billion downloads per year, Delta Lake, half a million downloads per year, and then MLflow, 120 million downloads per year. So pretty substantial numbers there. Okay, I want to add some context here and bring in some ETR data. ETR began tracking Databricks entry into the data warehouse market, Delta Lake, Rob, you just mentioned. Only recently, let me unpack this chart. This shows the granularity of ETR's net score so that lime green is new customers. This is the percent of customers that are adding new, that's the lime green, spending more, that's 54% in that far right, spending flat plus or minus 5%, and then that little pink, which is tiny, is down 5% or more, and then the red is churned. So there's virtually no churn since they started tracking this. And you subtract the reds from the greens and you get net score, which is that blue line, and you can see it's popping up. And then that yellow line down the bottom is presence or provision in the data set. So it's like an indicator of market share or presence inside the data set. It's not overall market, but it's an indicator. And then if you now go on to the next chart, if you compare that with the snowflake data inside of the ETR data set, here's what it looks like. So much more history, which underscores the maturity that we were talking about in terms of the data management platforms. And if you compare the reverse, i.e. Databricks take their strong suit, which is MLAI, with that of snowflake, you'd see the reverse, where Databricks has much, much more history. In fact, it's even more favorable probably for Databricks as snowflake is really just entering that space. So guys, let's talk about the question that everybody debates. Is it harder for snowflake to encroach on Databricks territory or Databricks to get into database and data management? Or is the market so big it doesn't even matter, Rob? Yeah, I mean, I think the market is so big it really doesn't matter. I think there's so much to steal from other people, besides just stealing from each other. And I think we've left out, and I think on one of George's charts he has, with the uniform and with some of the Delta Lake or Delta sharing, things like BigQuery. BigQuery is still out there and used a lot for a lot of analytical workloads that are out there. There's still a lot of Oracle out in the world. Both of these have not gone away yet, and there's still other things out there that are gonna play to both the strengths of these two competitors. And I think they can go after those folks as well. And I think they are, especially when you go and look at everybody going after the 10 or so different databases that Amazon has. None of them have bubbled up to be this kind of competitor. Unlike BigQuery, and I don't even know, I think Microsoft's on their fourth version of an analytical version of this now with their newest one out there. So I think, again, you start to look at it. There's a lot for them to go in. The TAM is just massive still. So George, you went into the Databricks event, kind of with the attitude of saying that Databricks are largely middleware, and unless they have a database or data management platform and really can get heavily into that BI space, it's gonna be harder for them to justify their market cap, which we think is now down around low 20s. Did you change your mind at all? Yes, and if I had a hat and a scissors and something palatable, I'd cut it up and try and eat it. But what I would say, and this is I may be dating myself, this is, they started out, Databricks started out like Informatica. This was a data engineering pipeline that took raw data and created sort of normalized tables that were ready for analytics. And then many shops had both Databricks and Snowflake and they would serve the analytics to BI users who wanted dashboards, highly concurrent and interactive dashboards. What they're doing now to mix metaphors a little bit, they're taking what in on-prem world Informatica did well with pipelines and they're adding the application server that WebLogic made famous. So they're doing both the data engineering and the application platform. And they're not yet a database management system that does operational and analytic data. But by owning those two pieces, they're trying to harness the data engineers and the data scientists and then build these analytic apps on this layer that's a combination of Unity and Lakehouse IQ. And that's essentially what Palantir has done. They're becoming or trying to become an application platform and then Snowflake is when I call them Oracle, I mean it in a complimentary terms. They are a dominant DBMS that unifies all data types behind one query and transaction manager. So they're two very different strategies. Yeah, Nandy, I'd love your thoughts on the market and the opportunity for each of these. It feels like the TAM is quite enormous. And then LLMs and generative AI just blow the top off the thing. Yeah, so the TAM is big because again, the potential out of the possible everybody's talking about is huge, right? I'm actually working on an article on this friend which should be published on in the AI world war as they call it. The alliances are drawn, right? Predominantly the model creation on the data are three horsemen race, which the third one we didn't even talk about to discuss that much today, which is the MongoDB with its own vector search and the whole nine yards and the announcement what they're making. That's one horsemen. And the second would be the Snowflake which acquired Streamlet and NVIDIA now and with their native application framework. So they're going out to the same market and then of course you've got a the Databricks combo that they announced and all three of them are trying to go after saying that you know what, we have the best possible way for you to create whether it's a LLM or small language models or mission learning models and get into production. So that's their overall view of these three horsemen. But then there's also the other players that are in the market that they're trying to partner with them to see how they can get peace of the market too. Good example of that would be AWS providing a Lego components as you and I talk about all the time whether it's a hugging phase bringing models in or doing some other way to do that. And then HP takes a totally different angle on this. They're like, you know what, forget about all of that. We will give you the high density, super density as they call it, super computer service.  We'll figure out a way to do that. You don't have to go all the way to configure to the lower possible level.  Don't worry about any of that. Just give it to us, we'll take care of it for you and then it'll be not cheaper, more compliant and sustainable, whatever would have you. So everybody has their own story. They're all telling their story, their own version of the story. Which one is going to win? It's going hard to predict right now but they all have their own different versions of the story and the time is so big right now the way that people are looking at it. It's, you know, everybody has got an opportunity. You know, it's going to take maybe a year or two to figure out who's the real winner in this but they all can score some sizable market. Yeah and I think the market has grown so much. You know, the tech industry used to be a winner take all or certainly winner take most. Not really the case in cloud, although recent leak data suggests that Azure maybe is not as big as we thought but it's still freaking enormous as is GCP even though they're a distant third. But I'm glad you guys brought up the cloud players because they've all got their own sort of database strategies, big query, really solid Amazon. You know, Amazon is going to stitch together. It's all those different databases and try to minimize the ETLing and all those, by the way, in the ETR data are above that magic 40% mark, the cloud players, Snowflake and Databricks all, you know, either at or well above the 40% net score mark which is an indicator of momentum which speaks a lot to the, again, size of the market and the momentum these guys have. And then Mongo was just below but is making some moves and has had a lot of momentum lately.  Yeah, the big five themes as it relates to the expanding Databricks universe. Last year we talked about the expanding Snowflake universe and you can see both universes big, right? So broadening access to analytics was big theme, major investments of course in generative AI with the Mosaic ML announcement. AI apps with a lot of optionality, they really stressed different options. Unified governance is a huge thing. George, I'd love your thoughts on that in terms of, you know, where it stands relative to Snowflake's promise because that really is the fundamental strength of Snowflake. This single platform strategy we're hearing from both companies and then again, this expanding ecosystem. We didn't talk much about it. I didn't physically see the ecosystem at Databricks Summit. The ecosystem at Snowflake was awesome. It was so big they had to go into two halls. George, I'll start with you. Your premise going into the event again was, Databricks was largely selling middleware.  So the story's evolving into data management and a fairly big well, what's your bottom line? These are the new data, these are the new application platforms because now that we're building applications that program the real world, the API is a company's data assets. So that's why the cloud vendors care about data platforms and the two leading ones now are Databricks and Snowflake. But there's an issue that came up before about people moving their pipelines off Snowflake whether it's perception or reality, the very vendors that defined the modern data stack, 5Tran, DBT, and then companies building on DBT all told me they have a lot of movement off of Snowflake for the core data engineering pipeline. And whether it's because the Snowflake engine was optimized for interactive and concurrent queries as opposed to batch workloads, I don't know, but there is that perception. The reason I bring that up is that breaks the unified governance story that Snowflake offers. Databricks offers unified governance for heterogeneous data assets. That's the genius of unity and that's what makes it so strategic. They're trying to say, will govern assets wherever they are? So these are the two platforms for building data applications of the future. And I will say that the energy behind the generative AI enthusiasm was captured more by Databricks than by Snowflake and even more by Databricks than by Microsoft when I was at the build conference. Yeah, and while Snowflake, I think had a really good strong story about building apps on its platform, the number of apps that we saw were limited. So they got a ways to go. I think it was maybe a couple of dozen, but they're off to a good start, but we've got to see more there. And they're bringing together their dev conference and Snowflake Summit next year, so we'll be able to track the progress there, Rob. What's your bottom line? Yeah, I agree with everything that George said, and I would even go a little bit further in saying that Databricks is a little bit behind on their ecosystem. And to your point, I think that there was a vibrant ecosystem very fervent. It's almost the same people who were at the Snowflake. Summit as well. So I think there's heavy overlap between the two. Snowflake announced Snowpipe, which is supposed to be kind of a battle about getting them from being seen as operational to being more of that real-time data ingest. And to me, again, it doesn't work, it doesn't not work. Other people have worked around it already and built their own homegrown solutions. I think Databricks still has a lead in that real-time personalization that George brought up with 5Tran and taking that first-party data and being able to do something with it in a data product. I think Databricks is talking to the data engineering and above people, whereas you still have a lot of Snowflake talking to this. The word sequel was used in an ordinate amount of times in the keynotes that I saw. And I think that's not what people are working in. And I think that plays to Databricks' strengths going forward. I think their marketplace, their apps and their ecosystem is less mature, but I think they definitely have a huge amount of momentum behind them. And I think it was really that momentum was on stage at the summit this year. All right, thank you. All right, Andy, you bring us home. What's your bottom line? Okay, so my view is that in a couple of areas, Databricks has a slight leg up. For example, things like data lineage, we talked about model lineage, model creation tracking. Things like when you're creating models, the data visibility of who can create what models and where it can be visible. They got a slight advantage over the other two. And then also, how do you productionalize all of these models, whether it's the mission learning models, ML ops capabilities, or even when you create LLMs, the LLM ops capabilities, that's a huge market LLM ops now. And the other two guys are not thinking about it, whereas Databricks is slightly ahead of it. So, among the comparable capabilities of model creation, model maintenance, model management, data governance, I would say Databricks has a slight advantage over that. But there's another thing that we didn't even discuss about. It's about having a visibility, inferencing, privacy, and all that when you're deploying the models, when it comes to, can you deploy the same model in EU? And if you get the data, what kind of a regulation you'll have in there, that operationalization portion of that, none of them have it now. I call it as unknown unknowns. All these enterprises, when they hit to that stage, they're going to look forward to say, you know what, I need to build these things. Who's going to provide me the most? Right now, nobody's thinking that far ahead because not many LLMs have gone to production phase these issues. When they come out, then probably they'll regroup and say that how we can do this better. So that's going to come up and it's going to figure out on how, who can adapt faster and provide those solutions as we move along at this fast pace. So my bottom line conclusion is among this race, I cautiously declared Databricks winner compared to the other two, but that has to be seen. Well, I have to say the keynotes which sort of breaks from convention post COVID were almost three hours, but they held our attention because they were so meaty and so strong.  I also really liked the way that they showcased the founders. I think they had four founders on stage. And I think Ali and Matei are always front and center, but they brought in the other founders who are made of, I don't know what the history is of those other guys, but anyway, they had all four on stage. I thought Snowflake really under stated the founder presence. Now Thierry rarely goes in front of folks, right? But Benoit usually is front and center and he was there, but I didn't think he got enough time. I would have liked to see more of that. So the fact that they were able to hold our attention for so long was pretty compelling. The last thing I'll say is I'm kind of waiting for this IPO so we can actually unpack some of this stuff. They've been giving us hints. You would have thought given the tech run up in the first half of this year, they would have taken advantage of it. I think it's penguin on the iceberg. They don't want to be first and now people are sort of tapping the brakes so we'll see what happens there. But I want to thank you guys for helping us in our audience really understand, connect the dots. There's a lot here and it's a complicated topic. And I think there are a lot of customers out there trying to squint through it and your expertise is really greatly appreciated. So thank you. All right, I also want to thank Alex Meyerson who's on production and manages the podcast as well as Ken Schiffman, Kristen Martin and Cheryl Knight helped get the word out on social media and in our newsletters and Rob Hofers our editor-in-chief over at siliconangle.com. He does some great editing. Remember all these episodes are available as podcasts wherever you listen, just search breaking analysis podcast and appreciate you subscribing. I publish each week on wikibon.com and siliconangle.com. If you want to get in touch with email david.volante at siliconangle.com you can DM me at dvolante or comment on our LinkedIn posts. Check out etr.ai. They got great survey data, the best in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching everybody and we'll see you next time on breaking analysis.


to his concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law to the 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law to the 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law welcome to the 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law 2022 state house Christmas concert it's going to be a fun evening right here at the state house banquet hall we have an amazing lineup you don't want to miss so sit back relax and be prepared to sing the law ladies and gentlemen please rise for his excellency the vice president of the federal republic of Nigeria professor Yemi Oshiba Joe S.A.N.G.C.O.N. and his lovely wife Mrs. Dolako Oshiba please remain standing as we take the national anthem God is enthroned upon the praises of his people and here to lead us as we proclaim the Messiah and gentlemen please rise for his excellency the vice president of the federal republic of Nigeria professor Yemi Oshiba Joe S.A.N.G.C.O.N. and his lovely wife Mrs. Dolako Oshiba Joe please remain standing as we take the national anthem God is enthroned upon the praises of his people loving father we have gathered here coming from different parts of our nation for the 23rd edition of the Christmas praise concert and carols marking the 2022 Christmas season we gather in the spirit of brotherhood and sisterhood to prepare for the coming of your son this Christmas help us to take this moment to pray and to reflect on the true meaning of Christmas Almighty God grants that the new birth of your son in the flesh redeems us from the old slavery under the yoke of sin so we may receive him with joy as our Messiah our Redeemer and our King put into our hearts the peace which we seek and which you alone can give us help us in Nigeria to know one another better and to live as brothers and sisters children of the same father irrespective of ethnic group religion political association or social status oh Lord awaken in our hearts love and gratitude for your infinite goodness join us all together in your love and give us your heavenly peace as we reflect today on the birth of the Savior deliver us from every evil by the blessing which Christ brings and teach us to celebrate Christmas with hearts free of sin and evil let your mercy reach us from the heavens Lord caress us embrace us and pierce our hearts with your love touch the hearts of those who do evil in whatever form that they will stop touch renew and transform the hearts of those who in this country are planning to cause violence civil disturbance pain suffering displacement and death cause them to have a change of heart Lord God come to us fill our hearts with joy and grant us the light in Christmas which is faith the warmth of Christmas which is love the radiance of Christmas which is purity the righteousness of Christmas which is justice the belief in Christmas which is truth the all of Christmas which is Christ may this program be gone in your holy name be happily ended in your name through Christ our Lord and may almighty God continue to bless us may we be solidly united in love enjoy and peace as fellow Nigerians and people of the same human race all these we ask through Jesus Christ our Lord the Bible says ladies and gentlemen we can please be seated your excellency vice-president Yemi or Shiba Joe your excellency the wife of the vice-president mrs. Delacro or Shiba Joe your excellences distinguished ladies and gentlemen my name is Lanquie Omoele and it is my distinct pleasure to welcome you to the 2022 Statehouse Christmas Carol concert themed the Messiah this evening we shall celebrate our Lord and Savior Jesus Christ the Messiah of the world and as we proceed I would like to recognize distinguished members of the Senate here present members of the House of Representative members of the judiciary the secretary to the government of the Federation the head of the service and members of the federal executive council our royal fathers here present members of the diplomatic corps distinguished guests ladies and gentlemen you're indeed welcome to what promises to be a truly exciting evening I'd also like to request that you do me a favor that you look around you and give a warm welcome to those around you wishing them if you will welcome in your local language and also Merry Christmas in your local language may I request that you please give do that very very simple tests thank you very much fantastic fantastic fantastic I see that we've all passed the test if anybody didn't do it to you please look for somebody else one more tests one more tests can you just say in any language God bless you in any language God bless you I think that you all deserve a round of applause you may ask that you please give yourselves a very warm round of applause thank you very much indeed we shall be proceeding with this event the Bible tells us that out of the mouth of babes the Lord has ordained praise please give a very warm welcome to the ASO Villa chapel children choir where is this king? Bethlehem of Judea okay go now find this king when you find him bring word to me so that I may go and worship him yes your mother's too small Bethlehem was not so in the beginning this is how the story began I'm an angel I've got a message for you a message? okay you're gonna have a baby my Jojo don't worry you'll be fine it's the Holy Son of the Moe let your will be done let your will be done let your will be done let your will be done we have another youthful group to come minister to us and they are located here in Abuja please give a resounding applause as we receive the Eagles Music Academy Abuja I sense a bit of bias at the back is it because they are a bit alright can we please welcome the Eagle Music Academy Abuja thank you very much


Thank you Mr. Speaker. Good morning colleagues. Mr. Speaker, before I address the substantive motion in front of us, I just want to comment on two matters. One has to deal with your pronouncements at our last sitting, where you touted the achievements of parliament. But still this nagging issue, Mr. Speaker, where copies of hindsight cannot be made available and we have to rely on recordings. And you can well imagine the hustle it is to go through a recording to try to identify something that was said. So I am hoping that we can see some significant improvement in that aspect of it. But I commend you on the... If I can just respond right away. We have written to the office of the prime minister for the establishment of a separate handset. What do you call the transcribing unit? And I think we are getting some positive response. So we will be able to update the handsets. But if you need a specific report, we can get a transcribe for you. Mr. Speaker, because I wanted to remind the honorable prime minister on a matter that when we had first brought the draft of the health and security levy for general observation and discussion, one of the recommendations that I brought forward to him was the whole issue of recognizing the sanitary napkins, how important it was to include it as one of the exempt items. And I was very happy that he did it, Mr. Speaker.  Thank you. Mr. Speaker, the other thing the prime minister said in his opening remarks, and he continually said it, but there is a misleading item to it, a misleading part of it, where he continues to say that he provides resources to the members opposite. In fact, in one news item I read that he said I provide the necessary resources to the members opposite. And while it is true, Mr. Speaker, and my colleague from then Rinoff continuously reminds me that he served for six years and he did not get a cent. He continues to remind me I need to be grateful, you know, and he continuously reminds me whenever I said to him, but the prime minister is misleading when he gives the impression because the people in my community believe that I am receiving similar resources like the members of cabinet. And I get a lot of pressure because they say that I get work and I send it back. So I want to set the record straight, you know, that while I'm appreciative of what comes, Mr. Speaker, but it's misleading to give the general impression that we are getting the necessary resources to do work in our community. Mr. Speaker, I move to the substantive discussion. Yes, I get $100,000 a quarter. Mr. Speaker, there's one thing that we agree on on both sides of the house. There's one thing I agree on. We agree on on both sides of the house, Mr. Speaker, and that is water is life. We agree that water is life. And I believe Wasco has been an unfair target by many solutions. And I also believe, Mr. Speaker, that Wasco has suffered even significant greater challenges since Hurricane Thomas. And no did a thing that we need to recognize, Mr. Speaker. But my colleague to my direct left, Mr. Speaker, I must say, you know, he looks very striking in his colors today, you know, colors of he tried to represent colors of Wasco, but he needs a deeper blue. But and we get along quite well, Mr. Speaker, because, you know, we have various similarities in our socializing, Mr. Speaker. But Mr. Speaker, he said something, and I just I just want for the record, because when the when the prime minister referred to the the past years, what a supply project in last season, something I remembered, a similar discussion when when we were in office. Because I remember when the past years project was mentioned, there was a discussion about feral pigs, kosher mawan, and the damage that it does to the various catchment areas. And I remember because I asked permission then, if I could go and hunt some of these kosher mawan. So I remember and I remember, Mr. Speaker, very distinctly. And that's why I had to get it. A press release, that was released on Wednesday, November 18, 2020, to the past years water upgrade. And Mr. Speaker, if you have permission, I will just residents of past years will have their waterways address with the soon to be commissioned water project in the community. Less than one year ago on November 25, 2019, to be exact, officials from Wasco and the San Lucia Social Development Fund, SSDAF held a contract signing ceremony for the past years, Meekood water supply improvement project. The scope of the project included a replacement of a 50,000 gallon water tank with 100,000 gallon tank and repairs to the water tank at Lombard as well as some drainage work. The project was promised by the parliamentary rep for Meekood North Honourable Dr. Gail Rigobot. To date, works have been completed on the Lombard tank. The old one was demolished and a new 100,000 gallon tank erected and the necessary connections are now in place. Repairs on the existing 50,000 gallon tank included a replacement of faulty sheets and thorough cleaning of the tank. The resurfacing of the access to the Lombard tank site was also part of the project and has since been completed with only the drainage works now outstanding. Mr. Speaker, I speak to that because, you know, while the member indicated that some work had started, it was very flitting in his comment, you know. And you know, I'm always intrigued when we speak about Wasco and the providing water because and the reason I'm intrigued, Mr. Speaker, because, you know, we in 2023 and you would think that this is one of the basic things that, you know, must be provided and must be, you know, identified by various governments to ensure that, you know, our population does not have that. Because, Mr. Speaker, if anything, water is one of the things that touch the very lives, the souls of every single resident. And maybe one day, maybe one day, we will identify, and I'm not just talking about, you know, anybody that we seem to have really, and I've spoken about it on several occasions to speak the amount of poverty in our country, but maybe one day we'll be able to identify the people who truly cannot pay water and Wasco and provide maybe a limit. And, you know, that could be one of the subsidies that government can, you know, provide to some of these very, very vulnerable families, Mr. Speaker. Because, you know, it is, it is, it is downright shame, crying shame that in 2023, there are people who without running water as a result of not being able to pay a Wasco bill. And I speak to the very poor and vulnerable people who really have no means, you know, Mr. Speaker. So I'm hoping that that day will come, Mr. Speaker. But Mr. Speaker, I spoke to some of the challenges that Wasco has. And just before the meeting office, Mr. Speaker, we wrote to BNTF under the 10th program, Mr. Speaker, to identify a 100,000 gallon tank for Victoria Choiselle. And the grant was approved. But to date, you know, we're still without that tank. I have noticed that the platform has been erected. And I'm hoping that that tank will be in place before the next dry season, Mr. Speaker. Because that tank is supposed to benefit at least 13 communities in the constituency of Choiselle, Salty Bus. And now that I've mentioned Salty Bus, Mr. Speaker, I took personal responsibility to visit the catchment in the Salty Bus area and recognize that it's a catchment that is no longer, it has served its time, Mr. Speaker. And I felt that we need to identify another place where we could harvest that water to provide to the people of Salty Bus. And I took it on my own. I invited staff of Wasco, along with members of the community, and we trekked upriver, Mr. Speaker, to a place called Wavin Mabet. I'm sure the fittest of you all here may not be able to reach that area, Mr. Speaker. It was a very difficult trek. But we recognized at the time we did that, Mr. Speaker, it was in the dry season. And we identified an area, Mr. Speaker, where you would think that we were in the rainy season. And Wasco was there. They were asked to look at the feasibility. And I'm really hoping that government could provide the necessary resources, the necessary grants to really improve on the Salty Bus water supply, Mr. Speaker, because for an area that lies within the water belt in the forest area, it's a crime shame that we cannot get the necessary water supply as required. In fact, if we were able to tap in that new area, Mr. Speaker, I can assure you that that water may serve as far as as library because of what was indicated by the engineers and the other Wasco personnel that accompanied us. Mr. Speaker, I also want to speak because sometimes, you know, we speak to some of the things that are done when various governments are in office, but we seem to breeze over very important things that have been accomplished. The last administration spent over four point five million dollars in replacing pipes along the Grosily Highway at the same time that we had started the construction of the section in Rodney Bay there, Mr. Speaker. And that also delayed the roadworks. And I can tell you, Mr. Speaker, we spent about about two point five million dollars in purchasing pipes and about the contract to lay down these pipes was in the region of about two million dollars, Mr. Speaker. And I think that's a project that we have to look at island wide because you look, Mr. Speaker, in fact, I experienced in Shorzel where Wasco pipes continuously burst in the very same area time and time again. They dig up the road, they fix it within a week. That same area, Mr. Speaker, has compromised. I'm not sure why I cannot, you know, I'm speak to it. I don't work at Wasco. But I think Wasco needs to come out and comprehensively speak about some of these challenges that they have. I know the former minister responsible for Wasco had discussions with the government of Cuba, I think it was, where we identified a particular material that was being used to construct water pipes, the four inch pipes. And I think there was supposed to have been some arrangement to bring down some of these pipes so that it could replace our overall pipe network. And I'm hoping that the government will continue to pursue that avenue because the rusty pipes that we have supplying us with water, you know, their time has long passed. So, Mr. Speaker, there's no doubt that, you know, a project like that is welcomed by both sides, welcomed by the entire population of St. Lucia. And, you know, I want to say we need more of these type of projects. Sometimes I wonder and I speak to the mentality of both sides in terms of our supporters, whether, you know, some of these projects are not sexy enough to guarantee, you know, a seat in the next elections and whether people want a little, you know the saying, give them a fish or teach them to fish, whether that is more of a priority in terms of how voters look at things, Mr. Speaker. But, you know, things like that, Mr. Speaker, are very important, especially in the age that we live in. And I'm looking forward to more things that will strike at the very heart of our households, particularly the vulnerable. That being said, Mr. Speaker, I thank you.


Thank you all for being here today. My name is Parfe Casana. I'm a data analyst at Winston Astronomy LLP in Chicago, Illinois And for this virtual use our 2020 talk, I'll be presenting our Postgres SQL as a data science database And just to get started here real quick many people well aware of the challenges that Associated in a data science space both if it's specifically in our environment or generally across many In the industry Just by orders of sheer magnitude Big data volume variety velocity and the veracity of data is a big challenge This is challenge of data munging and wrangling many we hear a lot of anecdotes of sixty eighty percent of Analysts time is just getting data into a nice manageable format for the end-use needs Documentation is a big challenge on keeping track of code books Data dictionaries and all the auxiliary files that are associated with the data the metadata essentially I'm keeping track of that for citation and annotation needs is a big challenge And there's reproducibility being able to replicate both peers and co-workers Solutions and confirm results very critical in the scientific method and then there's hardware resources simply owing to the big data challenge as well as the data intensity of data science algorithms modeling and reporting needs and Here is where I believe a relational database can help with some of these challenges Certainly, it's not the end-all be all solution, but it can really Ameliorate some of these hurdles on relational databases has historically been used for desktop and web applications But I believe that they really do have a place Specifically and dedicated for data science and even data analytics teams Relational databases primarily objective is data persistence Storing data both from historical to real-time And it's an added infrastructure You know setup that brings durability and security and and you can rest assured of data Losses is very minimal to none So, you know has replication and backup setup with it Storage efficiency you store less on disk using the relational and entity model Rather than a lot of the redundancy as seen in CSVs Text files or even the spreadsheets and then the centralization now you foster a multiple user environment Where your team can all be set up a one centralized platform For accessible needs, you no longer have to zip or or email or point to different network file system shares cloud Access points for data or all the metadata associated everything is resized in one central location for remote and local access and the scalability you no longer are Behold or limited to your client's machine. You can now leverage a back-end server database solution And while there's many relational database Essentially out there both for commercial and open source environments I believe postgres sql is the best almost optimal That it works straight in our environment both are both open source tools Both are extendable tools postgres sql You know, it's a it's the advanced level object oriented relational database system You can you can do very sophisticated Solutions set your own types and they all work great on the seamlessly and are And you can even run our in postgres for stored functions And so there's even more adaptive ability and that could be very conducive for data scientists And so um to showcase and really show show the power of postgres as a back-end to an our environment I set up an environment database Last year really the central objective was to study the human impact of global and local Biosphere and climate and this is being physical natural environment And this is this actually database is available as a docker container I'm a docker hub and this is this link in the upper right corner Is access to that and the database is pretty comprehensive. It covers multiple domains demographics economics biology both animal and plant species list You got land arable land to the national Seeing um snow and ice data center Arctic and Antarctica sea ice data Noah's ocean data and atmospheric data from NASA really um all these Metrics and see how they all interplay together On the premise of human population and and and activity via GDP So um with that set with that in mind, you know the first You know, you know As you know in the pipeline for a database connection is essentially connected to that back end And um one great aspect of our postgres is that there's multiple ways of connecting So you're not really, you know forced into one standard or one package Um, you can connect in the traditional ODBC. It's a very popular industry on database technology other database connection Originally was developed by Microsoft But adopted across the industry and you can do that with the ODBC package that runs off of the dbi family of database solutions in our Download the postgres driver beforehand And and then there's essentially point your your server database and all your credentials for their access another um Mode is jdbc java database connector You download that in advance and use it as the middle layer between r the client and postgres the server um And this also, you know, it can be and one great thing postgres does a great job in keeping up today Well, all the versions and even the standards jdbc and ODBC standards So you can rest assured that these are very sound and well maintained solutions And then um, you can just do a direct connector without a middle layer with The our postgres sql api another dbi family of packages For database connection and you just point all your credentials and then you expose a connection for query needs So once you get um your your setup, um, um across your various um environments Is is it now interact with the data? One great um aspect of using postgres sql is that you you really can start today I'm migrating your data. Um, once you set up the schema, um for this example with the global temperature data from nasa You can then point you can then migrate your data directly from csv So what you're already using it are you can use you use those same source files Um to populate your your your tables um add here, of course to the data types So and I want to stress again the the usefulness of copy copy is a postgres sql command That's part of the sql language. Um, other database requires a separate executable utility Just to get data into that database, but you you can you know, no You don't need to jump across different softwares just to get data into your your your your Your database. So postgres does a great Has a great facility in that aspect the very bulk fast load reader documentation um postgres has a comments Feature both at the table as well as the column level and for this you can this will help in that the documentation challenge annotating the the original organization that is you know, and then their Corresponding urls where the data derived and guess what it's queryable. You can run this query Outputs all of the comments from all 21 tables pointing to their sources as well and the url So so if you need it for every day and future needs you need a one-off request You need to search for something. You no longer have to jump To those metadata files and and navigate through file systems. Everything is is sits with the data So it moves with the data. So, you no longer have to worry about All that metadata, you know challenge tracking those files. Everything is is set up in one central No, um access point And then data analysis is really the next step once you have your connection Your your your schema and populating data. We can now start using your data One great aspect of using a back in on database engine is that you have to add Access to sql sql is a special purpose declarative language. It's been adopted by the relational database industry And and postgres is Keeps true to the standard and and as you can see it's a very compact statement that doesn't lie under the seams Here we're joining three different data sources and then and then um running aggregations on them In the counterpart in r for the single aggregation solution is to is to import three different data frames And then run a chain or iterative merge and then run multiple aggregation possibly also in a chain Just to get that um year level and month analysis, but Unfortunately, you know with with sql is one compact statement to get a nice Data frame table Or you can stay at the granular level um without aggregation and here's just tracking time Date and month across those same metrics carbon ppm by month us Energy consumption and then us co2 emissions and then I leave r to do that final step of the data visualization Specifically a time series plot of those three metrics together across time from 1970s to the 2010s So if you if you saw what I just did I left on postgres to do the data the heavy lifting Of the data and then just have r do the analytical Solution and both are really working in their specializations. And so that that's one great aspect of Connecting these two tools together. This is yet another example of a separate aggregation Renewable energy production consumption. So you can change your plotting needs. Um, according to this is now going to run iterative faceted type of time series Sliced and diced by their energy type biofuels geothermal hydroelectric solar waste Wind and wood energy total biomass and total renewable energy across again production and consumption across time 1970s through the 2010s And get more sophisticated. Um, this is now a dual aggregation using a common table expression Where we're running two different aggregations and then joining them together This is us primary energy consumption by the decade and sector um, and likewise, um us co2 emissions by decade and sector and then joining them together Um, get a nice um imported data frame and then um can render the final solution Of where our Works best us primary energy consumption by sector 1940s or 2010s commercial electric power industrial residential transportation sectors and then um similarly for us us co2 emissions So as you can see, you can be very sophisticated still working, you know Intending with a back end on resource parameterized queries are very popular industry on process or sending on data from the the Client are into a back end for a more dynamic data on setup There's a prepared statement where I pass literals transportation residential commercial sector parameters To to to render a nice long data frame a more dynamic suit rather than repetitive rewriting the same as qr Um, and so with that you're able to get a nice data frame for your plotting needs Here's a again of another faceted group Bar plot by the sector us co2 emissions 1970s to 2010 but this time it plot it plots Fuel types and see how they compare over time with amongst each other then databases can make um help you broaden your lens So for a more comprehensive coverage, um, and this time we're running multi-wagging up to seven still in one compact statement Using a common table expression. This is now parent human population with many other metrics Um, um that associated in the database all joined together By year and then we're able then to render a scatter plot World population across all the other global metrics world population with um, arable land world population animal counter IUCN's red list threat level data um human population and plan counts on the botanical gardens international plan assessments list human population arctic sea ice and Antarctica sea ice from the national sea and ice data center In the newest ocean data total carbon dioxide and ph scale trying to measure the acidification of oceans and then um global temperature Um from nasa and trying to see if there's any discernible patterns both positive or negative the is associated with between the population And naturally we do see that as literature suggests Um, and then one great aspect is that you can seamlessly move solutions. You can run a shiny um, um Solution simply with your data uh, uh, uh, with your database. Um, so so you can really um set up Very sophisticated analytical solution and move seamlessly across with a backing database running the same query same same setup no longer have to drag all those um Files data files or metadata everything's intact the one and then you can even work in other environments like python um with this flash um application Really powered by the same backing database So so you can really leverage and and really expand your your whole solution if you really use post-grader as a tool in your pipeline very robust big data solution facilitates access and data management you leverage a query engine with the power of Expressiveness of sql you support reproducible process overall, right? You're keeping things intact and For replication reproducibility And then overall with post-graders and and it are you really are working in a very streamlined open source ecosystem And then even set up with a Docker Um container, so I hope I was able to demonstrate and really advocate for many um to to move into a relational database solution Thank you so much for your time


Hello, I'm Denshi. Before I start, I want to know that this video is for people who know how to homebrew their Switch and a Switch that, you know, can be homebrewed is in your possession if you want to actually do this. If you're having trouble, feel free to join Exalix's Discord server, it's very excellent for tech support, and maybe join mine if you need specific help with this specific guide, if I don't know if something seems wrong to you or something. This program named sysdvr allows you to stream video using the capture function from your Nintendo Switch to your actual Linux or Mac or Windows machine because it was written in, it was made, you know, the .NET SDK. And the first thing you're going to want to do to start streaming over USB, this tutorial is for streaming over USB with very low latency, you know, it actually looks good. You're going to want to download sysdvr client by clicking on it and sysdvr.zip. Once you've got both of those, you're going to want to essentially go over here and extract sysdvr.zip to your Nintendo Switch. So let's pretend this is my SwitchSD, I'd grab these and I'd extract them over to there. I've already done this so I can apply it all and overwrite. That doesn't really matter. So essentially your SwitchSD, just extract the sysdvr zip to there. Then extract the client zip to the, just go to my desktop, sysdvr, any folder that you want to run this program in. Whoops, I accidentally opened it by a wine, whoops. That's not supposed to happen, but just cancel that, yeah, there we go. But anyway, extract this to literally anywhere, like for example here, overwrite, and there we go. We basically just any folder you want to run the sysdvr program in. Before I start with showing you how to configure it on your Switch, I'll recommend some packages you need to install to get this to actually run. The first thing you actually want to do though is sudo them etsy pacman.conf. And you're going to want to enable the multi-lib thing. So normally there would be a little, well actually I'm going to go into insert mode so I can represent this. There would be a little number of symbols before these, just delete those to enable the multi-lib library right quick. I'd also want to note that this does not work on Manjaro. I've tried it out on Manjaro, I don't know what's wrong, but it just doesn't work. This only has worked for me. This is vanilla arch, just arch with KDE obviously. The packages you're going to want to need, so just yay, I'll have all these commands linked in the description. Minus s to install, maybe actually update your database too, because you're going to have to do that for the multi-lib. You're going to want wine, you're going to want MPV and .NET run time. So install all of those. So I've already installed them so it's just upgrading them for me. So yeah, you're going to want to do that. Now moving over to the Nintendo switch. It's finally time to actually enable SysDVR with USB streaming. So press A just to open any kind of game. Hold down the R trigger and you'll open up into the homebrew menu as you can see. And go over to SysDVR settings, so press A to open that up. And you know, here we are with the SysDVR settings. Select the option stream over USB and press A. And then as you can see here, it will say SysDVR streaming over USB. If you wanted to stream over USB on boot, so every time you boot into a custom firmware it will just automatically start doing it. It might take a couple of minutes to start, but you can click on set current mode as default on boot. And yeah, quit real quick. Close this program and then we can open any game, let's say Sonic Mania, without holding down the R trigger. So it just opens up regularly. And as you can see, there's Sonic Mania loading up on the screen and now I'll show you how to stream. Okay, so now that you're done with that, we can finally actually stream. So you've plugged in your switch via USB and you click that option for streaming via USB. Time to CD into, well I have my specific SysDVR folder on my desktop. And then you're going to run the command dot net, well actually sudo, you have to run this with sudo. sudo dot net sysdvrclient.dll and then the USB option to stream via USB. Press enter and as you can see it started the stream. To actually open the stream, you can click on this link and it will open it via MPV automatically. So there we go, we're streaming Sonic Mania. Or if you really wish to play this in a different movie player, it should just default to the one you have installed. I just have MPV. If you want to open it manually, you can do MPV, then copy paste this specific link and then it will just open it up in MPV. But I normally just click on this open link and then it just opens it in MPV for me, so that's really good. So here we are streaming Sonic Mania from my switch, I'm looking at it right now, I'm looking at it. There's a little bit of delay obviously because USB isn't perfect and the streaming function on the switch isn't perfect either. But the picture quality is good, this is how I do my Pokemon stream just in case people ask me. I don't have any form of capture card, I just do them by this. There are a couple of limitations, like for example, it's limited to 720p 30 FPS and you can look at more specific details on the actual GitHub page. There's a couple of stutters there, obviously if your USB cable is imperfect or if you have like a bandwidth limitation or more specifically, I don't know, there's just a lot of things on stream, it's not perfect. But it works really really well and it's much more affordable because it's completely free than buying a capture card. Thanks for watching my video, this was how to stream from your Nintendo Switch to an Arch Linux machine using SysDVR. Feel free to check that GitHub page, feel free to check the Discord server of the developer, it's a great Discord server I think. It's very very nice for people who like Nintendo Switch Homebrew. And yeah, thanks for watching my video, I was Denshi, goodbye.


Hey everyone, Shaber1000 here today. We got a brake light out, so I'm going to show you how to change its real simple four screws. Let's go ahead and change this so she don't get a ticket. Okay guys, so a little bit of cracking there. I've never since we've been together, four and a half years, I've never had to put any tail light bulbs or anything in this truck. So you know, I've had to do headlights and front turn signal bulbs, but I haven't had to change any in the back, so they last a long time. They're not LEDs, they're just a double filament bulb. Just one of these. That's all it is, so this is the turn signal, this is your reverse lights, this is your brake and tail lights. And I know a lot of you guys already know that, but some of you might not, but I noticed brake light when I backed the boat off the trailer and she was taking the truck up to park it, truck and trailer up to park it. I noticed this brake light was out, so I figured for a $3 bulb, it's no sense in getting a $150 ticket. So it's going to be this bottom one, which is right down here, I don't know what you can see and what you can't. Right here, right there. So should just be able to, that's been in here, the problem might be original, I don't know. Let's see, does this come out? Yeah, sometimes these will come out, there we go. Alright, so it's the bottom bulb here. You push in and turn a quarter turn counterclockwise. See how dark it is? So, now you're, there's a certain way these go, like that, these little ears, just look in there, one will be down farther than the other one, so it'll go like this. And you push it in and turn it a quarter turn, same way if you take it back out. Okay, so when you take it out, you push it in and turn it counterclockwise, well probably not even a quarter turn, but, and when you put it back in, in, just till it clicks, there we go. Now, let's check and make sure our lights are working. Okay, so our tail light is working, now I got to somehow put my foot on the brake and look at the camera. Okay, cool, so good to go. So, these 2003s, some of them you got to take the socket out, but this one apparently you don't have to, so just make sure you get your seal in there, put that on there, and put your screws back in. Really simple work. So I just thought I'd share that with you guys, for those of you that don't know how to do it. Sometimes, these are kind of, there you go, you got to look in here, there's a little plastic square piece, make sure your screw goes in the center of that, and what I do is I start them that way, if I got to pull it back a little bit and see, I can. Usually once you get two in, you're good to go, oops, guess what, this one takes a long one, I forgot to mention that, see, two long ones and two short ones. The long ones will go on this side, and the short ones will go on that side. And get it started, make sure it's getting into the plastic part, and there you go. Really simple. Now, I've always got a couple little tools, I got a straight screwdriver, and a Phillips screwdriver, a couple hand wrenches, some pliers, I got like a little tool kit, a couple sockets, and a couple ratchets, so I got this extra one, I'm going to keep in the tool box of the truck, that way if we're out somewhere, I don't have to go stop and buy one, I can just pull into a gas station or whatever, and change it, finish tightening these up, all done. Hi guys, so there's how you change a tail light bulb in a 2003 Toyota Tacoma. It's like, from the late 90s into 2004, 2005, they're all pretty much the same like this, a lot of different vehicles, Ford Chevy's, some of them you'll have, I'll show you here real quick. Now some tail lights, to take the lens out, you'll have a couple screws over here, and then you'll go inside here, somewhere, you know, just look at it and you'll see, just take them screws out, and you should be good to go. So there you guys go, appreciate you watching, and I'm going to say, shea bearer the myth man legend gone for now, hot one, there that's good enough, we'll see you soon, bye bye and take care.