a little bit about some of the work I did in Facebook. This is about a couple of years back, so specifically around the architecture of the Facebook messaging system. So just to put some context on the talk, what you experience used as the Facebook messaging system actually used to be the chat system, and then there was a separate messaging system. And at some point, they were unified. So what I'm going to be covering this talk is not the chat system, but specifically focus on the re-architecture of the messaging backend, which used to be based on MySQL and was written very differently to a more sort of modern implementation. So one thing I want to make clear, I'm not a Facebook employee anymore. I left Facebook last year, so I'm just going to be talking about a brief period of six months where I was one of the key participants in the design team that came up with the design. There is a lot of material on the net that you would find. And I've tried to not repeat that stuff. Like there's a publication. I think in June in ICD, there's a publication by the HBS team in Facebook. There have been Facebook engineering blog posts and stuff, so there's a lot of stuff, background material that you can find on the web for free. I'll try to lend a little bit of insight into how we came to our decision point, rather than try to focus a lot on the implementation, because I was actually not part of the implementation. I moved on to other projects. So having said that, this is a pretty hard problem. We've been hearing about a lot of hard problems, and this is definitely up there amongst the hardest ones. The stats are very simple. Facebook obviously wants to be a serve everybody on the earth. So we were at least projecting, hey, we'll have billion users in short order. And that is obviously almost true, I guess. And even if you take very moderate usages of any emailing kind of system, so I put some example numbers, 25 messages per day for every user, assume that every message is four kilobytes, and even just forget about the attachments, the photos, and Excel, Word, whatever people are sending around. So just to the math, hey, it's about 10 terabytes a day, just the message bodies. So now, but that's not it. I mean, if all your messages were just stored in one gigantic file, you still wouldn't be able to access them and actually give you the email interface that you're all familiar with. So we have to add some indexes. You have to maintain threads like Gmail. In Facebook, what I think they do today is sort of, there's an implicit thread for every friend. So you see all messages from the friend sort of in a chain, and there would be threads for groups of people communicating. So there is this notion of threads, and you have to maintain indexes. What are the messages in that thread? You have to provide search, so that means you have to maintain a keyword index. You have to maintain all these counts. So how many we log in, and we expect to see an unread message count overall. We want to see, on every thread, how many messages are there, how many are unread, and so on and so forth. So there's a variety of sort of indexes and summaries that you have to compute on the base data. And then once you start adding in those, you realize that the actual amount of data that you'd have to store is even much more than just enter by today. So beyond the scale, the amount of data, the volume of the data, these are standard aspects for any company is that we want the data to be replicated to some remote data center, so that it's available if the primary one goes down. So ideally, we want to be able to concurrently update data from different data centers, so that no matter whether you're logging in from India or you're logging in from Europe or America, you might be hitting the data center that's closest to you, and actually we can sort of make updates there. And so I think in the summary, we probably mentioned that this talk might cover consistent, the cap stuff. And this is where it starts getting really hairy. So this is a hard problem. Doing concurrent updates is very hard. But anyway, the minimum we want is that there should be a copy of data somewhere else. In the case of disaster, we should be able to recover. The other thing I wanted to mention was we talk about availability all the time, failures, 5-9s, 4-9s. One of the things that people really liked internally at Facebook was that it's not that things didn't go down. So I think we had at the time of my leaving maybe like 5,000 databases or so. And what I used to hear was that every day, we would lose three or four of those databases. They would just burn and crash. And then somebody would actually bring up a replica manually. It was kind of not automated. But none of you guys know about it, and none of you guys care about it. From your point of view, service seems to be mostly up. So the reality of the market is that if you bring down 1% of your service, nobody really cares.  Facebook goes down, Amazon goes down, and so on and so forth. So we wanted to design the system that this has to be a design requirement is that the system should never crash in its entirety. It should always have independent points of failure. And it's OK if some things are offline. That's kind of like OK. But losing all of it is not OK. And the overriding concern was can't lose data.  So that was sort of priority number one. Things that we did not have to solve at the point about photos that I just took that out of the equation. And the reason was that there's a system in Facebook already that's called Haystack. Again, you can find a lot of documentation online. It's globally distributed, distributed in a data center, just scales out linearly. And it's something that they've perfected. It just works at this point, because Facebook is by far the largest photo store in the world. So the idea was that, hey, any attachment data, we'll just throw it there, and let's not worry about that problem. The other key thing I want to highlight here, and I cannot stress this hard enough, is that please take anything I say here with a grain of salt. Because not all companies have the choice of talent and engineering skills and bandwidth that Facebook had. So our charter was that, hey, find the best implement design. Something that is rock solid that's going to scale to billion to billion users. Don't worry about whether it works today. I mean, you would worry to a certain extent, but you can't start off with something that's completely broken in concept, but if it's not perfect, if it needs features, then hey, we can hire anybody we want, and we can get that built out. And that's very different from an average startup who's got to choose something that just works. And getting things done fast. So this is another sort of always a tension between, hey, getting things perfect versus moving fast is like the religion at Facebook. You've got to do things fast, including the fact that the product changes. So Zuck would just sort of dream up something, and tomorrow he'll say, OK, guys, I want the ship like in two weeks or something, and you've got to do it at that point. So that's sort of the context here. So let me move on to some of the more detailed sort of point by point. So if you go and look up online documentation, you will find that we ultimately settled on edge base, and I'll describe some of the other components. But how did we get to that point? So the first point that I think everybody in the, so there was a small team of six people. There was a lot of conflict. Everybody had different ideas, stuff. But I think everybody agreed that whatever we chose had to have stupendous right throughput, because we're going to have email system messages pouring in like crazy. We did not even cover spam. So for example, if you go to Yahoo Mail, you would actually see a ton of spam that they don't throw away. They'll hold it on for a month or so. So maybe that 25 messages per day was actually something realistic for genuine messages. But actually, if you include scam, you will be moved. So tremendous right throughput. There were only two choices. If you were going to go with a disk-based solution, you had to have a log-structured data structure to store data fast. So there have been talks here that have talked about this that you can do buffering and async and all the stuff. But at the end of the day, if you want to store data really fast on disk, you need a log-structured container. Again, tons of papers around. I'll talk a little bit about what we chose. One of the things, and so there's another way, which is like, OK, let's just put everything in flash. And one of the designs that emerged internally at Facebook was this very clever design where it was argued that, hey, if you look at your mailbox, let's say it has 10,000 messages or even 20,000 messages. If you just store the metadata, what time it came, who was it from, just a basic metadata. It's not that much data. And so maybe, hey, we could just store every mailbox, just a metadata of every mailbox in flash and potentially combine with memory, and it will be done. And that might actually be an interesting solution. And I think it will get more and more tractable as flash prices go down. But the trouble is that it puts you in a bind. You can only store so much. And I'll take a very simple example. Let's say when you open your mailbox or any thread, you'll see a bunch of messages just pre-opened for you. It makes no sense to force you to click on every one of those. If you were implementing the back end of such a system, you would think that, hey, I showed in one IO or in one request, just pull down a bunch of stuff that the user wants to see in a single page view. So with storing things on disk, you get this flexibility. You can start storing more stuff inline, small messages instead of just the metadata. You can pull it all out and show it to the user really fast. And in some ways, in the flash solution, you were sort of punting the problem of, hey, OK. So you've got all these 4k really, really small messages and who the hell is going to store that? So the indexing is the other part.  So again, you can do bitmap indexes very efficiently, but it's very tricky to get it right with a general purpose keyword index. So moving on, more sort of deep down stuff. So I wanted to show you this diagram. There's this thing called LSM trees. I'm sure many people in the audience are sort of aware who have looked at these systems. But if you're not, the way it differs from a traditional database like rdbms is that instead of storing one tree of all the data and having pointers from the root down to the leaf nodes, it stores things as a set of trees. And the advantage is that as data comes in, you don't have to update older data. You just sort of like, data just comes in and sits here. And then asynchronously in the background, you go and merge it. And all the hard stuff you sort of punt. I'll do it later on. There's a lot of follow on work from this paper, but this is pretty much the standard design pattern for log structure databases. And very high write throughput, because you're never accessing disk randomly.  One of the coolest things about this is that it matches the messaging system requirements very well. The reason is that if you think about it, when you open your mailbox, you tend to access messages that you got recently or even old threads, threads that got a new message. Those are the ones that you would sort of click. And guess where those things live? Everything that got updated recently or that was just created recently lives in the head of this pile, in a small separate data structure. That means in theory, if you were to get this organization right, then you could cluster everything that you need in a small part of your disk system and read that off very efficiently. So the interesting part was that I think when we looked at the mailbox problem and we thought what the ideal disk organization should be, it was almost exactly LSM trees. And so we said, okay, let's find a system that is built around this kind of concept and go with that. So and as again, many of you know, Bigtable was the one, the most famous system that sort of used LSM trees and then Edgebase and Cassandra, both sort of were built around as LSM trees.  This data structure is inherently snapshotted. So the last tree that lives on, I guess, your rightmost side is holds the entire data and every day you can just take the oldest tree and shovel it somewhere, it's like your NetApp snapshot or something like that. So it's very easy to back up. So moving on quickly, I don't have much time. The problem with the system is that reads are cheap, reads are expensive because the reason is that every time you come in and ask a question, hey, do you have this key? Now instead of a traditional database that just sort of goes down from the root of the tree and goes down to the leaf and finds it or misses it, now you gotta answer this question for every tree that you have in your system. And people have invented clever ways like bloom filters and stuff, but no matter what you do, you cannot get away from the fact that this is not a read-optimized system. So the solution then was that, okay, so this is great for high-ride throughput, for read throughput, let's build an application server on top of this data store and let's keep a very well-tuned cache of everything that's required to serve user page views and mailbox views in that app server. Now, once you start doing caching, you have cache coherency issues. So again, just to keep things simple, like just come with a very simple policy, let's just have users bind to app servers, right? So all updates to the mailbox, all reads to the mailbox are going through this one app server that also holds a very nice and memory cache and no cache coherency issues. The only problem we have to solve is, given a user, we have to find out which app server the user is hosted on, right? So the solution to this, I mean, we knew how to solve this, but the actual solution was done after I left. So I think right now, if you read the papers, you will see that there is a user discovery service that's written in ZooKeeper. So that is responsible for maintaining this mapping. The other, again, going back to the lesson tree, one of my favorite topics is that, like, think about user logging back in after a long time and you don't have the cache is completely cold, right? So how do you construct this cache? How do you populate this cache? And if you were dumb about it, you could, like, that could take a lot of it, right? But again, you know, if you think about it from that, the tree organization I showed you, you would realize that most of the data you need is actually going to be living in one of those trees and so you could, if you were smart, sort of like just scan the first tree or the first few trees and pretty much load everything that you would need to render a user's mailbox. No single point of failure, you know, don't want to take everything down, but HDFS, as I don't know if people have mentioned, it's a single name node, it doesn't have high availability. So, okay, that's not too hard, right?  So I think the current deployment has something like 100 node HBase sort of clusters. And I believe, you know, over time we've also added name node HA, but given the inherently sort of, like, the federated and the partitioned nature of the deployment, they actually don't care about it that much. I mean, this is kind of almost ironical. So, you know, okay, we decided the data structure, we know that it has to be distributed, so we have a choice, right? So we can do Cassandra or HBase, and there was also at the time, and even now I think there's HyperTable, there's another sort of good choice from a log structure, it's sort of a distributed store point of view. I didn't write it up because HyperTable's ecosystem is not that popular or whatever, so these are the two sort of main systems. So in a nutshell, I don't know if I'll be able to go in and sort of go deep into this. We tested things out, you know, that was the only, we were in a mad scramble, you know, trying to sort of get things out, we had to get this design right, but we actually had to ship things as well. So tried out HBase, tried out Cassandra, big caveat, we tried out, this is 2010, everything has changed since then. We tried out Facebook's internal version of Cassandra, not the open source tree, and you know, maybe that was a mistake. But in our testing, we had a really poor time with Cassandra, and this is just within a data center, it's not sort of across data centers and stuff. Then what happened was that while we were doing all this, we said, okay, we really want to understand what's going on inside Cassandra. We started looking, reading the papers, we started diving deep down in the code, and I'll try and go a little bit more deep into this, but for our use case, again, you know, big if, like I mean, if, but like for us, HBase was the better choice, Cassandra wasn't.  So one of the big worries we had was, man, like I mean, if like we screwed up, like if we chose a system that like ended up losing data, like basically we'll be all fired, right, it's pretty simple. So, but we really trusted HDFS. So, you know, it has its issues, it doesn't have HA, it's kind of slow, blah, blah, blah, but you know, we had stored like Peterbytes and Peterbytes on it for like almost a couple of years when we were doing this evaluation, and we had never suffered any loss. And so in the spectrum of things we could think of, and I think Pramod was just talking that, you know, of all the things that they looked at, MySQL was the most rock solid thing, and we had that kind of same philosophy that, okay, we really, really trust MySQL, but if you're gonna trust anything after MySQL, probably it was HDFS. And that definitely tilted the playing field quite a bit. And, you know, again, sort of to reiterate, like we could hire engineers, you know, we could hire committers, whatever, right? So the main thing was we could build out all the missing features of which there were a lot. To talk about disaster recovery, one of the things that I really love about Edgebase, and this is common to like many database technologies, is that it's really easy to do backups, right? So you got an inherently snapshot system.  And you're also shipping your right ahead logs, the transaction log that's storing the changes in the database. You're also shipping that to a remote data center. And every night, once you've shipped your, sort of, you know, the full compacted snapshot, you just truncate your log because you don't need the stuff that is already reflected in your snapshot. And this is so easy to do, you know, you can just probably write a Python script around it. You don't even have to get it right. Now, you don't have to synchronize number two and number three. As long as number three is before number two, I mean, you don't drop off things that have not been reflected in the snapshot. You're okay because the replays are very important. So that's actually how things are working. And again, if you look up the papers, they know this is exactly how they're doing the backups today to a remote data center. Right, so we tested, right? So I thought it would be good to like, so this is actually not part of this presentation. I fortunately still had like my report from back those days sitting in a PowerPoint somewhere on my laptop and I just like did a screenshot of that. So like no point going into it, but what you can see here is that, we were actually running a real edge-based cluster. We had an application server. We simulated like 12 million users with some standard workload. And we wanted to test this whole concept out. Hey, is the app server gonna hold up given a reasonable access pattern? Is it gonna be actually able to protect edge-based from most of the reads? And so on and so forth. And this in itself was a very interesting experience for me. I realized that writing a cache in Java is probably not the best idea. So I'll have to stop very quickly. Flash, I wanted to not, let's talk a little bit about this. So Flash is big in Facebook. Every time we design storage systems, the question comes up, how are you gonna use Flash? Because a few years from now, that's gonna be the thing that's gonna take over everything. So a few different ideas. We quickly understand that, okay, again, LSN3 is like recent data is clustered. Well, we can put that on Flash. There's a cache, we can put that on Flash. When the app server throws out stuff from its cache, you can put that on Flash. So a lot of simple ways of exploring Flash. Finishing it off of things that I sort of, even after leaving the project, I kept feeling that, hey, we didn't solve everything. So if the HDFS is too big, when you build systems in-house, we are better off building things out of small components. But here we have this pretty big code base that does something. And every time there's a bug or there's some performance or whatever, you have to touch large parts of the code. So we have learned to be nice if we actually had small pieces. So like data node, block manager, name node. And one of the things that, if you looked at this architecture closely, you'll realize that HBase actually doesn't need a name system. It has its own name space. All it needs is a block manager. So yeah, things are not perfect. I wish these systems were built as smaller components and we could choose best of breed, but we had to live with what we had. And if you've seen the talk so far, we couldn't quite cover the cross data center concurrency case. So we gave up on the concurrent updates and we kind of punted on it. We said, okay, well in future, if we really want to set up a data center in Europe or somewhere where we want the European users to have the data in Europe, what we're gonna do is we're gonna just federate users. So these guys go to US, these guys go to Europe, and we're gonna maintain a global registry that maps users to continents or data centers. So then we kind of punted the problem. So who maintains this global registry? How do you deal with concurrent updates to that and partitions on that? But I think the gut feel we had was that, hey, that's a much simpler problem. Talking about like much, much smaller amount of data. The updates are very infrequent. The updates we are an optimization. So if a European users keep setting up American data center, it's not a big deal. So we could do globally consistent rights at leisure and nobody would care. So I think I'm out of time. Let me stop here because I don't think I can cover the controversial part. Does anybody know, should I stop? Or... All right, okay, I'll show that. All right, this is a really controversial part, right? So Cassandra versus HBS. I mean, I'll try to be really quick about it. So these systems evolved from different philosophies. Cassandra believes in a flat earth. If you look at Cassandra, it's a symmetric system. All nodes are in a ring. There's no notion of, hey, this is the stuff that lives in data center A, data center B, or rack A, or rack B, right? World is not flat, world is hierarchical.  You've got different partition properties in each one of these boundaries. You have different latencies. So one of the best things that you could, papers you can find on the web, Jeff Dean has this sort of things that every engineer should know, right? Latencies, and you've got memory latency, L1 cache latency, L2 cache latency, same data center, cross-continent, so on and so forth. So I just wanted to put, this is not a criticism by itself. This is just a starting philosophy. Another starting philosophy, Cassandra, does not believe in centralization. Everything is independent. There are no special rules for any node. There is no central anything. There is no central commit log in the system. You want to say, okay, what are the transactions that have flowed through my system in the last 24 hours? You'd have to actually read like two or three copies of commit logs and try and reconstruct that. So I was trying to find, I've been working a lot in Ruby and said, okay, this is like the do repeat your reads paradigm instead of the DRY paradigm.  So that's the unfortunate part. I mean, the only reasonable configuration in Cassandra that you can come up with is this, the Quorum configuration would be, okay, let's maintain three copies. Let's do two reads and two writes to sort of have a successful read or a successful write so that, you know, every read and write can have sort of at least one sort of replica in common. So you could sort of get the most recent write. Now, the thing you would immediately notice is that I've been talking a lot about, hey, you know, we chose the write optimized system and it was not read optimized and we had to actually work on a cache to envelope it so to give it good read properties. But here we have a backend system, if we chose it, where we would have to read twice to get a consistent read, right? And it was just like, you know, we couldn't do it, right? I mean, we were always bound by a spindle of per second. We were not using flash when we started out. So we had to choose a system that was read, like had good read properties in addition to being a write throughput. Again, you know, if you go out there and read on the internet, you'd find people still debating whether strong consistency is actually possible in Cassandra or not. And there's no point in me trying to go into this. Like my general point is if five scientists twittering, you know, throughout the day, I cannot agree that it has strong consistency. Imagine if the science has bugs, what kind of code would you write out of it, right? I mean, we can barely get the code right and we understand the science completely. In this case, like it's almost unimaginable. The other key thing you might want to take away from here is that distributed storage is not distributed database. So, simple example. So, you've got a bad disk or a bad block, right? You go to a system like HDFS and say, okay, Mr. HDFS, I lost a block. Can you please recover this block? And Mr. HDFS will say, yeah, you know, I know like these three, four other replicas floating around. I'll just like give you back one of the replicas. And by the way, I'll also redeplicate that replica onto the failed drive or machine or whatever, right?  I really don't know, you know, who this block affected, right? So, you got this missing block and like there's lots of people who got affected. So, yeah, you can recover. And how do you recover? You have to now merge these databases together. And there are very, very fancy ways of doing it. It can be done. Those are nice academic papers. But the thing is that you just took a problem that was very, very simple and converted into a problem that was very, very hard, right?  And how many bugs would I get, you know, if I didn't? So, basic stuff, gotta get that right. Another thing that, you know, vast majority of programmers here in this room as well. The way we write programs, you say, okay, read a value from a database. Do some transformation, write it back, right? So, if you were going to do an increment operation, you would say, okay, read a counter, plus, plus, write it back, right? And now, you know, if I were to come and tell you, you can't do that anymore, right? Because if you are working with the eventually consistent system, sorry, I'm in my hurry, I've switched back from like consistency, individual consistency. The reason I switched to this was that you could sort of get high rate performance if you were in a eventually consistent mode, right? But what I'm trying to point out here is that standard cookie cutter programming becomes very difficult in an eventually consistent database storage system. Now, some things are possible, right? So, you could take that increment operation and push it down into the database and tell it, yeah, can you please perform an increment correctly, right? And yes, that can be done. But you cannot take your arbitrary business logic in slip number two and push it down into the database. So, one of the things I took away from this sort of like analysis and exercise was that it's very, you should not try and do conflict resolution at the row level in the guts of the database. I mean, that is just the absolute wrong approach. The reason is very simple.  It's just a messaging store, right? Now, imagine that you were doing something more complicated where you said, okay, I actually need to make updates to multiple rows as part of something that happened in my system. And this thing that happened in my system has to be atomic. For example, if I got a document in my collection, I got to put the document in some database and I got to update three, four different indexes and maybe those are all sharded differently and are in different data stores, right? So, I got this concept of a global transaction that I have to perform atomically. Now, if every one of those transactions is eventually consistent, right? It doesn't help you, right? Because what you want is you want those five actions to all turn out the same way. Either all of them turn out positive or all of them turn out negative. It doesn't make any sense that some of them turn out positive and some of them turn out negative, right? So, my general feel, you know, as sort of a more of an architect hat is that you should try and solve conflicts at the very highest possible layer in your system where you can describe your outcomes. And I think this is almost like a transaction monitor. I mean, this is the kind of the layer where you would also like do transactions. And this is actually how Facebook is sort of like has gravitated in this direction where we have a team that now works on sort of just transporting global events across like pipes and that would become the layer at which you would say, okay, we had two conflicting events because they wanted to modify the same key, right? And we optimistically allowed them to go ahead in their data centers. But now when we detect a conflict, we got to like do some conflict resolution. And this has to be at the layer of this transaction that spanning multiple rows, multiple databases, God knows what, right? It cannot be at the level of the database. All right, done? Time. Okay, yeah, I should stop. So there's a lot of stuff on the web. You can find all this. Unfortunately, there's no question for time, but no time for questions. All right. But thank you for that second wonderful talk in today's topic. Thank you so much.


Welcome to the 21st Economy, Energy and Fair Work Committee meeting of 2019. Can I remind everyone to turn electrical devices to silent or off so as not to interfere with the work of the committee? We have apologies today from the convener, Gordon Lindhurst, so I am in the chair instead. The first item is decision on taking business in private. Item 1 is decision by the committee to take item 4 in private. Are we agreed to do that? Yes. Thank you. Item 2 is the electricity brackets applications for consent and variation of consent. Close brackets, fees, Scotland regulations 2019 to 19176. The regulations replace the previous fee structures and provide for increased fee levels for applications. Does any member have any issues that they wish to raise at this point? There appear to be no comments. Is the committee agreed that it does not wish to make any recommendations in relation to the instrument? Agreed. We move to item 3. The committee has been asked to consider a notification from the Scottish Government relating to the insolvency amendment EU exit number 2 regulations 2019. The committee agreed a proposal for consent for the UK Parliament to legislate in relation to the insolvency amendment EU exit regulations 2019. Is the committee content for the issues to be dealt with by statutory instrument laid at Westminster? As the committee is content, the convener will write to the cabinet secretary to note him of the committee's decision. That ends the issues that we are doing in public, so we will now suspend the meeting and move to a private session.


This 2018 Chevy Silverado 2500 Crew Cab Short Box is stock number 12147Z. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used heavy-duty truck headquarters. This 2018 Chevy Silverado 2500 has the 6.6-liter Duramax diesel engine with the L5P motor, which pumps out 445 horsepower. This truck has been fully-safetyed and inspected by our service shop, has a fresh oil and filter change. All the fluids have been checked and topped off. And this truck is 100% ready to go. Summit White is the color we shoot all of our videos in 1080p, 60 frames per second. So if you have HD capabilities on your computer, tablet, smartphone, or television, turn them on right now because it is definitely your best way to check out the quality, condition, and options of the truck before seeing it in person. And if you like the videos and want to subscribe to our YouTube channel in the upper, I'm sorry, the lower right-hand part of the screen, is a subscribe button, click that, and then click the bell notifications and you'll get updates on the videos we do each and every day of our new and used inventory here at Summit Auto. This one comes with the painted and polished aluminum alloy rims. No skelfster scrapes on there, it does have Bridgestone Duler AT tires, these are LT24575R17s, about half the tread left in the front, maybe just a hair under, frame and underbody looks pretty good. Front fender, I didn't see any dents or dings on there, looks really good. This truck comes with the projector lamp headlamps, and the front bumper is in excellent shape as well. No dents or dings on there, you get the chrome trimmed grille. And that is a fully functional hood scoop on this truck. The hood looks really good. Passenger side front fender looks really good as well. And the passenger side rim, no scuffs or scrapes on there. As you go down this side of the truck, take note of how clean the body is, how reflective and mirror-like that paint is. We take these HD videos, so if you are far away, or even if you're close by and just cannot make the trip down, but you're still interested in purchasing the vehicle, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So when you do get here, there's no surprises, and you can make a smart and informed buying decision. From wherever you may be watching this video, pretty clean down that side. This back rim is in excellent shape as well. And the back tires have, I'd say, probably 60% of the tread left on them. Frame and underbody looks pretty good. And it's like that all the way underneath has all the remaining factory exhaust, so it hasn't been altered in any way. Lower rockers all look really good. And it comes with a nice set of chrome step bars. Passenger side box is in nice shape, coming around, excuse me, coming around to the back. Rear bumper is in nice shape as well, no major dents or dings. Does have the rear bumper parking, rear bumper steps. Rear parking sensors. Full towing package, which includes a receiver hitch, four pin, and seven pin wiring. And the tailgate is in excellent shape as well. Does have a locking tailgate and a backup camera. Comes with a trail FX soft tonneau cover, I didn't see any rips or tails on there. This one does come with a superglide fifth wheel in it. So that is included. Not sure if you can see the information on there or not, but looks like a very nice adjustable fifth wheel. And it does come with seven pin wiring back here as well. Spray and bed liner. And the bed's in really nice shape too.  No major dents or dings on the box. And for full disclosure, this back rim is in great condition as well. Down the rest of the side of the truck, doors are absolutely perfect. And it does come with power mirrors and driver's side blind spot mirror.  There are no rips, there are no tears on the seats. This is the factory all leather floor mats and auto headlamps. Turned out four wheel drive. Power windows, power locks, and power mirrors. We'll hop inside and take a look at the miles and the radio. You can see that this one has 40,678 miles. You do get the digital speedometer, compass display. Instrument cluster is very nice and clean. Comes with the leather wrapped steering wheel. No major wear or scuffs on that. You do get Bluetooth and information center controls on the right and cruise controls on the left. This one has the six speed Allison heavy duty transmission with the optional tap shift. And this one comes with the Chevy MyLink radio, AM, FM, and Sirius XM radio capabilities on here. This is also where your backup camera shows up. And this one does have projection manager where you can project your cell phone to the screen via Android Auto or Apple CarPlay. Pretty cool system. So if you have a navigation system on your cell phone projected to the screen, use it through the screen and it's like you have nav right on the screen. This one does have a CD player. Climate controls are down here. It does have heated outside mirrors. Stability control, that's for your cargo lamps. And that's for your exhaust brake. It does have an aftermarket electric brake controller that does work. And down here you do get a 12 volt power point. Carpeting and passenger side format are in excellent shape. No rips or tears on there. This truck smells very clean inside. I don't think it's ever been smoked in. And the headliner is in great condition as well. Up here you get map lights, on-star and SOS capabilities in the rear-view mirror. We will take a quick look at the back seats and then we'll start it up and check out under the hood.  No major rips or tears back here. It does have the latch child safety system for any child car seats you may have. Fixed glass rear window. These seats do fold up for extra storage. And the carpeting and floor mats back here are in excellent shape as well. Side curtain airbags on this truck. Bottoms of the doors look pretty good. We'll start it up and take a look under the hood. Starts right up. No check engine lights or anything like that. I would personally like to thank you for checking out the video today and hopefully from this HD video you have been able to tell the quality and condition of this truck inside and out. Under the hood we have the 6.6 liter Duramax diesel engine, L5P engine, 445 horsepower. Engine based very clean runs very smooth. Once again this truck has been fully safe and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and topped off. And this truck is 100% ready to go. There is your true color hood. Air goes in there. Into the air filter right there. And this truck is 100% ready to go. And to see more pictures of this truck or one of rather 450 new and used cars, trucks, SUVs, minivans, Wranglers, half tons, three quarter tons, one tons, you name it. We got it. Go to that website right there, www.SummitAuto.com, full pictures and descriptions of every single vehicle from two locations all at SummitAuto.com and if you'd like to check out more HD videos you can go to www.youtube.com slash www.SummitAuto.com. Remember to like, subscribe and share on this video and all the videos that you see there. In fact, in a second we'll see a link to subscribe to our YouTube channel in the upper left to link to more Chevy 2500 truck videos like this one in the upper right, a link to this vehicle on our website in the lower left and a link to one of our latest YouTube videos in the lower right. Click those, check us out and we're super excited to help you with this Ultra Clean 2018 Chevy Silverado 2500 Crew Cab Short Box 1LT in Summit White. Thanks again for checking out the video. Remember to like, subscribe and share and click those bell notifications. Thanks again.


Plus, if you guys sit down, welcome back to my channel. So I wanted to tell you guys first and foremost that I am going to be making videos every Sunday and Tuesday. So if you haven't already done so, subscribe so you can know exactly when I come out with a new video. But I have this question asked by one of you all. It's from Michelle Marmalejo. It says, can I become a PA with a BS in communication sciences and disorders? So I'm gonna be answering that question for you right now. On my channel. Thank you guys so much for joining me today. So I have this question asked to me. It was asked by Michelle Marmalejo. I believe that's how you pronounce it. It says, can I become a PA with a BS in communication science and disorders? Now, I mean, that is a question that I'm not specifically like, can I become a PA with a Bachelor's of Science in communication and like science disorders? That question is not specifically what is asked to me all the time, but the, can I become a PA with this major or that major or that major or that major? That question is asked to me all the time. And so far, the answer has not changed, you guys. The answer is yes, okay? So the PA profession, the PA school, PA programs are very unique in that you do not have to be on a specific major or a specific track to get into PA school. You can be a fine arts major. You can be a communications major. You can be a music major. You can be maybe like a chef, I don't know. Whatever it is that you have decided that you are passionate about, that you really are interested in learning about, if you really like the mind and you wanna be a psychology major because you think that you wanna be a psych PA, do that. Whatever it is, you can do it because ultimately, what it is going to do is just make you a more well-rounded PA and you're gonna be able to meld those two passions together. Here's where it becomes a little bit tricky, okay? So yes, you can get into PA school with whatever major your heart chooses to major in. However, you have to meet the prerequisite requirements. So the reason why the majority of people choose your natural sciences, like your chemistries and your biologies or your biochemistry or your bio statistics and choose those majors in their undergraduate degree to get into PA school is because it already integrates the majority of the prerequisite coursework that is required for PA school. So for instance, myself, I was initially a biology major and then I switched to biological sciences or no, I switched to biomedical sciences because I just thought it sounded fancier than biology and I only had to take one extra course. But with that being said, all of my classes were sciences. I had biology, general biology, general chemistry in my first year of undergrad. I had organic chemistry in my second year and so a lot of the prerequisites that PA schools require were already built in to my degree. So for PA school, you're typically gonna need your general biology one and two, your general chemistry one and two. You're gonna need organic chemistry. Some schools require biochemistry. They will require an upper level science. So like a biology, an upper level biology like your cellular and molecular biology or microbiology. Typically they require anatomy and physiology. Some schools will require medical terminology. You will have to do a statistics, a psychology. Some will also require an upper level psychology like an abnormal or developmental psychology and then there are some other little nuances in there. And the tricky part about that is that not every PA program is created equally, right? So across the board, it's not standardized. Not every school that you look at is gonna require the same prerequisite requirements and that's why you have to do your research. You guys hear me saying that a lot. That is my favorite phase, do your research. But you're going to have to do that because ultimately you're going to have to look at these programs and see, all right, if I meet the requirements for three out of the five schools that I'm interested in and I'm going to have to take like an extra semester to meet the requirements of the other two or an extra year to meet the requirements of the other two, is that worth it?  Is that worth it to you? If it is, then do it because you're making yourself a broader candidate. If it's not, then don't do it and look for other schools that you already meet the requirements for. But when it comes to the best major for PA school or if your major will get you into PA school, there's no end all be all major that will automatically get you into PA school, okay? I want you guys to hear me say this again. There is no perfect right end all be all major for PA school.  You do have to have a bachelor's degree to be eligible to apply to PA school. Now there is a little nuance to that because there are direct entry PA programs where you can get in straight from high school into these programs that you will do a bio, you're like your bio, your biology degree or your chemistry undergraduate degree in three years and then you will go directly into the PA program within the last two years of your direct entry program. So there is that option, but for the most part, the majority of programs are master's programs and they require you to have already obtained a bachelor's degree. So that is the only main thing that is required initially to be eligible. It doesn't matter what you major in. So yes, you can get in with whatever the major it is with a bachelor's of science and communication science and disorders, you can absolutely do that, Michelle, but you also have to make sure that you are meeting the requirements and that means looking to see what are the prerequisite courses and then on top of that, what are the hours that they're asking for in terms of direct patient hours? What are the healthcare experience hours that they may be asking for? What are the shadowing hours that they're asking for? You know, are they asking for volunteer hours? These are the things where it comes down to like being very picky and the nitty gritty of getting into PA school, okay? So please, please, please, if you take nothing else away from this, take away that absolutely do what you are passionate about doing, whatever that may be, major in that. If it is music you love to sing, you have a beautiful voice, you know, do that. Maybe you can provide some music therapy to your patients once you become a PA. Who knows, but whatever it is that you are passionate about doing, do that because you can meld it beautifully with your career as a PA and it will make you an overall more well-rounded person, okay? So absolutely do that. Just make sure that you are looking at the programs and doing your research to know and ensure that you meet all of the requirements, okay? All right, thank you so much for asking me this question. Please, you guys leave your comments in the comments section below. If you haven't already done so, subscribe, follow me on Instagram at AdanaThePA so you can know exactly when I'm putting out a new video and all of that good stuff and I will talk to you guys next time. Bye.


Dear friends Camarads like Sandy likes to to call me once in a while comrade Camarads My name is Eric Agnero I'm originally from Côte d'Ivoire, I recalls We supply 40% of the cacao in the world yet kids are now facing I mean travels like when you use roundups Roundup is now ditched over there because here our regulations Don't want them anymore, but over there is still flexible. Côte d'Ivoire cacao So every time you bike on a chocolate bar, remember those kids Steve Tom and I went to a Côte d'Ivoire and then we went to a plantation It was a nice experience to see where your cocoa comes from and How you can affect the people's lives over there Well Eric, so now I live in Vermont. I used to be a reporter for VOA When I was Slying around maybe I worked also for the private sector CNN, but since Three or four years ago. I found a nice place where I can call home Which is Vermont I decided to come here and I feel alright and there's a lady Who welcomed me here? Hope as she held my hands and then showed me around Do you know Peter? Peter Claver Call him sometimes So could be brother of yours How about a good kind Huh? Have you talked about this to Tom? She has a an idea every minute Hard to follow But it's fun Because she brings so much to our community and then Sandy also brought me back to a house which belongs to To the new Americans but also beyond the new Americans to all People decent franchised people around I see Clients of ours from different walks of life Women that are facing, you know trouble at home kids that are you know You know getting used to a play with a gun and then boom a tragedy You know Peter told me once Eric I've seen now. I'm seeing you know those kids those kids, you know at battery park Most of them, you know in the peripheries of our society. What's wrong? And then Sandy says It's because we need to empower more the families the women that you know This is the solution This year we couldn't You know do a big festival with you know because it's not only about festival it's also about Thanksgiving and Recognizing those who are doing it in our communities and Alv where Sandy brought me back is doing so much So much I see kids who normally would be after school Wandering around town Seeing they I think they went skiing So thank you so much for being here Now I would like Sandy to Come and tell us why she wants us this Sunday afternoons To be here together Sandy there Hi, and thank you all for coming out on a Spring day and a sunny spring day, which is of course very unusual Many of you know what my name is Sandy Baird and I founded many years ago and my daughter was murdered in a situation of domestic violence by her So-called boyfriend, I guess and that was in 1998 at that time my family Grant Kirchfield and Our daughter Rosie and our grandson Nathan formed alone. Nathan wasn't born at that time a Fund or a foundation called the Caroline fund and that was a fund that was to help women in Crisis situations to pay for instance an electricity bill I just got a call today from a woman who had a disconnect notice as Many of you have had I've had them to disconnect notices come when you haven't paid your electric bill And usually it can be for a very small amount So the Caroline fund recognizing that families were often in trouble financially for very small amounts of money We founded that and Caroline was often in that kind of a difficulty We founded the Caroline fund to help those women in crisis with small amounts of money And that was the original purpose and we did that and we still do that today We also though try to empower at this point projects Which and help those projects develop which help women in their families and by that I mean we don't just help women We also help men who have the Grace and the sense to honor their women and to empower and to Empower their own families so we help women and their families that that includes women and their male children and daughters of course, but also their husbands and their partners and all Women have those kind of men in their lives So we welcome that over the years We then have helped develop projects was which assists women and their families and we help other Projects who are also in existence to help women and their families I have been at AALV for a number of years and I do a lot of legal work here What I've noticed over the years is the help that this organization of AALV Gives to women and their families. We see and they and AALV sees women from all over the world I cannot tell you the stories that come through my office I think Nathan who's back there and who's a new young lawyer as I am and I'm always calling on him for help I can't tell you as but Nathan can all the stories of The situation for women throughout the world these people come to AALV for help And they come to me for legal help a lot These are women who live in our community I cannot even stress how important it is to recognize something that goes on in this community We have mothers in this community mothers of large families and thank heavens because as we all know Vermont's on a Demographic decline. It's a very aging state. We need children the women I see have the grace to give us big families They don't have to you know anymore women don't have to do that However, these women that I see are the mothers of large families who will be the future of the state of Vermont But they have been completely. I think neglected by our community by our state and by our country These are women I see every day who lack Education many of them come from even poorer parts of the planet many of them come from Africa Nepal Burundi from even the eastern block of countries Russia Vietnam those women come here under really dire circumstances. They are often Often the victims of war which we tend to forget. They are from countries that are at war They get out of those countries because they've been in refugee camps a lot of them and then they come often married to the United States Kind of a sign here and they come with husbands sometimes and they come with husbands who very often like American men Unfortunately, they come with men who very quickly often too often abuse them Act violently toward these mothers. They abandon them They're unable to get real child support because those husbands leave the state or Have other families to support I've and these women also are Largely uneducated some of them most of them do not speak English and most of them Some of them are illiterate in any language This is tough and the only institution I think in this town in this state Who recognize who they are? What they are giving to this community and What they are giving to the state of Vermont is this association called the Association of Africans living in Vermont They alone seem to get it What's happening in our community is these mothers have been totally neglected Most of the people I see mothers I see and fathers are black and brown people. So if we're going to recognize Truly if we are going to be a non racist non sexist society We have to recognize the reality in this community That the black women brown women and poor white women have been totally neglected Totally neglected without resources without education and without skills Because they haven't been taught that many skills and they are bringing up our future and it's Really tough. It is really a tough job to do that on your own the organization that recognizes that more than any other That I've seen really in the United States, and I've seen a lot Is a a LV the Association of Africans living in Vermont and their Directors Jacob Boughrey back there and tattoo. I've seen it and I was giving and for that We the Caroline fund are giving Tato a Certificate of service and to honor her work in this community Jacob Boughrey a man from Burkina Faso Tato is from Botswana And the Caroline fund is proud to give them these certificates of ward of their incredible I can't even tell you how incredible the work that's done here is and I'm so privileged and Honored to be here with them to do in my way to help them Help them help them up. I don't like to say help the mothers of this community. They support them They understand what's going on and I can't be happier enough in my old age To be here helping them out in this incredible role Anyway, so that's why we are giving them this award these was and the association Tato Jacob and Aalb but thank you all for being here and we're gonna have And I want to tell you he is also developing the second project The Caroline fund is devoting itself at this point to two projects that help all people and Families one is to try to make our law firm here Or economically viable in the first place because I've always done legal work, but I don't really charge the same rates as other people So However, that's one project. I would like to have an economically viable law firm and also a project an educational project called Vermont Institute of Civic and Community Involvement or Vicky, which was the original name of a very Special place for me, which is Burlington College Anyway, those are the two and Eric is the project director of Vicky in particular. Thank you Sandy I'm very honored so as Part of the Caroline fund through Vicky to now Give those certificates But I would like you to When they come Okay, I want to introduce one other person that's become part of this organization and that is this young man Will you stand up? His name is Tim Galloway and he is a young person And he I hope will be taking over because he's so youthful Some of the roles that I've had in this community some of the roles that we've all acted on Okay, and the other person that I want to say assisted in this gathering. It's Robin Lloyd who is back there who is Who is the chair or I don't know what's your role and will the women's international League of Peace and Freedom? Thank you Sandy so I Think Robin will give the title certificate and then you will give that of Jacob And then Peter will give that of a LV and We like that to very much so she will Take the one from a LV and say a word So if I'm very good Okay Yeah, stand here and Then give that to Jacob You know what people call him in this office is Papa Jacob He is the quintessential father that we all wish we had and most of us Maybe did or didn't I'll leave it that way. This is Jacob Papa Jacob tell me what oh Oh Ladies and gentlemen welcome Robin Lloyd who's From a will sponsor of this event, but also a very very good partner of the Caroline fund, please Welcome Robin Lloyd About Robin because I'm so impressed with her We have many kind of disagreements as I do with most people in the world But we always part comrades Robin's fan the reason that I think Robin deserve deserves a special recognition Her family is the Lloyd family who was involved in World War one correct and They see you can tell your own story But one of the things that Robin's family has always recognized is the connection between Women's struggles in the world and peace as I understand She's one of the most anti-war activists that I know of on earth her family was involved I think in 1913 correct and having women go to the Hague in Holland With and met with women from Germany to try to make peace in World War one They recognize that women have common sense and that war is really not good for the planet under any circumstances So I've always linked in my mind Robin's struggles for women's rights with all of our struggle to be to bring wars to an end But anyway, that's Robin Robin, would you mind give the award to Tato She said Tato ne parle pas français comment Tato you have to say what? Oh, yeah Please Please stay Tato. What last now? You know the first time I you know Peter and I met I was trying to be the mayor of The city that my uncle is like So why would you want that job? Especially now Peter. Oh It's a big place here Good to demand the remit of Tato these certificates of recognition for The world of old job On behalf of the community I'd like to thank you For all that you have done to make Burlington and the region and the state a welcoming place for those who are escaping poverty violence war A of B has been such a lifeline to thousands of people. Thank you Well, good afternoon everyone I almost always and I've been designated to speak and I do not like public speaking But I in fact, I feel very honored. Thank you, Cindy the carol and fun grand. Thank you Thank you everyone who's come here to to support this I Look around and I see some of our team Jacob and I talk a lot about how we would not be able to support The community the way we are able to without the incredible Stuff that we have We all come from walks of life. In fact, most of our staff came from refugee camps We do this work every day listen to family stories women's harrowing stories of their experiences of war Children who are getting traumatized and our team getting re-traumatized We don't have the normal eight or nine to five Our work we work throughout the day throughout the night when necessary So I greatly appreciate the opportunity to actually recognize our stuff who Work day and night to make sure that there's some stability that the clients that we serve I Have an understanding of what the mail that comes through their doors mean That when children are struggling their children are struggling in schools They're the first people to make a phone call or be present in school to advocate mothers who Trying their best to work in this community as well as continue to be really the backbone of their Families mothers who are now are starting to tell us more of their stories stories of joy stories of Getting married while some at a much younger age that the culture that they had but then being disrupted in the middle of wanting to make livelihoods going through Bushes being followed by You know people who are trying to come after them or their spouse Mothers who had to carry the baby says they were sitting refugee camps Who still made sure that the family's eight in the middle of the war?  We couldn't do this work Really without everybody's support. We couldn't do this work without send his support Cindy I I don't know how you do this every day. This woman is here before almost all of us every day and Sometimes she will leave very late Sometimes I'm in court with her. I come back. I'm exhausted and she goes like who's coming next I'm waiting for fire Where does she get this energy? But it just shows how passionate and The kind of heart you have for for for the community regardless of color regardless of you know sexual orientation regardless of Where people come from this is what? Actually motivates us to keep going we feel very very fortunate to be able to serve the community It's vocation for us The community we wouldn't be who we are really without the community We are grateful that we we are in the great state of Vermont We are grateful that we get to participate and hopefully most people who are citizens will go register to vote Because we need everybody to vote I just have to say that we have a woman mayor in Wellington I don't know what that means I try not to get in politics But you know these are the changes that we get to be part of and I very grateful to to be in Somewhat a safe space where we can be ourselves so we can talk about our traumas and talk about Solutions in the end so on behalf of the incredible team at a alv. We are very grateful and thank you so much for recognizing all it That's all right, baby. I just want to tell you all a story about how Tata works It was a Friday night. It was about 11 o'clock on one Friday night That she called me because one of our most precious clients Was locked up in our police station. This is a rather horrifying tale about our police station, too I hate to I hate to say it in a way because I support our police chief and in general our police department It was about the 11 o'clock at night and she called me and she said one of our young women who has having enormous enormous problems adjusting to the United States she was a woman from South Sudan a Woman who had been more or less brought to the United States by An American citizen also from South Sudan married terribly terribly abused by her husband Abandoned in South Sudan by her husband who took her kids all her documents and came back to the United States Leaving her there with some miracle a miracle. I can't tell you how many miracles this woman Tato and Jacob Conduct on a regular basis, although they're not saints yet, right anyway, so yet So she this woman however had been locked up in the police station apparently she'd gotten into a fight We're the neighbor an easy enough thing to do as as we all know Americans do it all the time This woman however was locked up in the police station and it was 11 30 So Tato asked if I could help and so I called the police station something that was normal Five ten years ago. You could always call the police station and get an answer. Couldn't you? Couldn't you can't anymore by the way? You can't anymore, but anyway, so I didn't get an answer And I was really worried and so I went down to the police station to see if I could at least Tell this poor woman that she doesn't have to talk to the police She has a constitutional right to remain silent. I didn't know what they were going to do I was told by Tato that they were going to interrogate the woman So I went down and met her this is 11 o'clock on a Friday night when a woman like her should be out on the date Right and having a good time Anyway, so I went to the police station and you could not get into the police station None of the doors were open. No dispatcher. Nobody would answer the phone I wandered around in battery park kind of scared as a matter of fact and finally I sat in my car Tato was there and the police saw a cop finally came out of the police station And I said look at my clients in there. I need to go talk to that woman. Do you know what he said? Lawyers are not allowed Can you imagine this? Anyway, but Tato was right there with me and she persisted we went into the police station But still couldn't talk anybody Right and we finally had to leave we never did get in contact with that woman that night Anyway, so that but that's the story of how she works every weekend This is what this woman does and Jacob and as far as I can tell every night every Saturday and every Sunday It is an amazing role that they are playing in this community I can't tell you how honored I am to be working with them Especially because I'm rather elderly and so I feel enormously privileged to be here. Hey doing Helping to do the work of this association last. I want to say happy st. Patrick's day Happy st. Patrick's and good for the Irish. They are the most rebellious people except it except for the scots of all the people Thank you. Thank you. Thank you very much, Sandy. Yes now Sandy, you know in French we see now I mean the petit plat dans les grands. There's good food very very good food Socialize to network and then to say again. Thank you to tattoo. Thank you to Joseph to Jacob Thank you to a lv for the wonderful job the wonderful work they're doing Merci beaucoup I've never done simple Tato who keeps it I'm a lawyer that I'm trying to control This is Jessica who does marvelous Yeah Thank you so much for Thank you so much for coming and Supporting a lv work. I think we would not be able to do what we are doing without this community support You know every day we come in thinking that we will be quitting the next day But at the end of the day you leave Knowing that you've learned something because when people come and share the story you see how resilient they are When a mother was being resettled year for more than 10 years is able to bring his two kids were lost in war in Ethiopia and bringing them to LB and said this is my family. This is where we should come if I die You remember that we as human being Cannot be Helpful to each other if we have not had a chance to meet God and also meet fellow Community members who know that humanity deserve better than nothing else than be a supporting each other And that's why we are always grateful and hopeful that we will be able to continue and our folks will continue to support our work And I support the team here at the LB. Thank you so much


As of April 19th, 2020, the World Health Organization reported a total of 2,241,778 confirmed cases of COVID-19 globally, with 152,551 deaths. There are now 821,860 confirmed cases in the region of the Americas. As of April 20th, 2020, St. Nusha has a total of 15 confirmed cases of COVID-19. Of these, 15 cases, 13 of them have recovered to date and have been discharged from hospital care, two of whom were repatriated to the United Kingdom earlier in April. So at present, we only have two positive cases in isolation and the respiratory hospital. Our last confirmed case was reported on Friday, April 10th, 2020 and since then, of the 58 tests run, all have been negative for COVID-19. Though we at the Ministry of Health are heartened by these data trends, we note that this should not provide us with a false sense of security and allow us to believe that we are no longer at risk for the COVID-19 outbreak. It is our awareness of this that drives our public health team to continue to work intensively in implementing all of the core components of our health sector response, which includes testing, isolating, treatment and care, contact tracing and public education. We appeal to every individual to keep a strict adherence to the infection prevention and control guidelines, which continue to be issued by the Ministry of Health, as these, though simple, are inexpensive and effective methods to reduce the risk for spreading COVID-19. St. Lucia continues to be on partial shutdown and 10-hour curfew, which spans the period from 7pm to 5am daily. We are still at a critical juncture in the implementation of the national COVID-19 response. As such, be mindful that everyone with the exception of essential service workers are required to remain at home unless leaving for food or medical purposes. There should be no mass crowd events and social gatherings. These measures collectively help in achieving the physical distance required to reduce the risk of spreading COVID-19 within our population. The public is also advised against going to public places with flu-like symptoms, which include fever, coughing and sneezing. When visiting the supermarket or public places, wear your homemade mask. Practice shopping first with your eyes instead of your hands. Refrain from touching items unless you intend to buy them. We continue to request that every individual practice the standard recommendations to prevent the spread of infection. These include regular hand washing with soap and water or alcohol-based hand sanitizer when soap and water is not available.  Avoid close contact with anyone showing symptoms of respiratory illness such as coughing and sneezing. Also, seek medical attention and share your travel history with your healthcare provider if you have symptoms suggestive of respiratory illness. You can visit our respiratory clinics or call one of the clinical medical lines for advice. The Department of Health and Wellness will continue providing regular updates on COVID-19.


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take down Jim and Brainerd has produced a number of division one collegiate and professional athletes. But now the gym has its eyes set on something even bigger and better. Clayton Castle has that story. The excitement surrounding the visit of the Stanley Cup to Brainerd was evident a few months ago when Brainerd native and current Pittsburgh penguin Josh Archibald visited take down Jim and Brainerd experiencing more momentum and growth and originally thought that Jim broke ground today on a new renovation and expansion project. Well, we have a 28,000 square foot renovation. We're currently in 14,000 square feet. Pretty excited. We we're gonna be having a lot of renovations down to this space as well. So it's pretty exciting. The project will add a large open gym area featuring a basketball court in a turf field. It will also add a spin room and other features, including renovating the current space. The project will build on take down Jim's mission of attracting people of all ages and backgrounds. We have stuff and we have programs from little athletes all the way up through the adults. We want to help kids get better at their sports. Maybe someday leave Brainerd and go on to a scholar college scholarship or beyond. We want adults to get back into shame. Manager Joe Chardelli says that the project will also benefit those the gym currently serves. But we want them to come to us and we want to provide the best training opportunity that they can so they could become the best athlete that they are. The momentum and growth takedown Jim has seen in its first four years is what motivates owner Sean Kramer and the entire staff at the gym. What gets us most excited is the opportunity that we give our athletes. It doesn't matter from a young person just getting into athletics becoming more athletic all the way through if you're a professional athlete trying to make the pros. I think for us having a high school athlete that we turn into a better athlete as a result gets a scholarship to college. That's what gets us really motivated. The takedown Jim renovation and expansion is expected to be completed in mid to late spring 2018 reporting in Brainerd, Clayton Castle, Lakeland news and the estimated cost of the entire project is about $7 million. If you've enjoyed this segment of Lakeland news, please consider making a tax deductible contribution to Lakeland Public Television.


Hello, welcome back to theCUBE's live coverage here in San Francisco for Databricks's Data Plus AI Summit. I'm John Furrier, host of theCUBE. Rob Stritch, I've been here two days, breaking down all the action. We had the analysts on, and now we're psyched to have the co-founder and chief architect, Natasha Haria, who's here with us in the press room. Thanks for making time out of your super busy schedule. Been on stage, been giving demos, meetings, back to back, having fun? Yeah, having fun, yeah, definitely being hectic, but it's awesome to talk to you. It's great to see the rise of Databricks and continued growth. Obviously a big event here, 12,000 people. Younger crowd, I see a lot of open source developers out there and I see a lot of data people, engineering, and you guys have the theme going on around democratizing AI, basically, and bringing AI to the masses. Bold vision, great vision, we love it. What does that mean for Databricks? As you guys have rolled out, you've been very successful as a company, you continue to get more data. You've been doing the Lake House, you have great open source presence with your code, data sharing's been booming, things are happening. What's the key ingredient to make AI work for you guys? Yeah, great question. So I think just supporting generative AI is a very natural progression to what we've been doing. Since we started out, we set out to democratize working with very large data sets. That was our bread and butter. We integrated it into the traditional data stack with data warehousing that we've been doing and we've always supported machine learning and data science. And generative AI is a very effective, a very powerful form of machine learning that basically every company's trying to apply. It's extremely general in its applications. And we think we have both some great existing foundations to support the whole lifecycle of it, from the data perhaps to the serving and so on. And we reach the exact same users in each enterprise that want to build the generative AI stuff. So it's awesome to work with them. So I want to ask your thoughts on it. We're going to get to the security piece in a minute. Obviously people want their IP to be protected. Data is their intellectual property. You guys have a very strong position on that in the keynote. We'll get that in a second. But the enterprise needs and cloud enablement are two factors we've been seeing a lot of conversations around. How do you see the LLM movement, large language models and generative AI specifically, take advantage of this next generation cloud as well as the needs of the enterprise, which are different than say a straight up developer. Yeah, the needs of the enterprise are very different and this is exactly the thing that we're specializing in and the thing that I think we can provide unique value in even compared to the very generic like language model as a service type of providers. So a lot of those providers are focused on consumer apps that are trained with just data from the internet that can talk about public knowledge in various ways. They're very amazing kind of applications like chat GPD. You can ask it about anything, eventually it'll do things like search the web and it's great. But in the enterprise, first of all, you need like a level of precision and reliability that's quite a bit higher. That's hard to build. There's a lot of R and D in that. Then you also wanted to understand your specific data that you've got in your company and your specific jargon and business terms and so on. So for example, you might have trade secrets, right? If you're TSMC manufacturing processors better than anyone else in the world, there's a whole lot of stuff in there that you want your internal people to know about but there's no way like open AI knows about it. There may be other proprietary knowledge. Like if you have medical research form and like all the science papers say one thing about an enzyme and you just discovered that it works a different way and it's gonna lead to the next breakthrough. How do you get that research into like your AI applications? So this is the kind of thing that we're enabling folks to do. It's very exciting to see the interest in it already. Yeah, we've reported that AWS and Bedrock has that same capability to keep everything in the VPCs of the clouds. You can start to segment kind of that data access. Well, how does that change the role of authorization and access controls? How do you see that playing as? That part of governance or management? Who controls that piece? Yeah, yeah, so we think enterprises will want to control it in very domain-specific ways and we're building the governance tools for AI based on the rich governance tools we already have for data and for sort of more classical data science and machine learning. We have something called Unity Catalog which is the only basically data catalog in the industry that also spans AI and unstructured files and gives you very rich controls, lineage quality across them. And with Generative AI, one of the new challenges is you want to train on lots of data. You maybe you get it from the web or other places but at the same time, some of it is wrong. Maybe some of the policies around copyrighted data and use of that are changing over time. So you really want to trace exactly what data went into this and be able to fix that as you release your applications. And I think increasingly just through regulation you'll be required to sort of explain and document what went in there. So it is a new set of use cases where this matters. So I want to ask you about the competition. We were just talking with our analyst team on our segment earlier today and before we came on camera about the other guides. They want to manage the data and control the data and govern it and then they want to let the people build apps on top, see analytics. You guys, your vision is to govern the data and the analytics, right? What's the vision of Databricks? What's the specific goal? I mean, 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 that 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 on, 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 replatform 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, great call out there. I want to ask you about the Mosaic ML you brought up, training, big acquisition, big number, 1.3 billion. We've been tracking Davine's company, obviously we've known him before he sold his first company to Intel and we've kind of been reporting since he launched his company. Those guys have a lot of GPUs on hand and they were going to do more.  Obviously the Yang to training is inference but they're the training side. Is there more work to do there on the Cat-Bex side for the training piece with Mosaic ML? And how does that relate into what you're building or have built in the current Databricks? Yeah, there definitely is work and the way Mosaic ML handled capacity, a lot of it was sort of a very kind of easy to use SAS model where you can sort of submit a job and they'll run it and they'll have a big pool of GPUs that they can assign to different workloads or even use internally for research when it's idle. So we really like that model. It's basically a serverless model which is also what we've been doing with data warehousing and with model serving now and eventually basically everything on our platform will work that way. So we think a provider like us or like Mosaic who can serve many customers and share a pool of resources and optimize that will just provide inherent advantages and availability and TCO and like ease of experimentation. How would you describe for the folks watching who are learning about inference and training? Which is harder, can you even compare them they're apples to oranges? What's the difference between training and inference from an impact standpoint, from architecture, how you think about it, how would you explain training and inference? Because they go hand in hand, they're ying and yang but they're two different things. They do, yeah, so training is when you build your model. It includes a lot of, in some cases, it includes a lot of sort of machine learning, sort of know how, like what exactly is my objective? How do I get really good examples of data? How do I even evaluate the quality? And it includes heavy weight computation on the biggest GPUs. Inference is actually serving predictions and it can range from sort of trivial if you don't have a high rate of requests, for say internal users in your company and you get a couple of requests per minute, then it's not very expensive to serve. It can range from that to extremely challenging if you have many automated systems, like every time a log record is written by a piece of software, you wanna analyze it or if you care about super low latency, like you're doing ad bidding and you wanna read the text of the web page and place an ad. So there's a huge amount of depth there. In general, inference is being easier to optimize. If you can train a model that does a certain thing, there are so many techniques you can use to make it faster and these are some of the ones that we package and our model serving solution and some of the ones where Mosaic ML has done research. It's a group of researchers who started the company. Yeah, great description. Talk about the LLMs because I wanna get into that.  Large language models, obviously. There's different sizes as proprietary. There's a third party in a long tail of open source developing nicely. When should someone build their own LLM? When should they work with a managed service? Because there's trade-offs. You got to get to know your data. That's the thing that came out of the conference for us as you guys just talking to your customers. Know your data and you can get value out of it. But when do I, how do I deal with LLMs? What, if I want one, what do I do? Do I just get my data and call it an LLM? Yeah, no, great question. Yeah, there is work required to build. You could build something, but to build one that's really useful, you're going to need to do additional work. Otherwise, you'll get something that's fused back your own data in sort of a random format. But I think there are a few factors to think about. So one of them is the control of your data and it's security, it's geographic placement, stuff like that. Like for example, if there's personal information in there, like you may, you got to keep it in specific regions. If there's like trade secrets and stuff like that that you don't want to pass. So that's kind of an obvious one. A second one that I think is even more interesting is you may want to build your own model if you want the absolute best performance in terms of quality and customizability. So this is like, you know, like basically in any area where you want to be able to iterate on it and improve quality, you want to control all the pieces. And it's hard to do with these one size fits all models. For example, something like chat GPT gets a lot of value out of Reddit because it's called Reddit, it knows random stuff that people talked about. But if you're in a domain where you don't want to have that stuff, then you can't really ask them to remove it. It's all baked in there. So if you- That's a public corpus and they keep adding to it. That's your point about IP as well. Yeah, it is. Yeah, I mean, not to mention the IP. Yeah, exactly. But they have, yeah, even the stuff on the web, like we think all the world's knowledge is on the web. But yeah, the web is also full of like junk, SEO, totally incorrect things, yes. Which you may not want to think to know about. And if you also care about like computational cost or other interactions, like what if I want the model that knows how to talk to my internal database, like see our user's latest orders, do something for them. All that stuff is easier to do to customize to get to like 99.9% reliability if you can own the whole stack. And then I think the third reason would be a just a strategic reason like you may, if you think you'll develop, like if you have unique data, you can probably very soon create unique like AI products also and you may want a team that can do that. And you could sell like the chat GPD of like finance or whatever, like retail, whatever it is. It'd be video, that's the key. Yeah, video, that's an awesome one, yeah.  Well, we've got a lot of linguistics and then I want to ask you about the future because I see hearing some people in the hallway we're kind of riffing with some of the entrepreneurs that built on top of Databricks. You know, old school techniques like Kumo has a recommendation engine background. They essentially have personalization in neural networks and graph databases as a service with you guys. So they're like a super on top of Databricks. We're going to see more of those. We think we'll see more examples like that. But you know, a lot of the old AI models were built on seed, seed algorithms and setting up the ontologies, if you will, language. Do you see more dynamic ad hoc LLMs emerging in the future where things will just grow dynamically with AI? Do you see AI going down that path? So adapting more to sort of new modalities of data. I think it could happen, yeah. I think it is hard, like even though LLMs are powerful it's actually not super easy to bridge the traditional machine learning and the signals there and all the metrics. So I think it's a somewhat open question, but definitely the idea of integrating LLMs with things like knowledge graphs and other, you know, like structured tables makes a lot of sense. We're also seeing some really cool multimodal applications if you've got some time series or images or videos, you know. Yeah, so it will be, that's one of the things I'm really excited about with customizability as well. You can build models for the unique domains in your application. If you've got the best image sensors on the market, the best gene sequencers, you know, if you've got like a jet plane with like 10,000 sensors in it, you can build AIs that understand that modality. Yeah, this is what we call the data developer that's emerging. We see a future where AI is going to be native in applications today. Most people are thinking about AI coming into an ex-application and adding value, obviously augmentation or bolt-on. We think there's also going to be a direction where there's going to be native AI applications that might be lighter weight faster but in the application and then more horizontal scalable data products that will need to work in real time with these data sets. What's your reaction to that? Yeah, absolutely. So you talked about the data developer. So you've been seeing a lot of our top customers, major software vendors actually, who say things like I have several thousand, you know, engineers and I want all of my engineering teams that build a feature to also own their data, like to do their data engineering or to do their own ML engineering.  And of course, they want those feature teams to own it. So we're seeing, and actually even internally, you know, we announced AI features and like our SQL editors, notebooks and search, so we're also embedding basically engineers into those things. I'm going to say you're like a masterclass here on theCUBE, really appreciate your time. Final couple of questions, as you look at the future, you know, reasoning has been around for a while in AI circles as you know, metadata got reasoning to it. What's the big aha moment? What's the big inflection point now about reasoning that was different than before? Was it the LLMs? Is it the more compute power? Is it the confluence of all these things coming together at this one time? Why is reasoning better now than it was? Yeah, that's an interesting question. So I would actually say there are different types of reasoning. I think with LLMs and the kind of stuff you can do with chain of thought, it's much easier to do kind of fuzzy reasoning with kind of fuzzy concepts. So that's really the thing that they've given us. Like computers are very good at precise stuff.  Those in some sense did reasoning, but in a very different way. And actually LLMs are not that good at that. And I actually think you'll want to combine them as external tools. So for fuzzy stuff, where like, you know, there's no precise description, but there's like common sense, you can do it. But for stuff like solving math problems and so on, honestly, like I think the existing tools are in many ways more useful and they can do things that like people already couldn't do. And maybe you'll combine them for something even better. Yeah, that's a great question on the fuzzy versus more specific. Let's talk about the vector database. It's been all the rage. Everyone's announcing a vector database. I mean, you know, who doesn't have a vector database these days? So why is it important to have a vector database versus using Office Shelf? I know you get platforms as big wave right now. Why a vector database? Why is it important? Yeah, I think, I mean, I think vector search is important. So vector index, the ability to search on these, you know, numerical embeddings that can do fuzzy matching. But I do think it's something that may, will probably be incorporated into many technologies in the same way that like most modern, you know, data processing engines have like, you know, basically like Starks and arrays and stuff like that. Basically these like sort of unstructured data types and inside, I think maybe soon they'll have, you know, vectors and vector search. So we're definitely looking at it more holistically as like, you know, in your tables and your engine, can you just use these? But it does open up this, you know, powerful like matching. It is really more of search less database kind of concept. Yeah, that's my feeling. We'll see how it develops. You know, it's a new, it's a new area for sure. Final question for you, and I appreciate your time. What do you guys have to do now at Databricks to bring this AI to life? That's net new capabilities and how much are you leveraging internally with previous work done? Yeah, great question. Yeah, so I think that we're building on a lot of the foundation in terms of data management, governance data pipelines and ML ops that we had, including like serving and monitoring models. The most, the biggest difference with generative AI language and image and so on is it's much harder to automatically evaluate the output. Right, like before you were doing predictions, we just see like, hey, how many of the ads I showed that a person click or whatever, it's just a number. With this, how can you tell like, whether this paragraph you created today is better than the one you created yesterday? So people are looking for new tools there. You can do some automated stuff. You know, you can ask a model, you can look for keywords. I think you bring in human evaluators a lot more and you ask them, what do you think of this? You know, but that's, I think, the big open thing. So we have like UIs now in our ML platform offering where like you can compare, you know, just a big spreadsheet of text snippets and say which one you like. And we're working on, you know, various other ways to do this, including integration with partners in this space. As chief technologist, I got to ask you a question, two points to the question. What's the coolest thing you've seen here at the event and what's the coolest thing you're working on? Yeah, great question. I think, I mean, honestly, the coolest thing I saw was the Rivian, you know, vehicle in the expo hall and like the discussion of the tech behind that.  Basically, you know, as it drives like, every component of that system which works in these extreme environments, they monitor it, they have software there, they can update it and sort of they learn how to make that on better. So I think it's sort of the future of a lot of, a lot of basically stuff that, you know, you build industrially, it makes so much sense. And things we're working on, I think the thing I'm really excited about is basically opening data to, you know, less technical users to business users directly with Lakehouse IQ, which is this knowledge engine that sort of learns how you query your data, how that maps to business questions. And, you know, lots of folks in the industry are working on this, but if this succeeds, I think it will open up data, AI, and so on to way more users. Very cool to see that. Greg, a question keeps jumping in my head. Final, final question, I promise. When Ali's on stage, he said the format wars are over, that unification measures really hit home to a lot of people that like that. Where'd that come from? There's just philosophy of data, you know, we open source, but that eliminates a lot of this confusion. What does that mean when you guys, when he says you're eliminating the format war? Yeah, so this is about the different Lakehouse data format, so it's kind of an interesting evolution where for a while, you know, everyone in the open source world was just using Apache Parquet for large data sets, but then it was only a file format, it couldn't do transactions and stuff, so these three different open source projects started that do that, and all of them, like 99.99% of the bytes are these Parquet files, and there's little, little files on the side with metadata that tell you like, you know, here's the latest version, here's the transaction log, and you know, I mean, they started out, we started Delta Lake, there was Apache Iceberg and Apache Hootie, and then I think a lot of vendors kind of got pressure from customers to support open data, but they were worried about like, there being a single format that runs, especially the competitors format, so they would say, oh yeah, I do support it, but it's this other one. And so it creates a lot of confusion for customers, right? So because these are so similar, we built a capability that just in your Delta table, you can read and write it as all three formats, it simply just creates that metadata three times, and you can just open it up, like any tool that understands, say Iceberg, for example, can connect through that catalog API and write to it. So we think, I mean, all our customers cheered when they saw this, because they had these headaches of like, oh shoot, am I in the wrong ecosystem? And hopefully, you know, hopefully it gets people to focus on like what you do with the data itself, and really like, you know, like let customers actually move workloads between platforms and use the best one. To win all around, congratulations. Thanks for coming on, and congratulations on the success. Tell Ali, we passed along, we missed him on this one. I knew he was super busy as well. I appreciate you taking the time.  Thanks a lot. Okay, it's theCUBE. Here with the co-founder of Databricks, sharing all the data, masterclass of what's going on with Databricks today, and then in the future, as Gen AI comes up forward, it's going to be a developer centric, business-focused world. Thanks for coming up today. Appreciate your time.