And here we go. Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of DataVercity. We'd like to thank you for joining this DataVercity webinar moving from a relational model to NoSQL, sponsored today by CouchBase. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen or if you'd like to tweet, we encourage you to share highlights or questions via Twitter using hashtag DataVercity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. Just click the chat icon in the bottom middle of your screen for that feature. And as always, we will send a follow-up email within two business days, containing links to the slides, the recording of this session, and additional information requests throughout the webinar. Now let me introduce to you our speaker today, Andrew Oliver. Andrew is the Director of Product Marketing, Product Evangelism at CouchBase. He taught himself to code when he was just eight years old and by 20 he was a DBA because he was the last one who knew how to operate the database and HP UX boxes. Now he focuses on telling CouchBase's story to a technical audience. And with that, I will give the floor to Andrew to get today's webinar started. Hello and welcome. Thanks, Shannon. So today we're gonna talk about how you can run a relational database schema on a NoSQL database. We use CouchBase for example, but the advice is generally applicable minus that many NoSQL databases obviously don't support SQL. As mentioned, I'm Andy Oliver and you can find me on Twitter at atacoliver. You can also email me at andrew.oliver at couchbase.com if you have questions and anything you wanna follow up. I wanna keep this at a high level.  We'll get a taste for the technical issues and differences. However, I'm not gonna get mired in the details. So we're not gonna go down into some of the differences on how indexes are handled and what have you. Just suffice to say that there's some additional sort of technical details that can affect you when you start doing more of the optimization type tasks. So we'll look at NoSQL. We'll talk a little bit about CouchBase. I wanna look at some of our migration options and really why you choose the methodology we're covering today, specifically migrating without optimizing and then optimizing as needed. First, let's look at NoSQL. When all transactions were offline and people browsed or met in person to conduct any and all business, batching data and relatively low volume transactions were fine. These days, however, users look at hundreds of things before making a purchase and everything's online and mobile. Supply chains are monitored and managed in real time. Whereas my package, where did the problem occur and everything especially in manufacturing midst data. So volumes of both reads and writes are way up. So people turn to NoSQL databases to make that happen. However, the first generation of NoSQL databases didn't do joins, they didn't do SQL, they didn't do transactions. People basically put up with them because they had to to meet some of their scalability and performance requirements, but they did their real work offline on mainframes and RGMSs. Now with business changing more rapidly, uptime requirements reaching 100% and everything moving online, this architecture is even more relevant. Moreover, now you need to analyze in real time. There's no more time to ship data over to another system, do the ETL, do some processing and then do some analysis. Analytics generally must happen now. What is my situation today? Even if I'm doing some projections or whatever, I kind of want that to happen more real time these days. That's why NoSQL databases like Couchbase support SQL joins transactions. And in Couchbase's case, we even embed an MPP. If you're not familiar with an MPP, Teradata and then these are those things are MPP type databases. We're moving to an area to an era where NoSQL isn't just for your volume, but also for reliability and flexibility as well. The most popular kind of NoSQL database uses JSON documents to store and represent data. JSON is the JavaScript object notation. It embeds key value pairs in arrays. It can embed sub documents. It can represent numbers, strings and bullions. JSON is supported by nearly every major language and platform and library. It's the language of the modern services of modern services on the internet. Even if your database doesn't support JSON, your application and application services are likely speaking JSON. The document is most analogous to a row in an RDBMS. Different NoSQL databases have different storage paradigms, but Couchbase stores documents in a bucket. However, a bucket is more like a database instance than a table. A table is most analogous to a set of documents with the same type, which we generally concatenate as part of the ID. So I can essentially represent my RDBMS tables on the left as sets of documents on the right. And if you look, you can see what's going on. One thing to note though, is while RDBMS is generally enforced constraints, NoSQL databases support agility or schema on read, meaning I can add a field to a document or emit a field from a document. There are various reasons this is quote unquote better and better isn't quotes, but it does mean that any constraint enforcement moves the query or application way. Another thing that is different is that generally timestamps are either a string, array or number. Conversions are handled at the query level. We recommend using epoch the number style or UNIX time as it might be referred. Let's talk about Couchbase. Couchbase is a NoSQL database that supports transactions, SQL and full tech search. It is a memory first, shared nothing distributed architecture. What this means is that the default behavior is that you can read a right from any node, the right will be put in the memory and replicated to at least one node. It'll be written to disk later. This is configurable, but then your application moves on. Once we've got it in memory replicated at least another node, your application moves on. If any node goes down, your clients don't care. They can just connect to a different node. Data reliability is assured through replication. I wanna make sure everyone has a good taste of how SQL and JSON works. So I've kind of sprinkled SQL queries through the slides and on this one, they're just window dressing. On here, you see an insert statement creating, you see creating an index, you see a couple of deletes. We'll see plenty of selects throughout, but just to give you a little bit of a taste on what SQL and JSON might look like. Couchbase is one of the leading NoSQL databases. You can build the community version via GitHub. We also have a pro version and announced a cloud version which you can sign up for. Couchbase can run local, you can run it on your own cloud or multiple clouds. It's RackAware built on Kubernetes, all of that stuff. You can also sign up for a fully managed as a service offering. We're now gonna go into all the sort of system and infrastructure type stuff today because we're focusing more on the SQL, on NoSQL topic but suffice to say there's a lot of options there and it supports a modern multi-cloud cloud-based architecture as well as on-premise with most people's Kubernetes stacks. We're gonna focus on Couchbase server today but Couchbase has a mobile offering in a gateway that allows local peer-to-peer and client server edge applications. While we focus on mobile usage, building resilient applications is just as important for desktop and other types of applications as well. So whether it's a grocery store or a factory floor, if your connection with cloud or your data center is severed and you don't want your application to cease function even if it has to reduce capabilities, Couchbase has an offering that offers the same kinds of capabilities on a phone, desktop, embedded web server and yet can sync to a database server and offers conflict resolution and all the stuff you need to make this work. So if you need to create a resilient application that works even if your internet connection's dead but does sync and work together once it comes back up, then we've got some stuff for you. Whether you're in the cloud or in a set of data centers, Couchbase supports high availability through replication and disaster recovery through cross data center replication. This lets you support multiple data centers or multiple geographic zones for the case of the cloud. This is highly configurable but now since you can read and write locally and have the right data go to other geographies. Most applications that go no SQL are greenfield but we've seen more migrations recently and there are a lot of reasons for this. The number one reason is that there's an application that while it works, now has the scale. Now there are new demands whether it's because a cell provider wants to give users more visibility into their data usage unless make the external service remote, remotely available or a grocer that suddenly has a lot of online orders or a top financial company that has ever increasing volume and is pursuing more service orientating to solidate architecture. Each kind of follows this general pattern. Lots of change, more rapid change, more scale but they need it to be more reliable. When we strategize, we need to think about some things. What kind of risk can we tolerate? What skills and expertise do we have? What is the cost of migration? What are our performance and scalability requirements? There are a lot of different options we have when pursuing this. Do we want to rewrite the whole thing? That's a lot of effort, a lot of risk. It does give us an opportunity to achieve the most optimal performance and scale. Another option is to migrate the database and totally rewrite the data layer in the application. This is less effort, less risk but still quite a lot of effort and risk. Performance and scale is probably almost as good as rewriting but we've got to consider the risk and the effort. We can keep everything as is but refactor our data and logic and RDBMS schema to a best practices schema. We're keeping our business logic, most of our data logic but pursuing optimization upfront. Still a fair amount of risk, still a fair amount of effort, a lot of optimization, a lot of performance. We can also optimize layer, basically just migrate the table rows documents, make a few changes to our queries as possible, make a few changes to our queries as possible. Our initial performance and scale may not be great but it'll work and it's low risk. Finally, we could just not bother optimizing it and host it. So really we're balancing performance and scale against our effort and risk. Generally, I see organizations doing more of a two or three than a zero or one. Some maybe do four for applications that can be moved over just for consolidation reasons that the performance or scale requirements aren't very high. So we're gonna focus sort of on that option number three, optimize later. So how do we migrate without denormalizing the data into a big document? First, we need a no SQL database that supports SQL, asset transactions and joins. We need to understand how the tables and rows relate to documents. There will be SQL dialect transitions similar to when you go between two RDBMSs even if those two databases support the ANSI standard. Anybody that's done this knows that they've got to make some changes. Once we get everything running, we'll experience lower performance on more servers. We need to expect that upfront. So if we just do this transition, we don't do any optimizations. We'll probably have lower performance using more servers. So let's set our expectations up front so people aren't like, wow, that no SQL database wasn't very good. Moreover, when we moved to distributed architecture, there's a different sweet spot. So for cache base, a key value lookup might be faster because we're gonna do it out of memory. An index lookup on five rows, not in the cache is probably slower than on a single server database. However, a query that hits lots of rows might be faster because we can pipeline it for multiple servers at once. I'm gonna say a lot of might and maybe because it depends on your topology, your data and your application and how it uses the data. But we shouldn't expect the performance to be one for one in all cases. Let's look at how things relate between your RDBMS and couch base. A set of typed like fields are analogous to a table. So what I've done here is I've got my relational table on the left. I've got a document on the right and you notice the document has a type and that that type is also included in the ID. I've got the same sort of ID as on the left and on the right. So I've got a user ID that also includes the type. So this is basically, we've got a bucket. It contains multiple different documents but this is analogous to a table. So a table is a document where type equals user that's analogous to the user table in our RDBMS. A document is analogous to a row. Light name fields in a document are analogous to a column. A conventional relational model tries to have minimally redundant data. This minimizes storage, enforces a schema, does joins in memory on a single server. It also requires transactions to put Humpty back together again. So where in my document, I've got everything potentially in one document, I don't need a separate transaction just to put all of these different pieces of the entity together. I do have to do that on an RDBMS. The document's fairly denormalized. It generally aligns with how your application users data. Normal single document reads and writes don't require transactions. This means fewer joins and fewer transactions. This ensures scales better horizontally. For couch base, we query on a bucket. So when you see the from there, that's on the bucket. Remember, it's more like a database than a table or a database instance than a table. What makes this a table is that we specify the type. These two queries do the same thing. One on an RDBMS, one on NoSQL. We do support, so keep in mind the differences. The table equals bucket plus type. You can have many different document types in a single bucket. The ANSI, there are ANSI joins and we do support other SQL conventions. But there are some, are no SQL, or are SQL dialects called in one QL or nickel. There are other sort of JSONisms basically like unnest and nest when dealing with embedded documents. Indexes are more important on couch base. There are other details that we're not gonna go into great amount of detail today, but there are other things to consider. But you can kind of get a taste for what this might work on. When you're creating an index meta, this meta keyword allows you to refer to the field names. When you look at the word GSI that stands for Global Secondary Index, couch base has other types of indexes. Again, gonna skip over some of those details today. A few more notes here. So changes within a document are automatically atomic. So what's that mean? We don't need a transaction if we're just doing a bunch of changes on a single document. This is why when we say that you don't really need transactions as often in a no SQL database, it's because you don't have to put together all the different tables together. Now, if we're just hosting our SQL database one for one, we are gonna have to put tables back together. We will need transactions. And if you look on the right, this is sort of standard Java code for doing transactions in an application. Changes to multiple documents without a transaction are not atomic. So if we are doing a query across several documents and then we're making changes across several documents, this is all not transactional unless we actually code in a transaction. Couch Base 65, the most recent release includes a transaction API allowing multi-document transactions. And we support both synchronous and asynchronous. So if you're a developer and you've started doing more event-based reactive sort of coding style, I put the synchronous code sample here in the slide because I think everyone can look at this and kind of understand it without understanding monads and all of that kind of stuff. But we do support the asynchronous version as well.  So we understand that that's basically the bucket slash type document is analogous to a table. So we understand that, but we've got our one for one hosted relational schema on our NoSQL database. Well, let's talk about what we do now to optimize things. So we did that performance, and maybe we wanna make some changes. We haven't met our performance requirements yet. What do we do? So first thing to consider is that joining and transactions joins and transactions are costly and that doesn't scale as well horizontally. Remember our relational databases designed to scale up on a single node really well, doesn't scale as well horizontally. NoSQL databases are designed to let you just add more nodes as you need more scale and in many cases more performance. Your well-designed application code is a guide. So I said well-designed, because if your application's designed poorly, well, it won't be a good guide for anything. But if person contains addresses, but looks up orders, well, that's a good hint that person contains addresses should address, should just be contained in the person document, but that you might look up orders as a second entity. So we might have an orders type and a person type. We can look at our application code and kind of realize this. One of the things when we're optimizing, we should think about is when we go to RDBMS, we do a lot of sort of transition. We flatten things out, we create more pieces out of it. And that sort of gives us an idea of how we've sort of de-optimized to meet the semantics of a relational database that are different from our application. When we look at our application and we look at a NoSQL database because it supports a put-together type, we don't have to do some of that work. So if we do less work, we've essentially optimized things if that makes any sense. So think of in terms of entities. In the scope of your application, does this entity have a lifecycle outside the thing that always references it? If you've come from a Java background and use Hibernate or another object relational mapping tool, you get the same sort of advice from your object relational mapping tool. And so that's a good sort of skill that sort of transitions from a developer perspective. Can you reduce the number of explicit transactions required? So when we're trying to optimize this thing, can we, should we put something together just because it reduces the number of joins and transactions? Sometimes the answer is, hey, we're gonna put this together into one document because it'll reduce the number of joins and transactions. And have you met your performance and scale requirements? So it may be fun to think, okay, I'm gonna make this perfect NoSQL schema kind of setup, but if you've finally met your performance and scale requirement, well, you can stop. I guess that's generally the best advice for any sort of optimization scale effort is to have a performance and scale requirement in mind. I need to meet this many transactions per second. I need to make these things this fast. Once you meet that, you can stop because you can always find more optimizations and how do you need to stop it? So have a metric in mind. So when we wanna reduce joins and transactions, if we look at our setup, on one hand, I've got, on the left, I've got sort of this relational setup here. So I'm doing a transaction. I'm updating the savings table. I'm setting some stuff. I'm updating the checking table. I'm setting some stuff. I've got two different tables here. On the right-hand side, on the NoSQL side, I put these things together into one document. This is not a very realistic example, but it gives you sort of an idea. I put these into one sub document, balance and savings, checking, and so on. So I'm able to do one update and I don't need to have a transaction because I just do two updates on the same document. This reduces the number of transactions I need. This is when you're optimizing, you need to think, okay, well, could I just combine these things so that I don't need a transaction? I don't need a join. If you're a DBA, come from a DBA background, one of the first things you look at when developers send you a query is, is it complex? Can I simplify this query? Can I reduce the number of joins? Can I make that a less complicated setup? And generally that's one of your first sort of operating optimization plans. That's the same sort of idea here. We're just looking at the object model.  Couchbase is a partner with Irwin and Irwin supports Couchbase. So if you are taking a relational model and you're trying to host it on a NoSQL database, Irwin's actually a pretty good place to start. So you might consider using Irwin to sort of reverse engineer your relational model if you don't already have it in Irwin, into a logical model and then use Irwin to manage your physical model on Couchbase. So something to consider when you do this. So look at your logical model, look at what is your object in this? So one option we have is to embed days. So if we look at our overall setup, we've got an order, an order has things in it. It has quantities of those things. We can put those together into a document. So for us, we took order, we embedded pay type which embeds product. We can also do embedding and referencing because, hey, our products sort of have their own life cycle that's independent of the order. So we may embed the ID of the product but store the quantity as part of the order. So we've got order, it has items, it references product just like you would reference in a relational database but we've got the quantity embedded in here that might be in a relational database. We'd have a join table in the middle that would map order, we'd have order, order items and that would reference our product type. In this case, we can embed basically that order items table if you will. So in a NoSQL database, you can have both embedded objects but you can also have arrays. On the left hand side, I've got phones and I've got home. On the right hand side, I've got phones but it's an array of objects. And each one has a number of types and what have you. I could have an additional, I'd have a comma after the right-handed bird bracket if you will and I'd have a comma there and then another open and close for the next phone. That wouldn't fit on one slide now, would it? All right, so what should we conclude? Well, migrate first, optimize later, reduces risk while increases performance and scale over just hosting it. Joins, multi-document transactions are more costly and distributed architecture. So expect a one-to-one migration if we just host and just change our SQL queries as little as possible, change our object models as little as possible. We could expect it to be slower on more hardware. Embed documents as an optimization. The queries can be converted noting couch space and JSON differences. So you're still gonna use your SQL skills but you'll have to make some changes for the dialect and because JSON is different than a flat row. Asset transactions are available but they're not as needed as often when we optimize our schema because we don't have to, when we have orders and order items and product, we've got three tables basically. We've got to have a transaction just to map between orders and order items. If the order items are basically embedded in the order then we don't have to have a transaction just to update it. Finally, have some performance and scale goals in mind. This is the part people skip. They're like, well, we never measured it. We'll measure it, figure out what you need. Come up with a goal, even if it's a straw man goal at first and once you meet that stop, if that works good enough for your customers and what have you, then you're done, stick with it. So that's what I have. Thank you guys. Any questions? Andrew, thank you so much for this great presentation just to answer the most commonly asked questions or if you have questions for Andrew, please feel free to submit them in the bottom right hand corner of your screen and to answer the most commonly asked questions. Just a reminder, I will send a follow-up email by end of date Thursday with links to the slides and links to the recording of the session to all registrants. So diving in here, Andrew, are you indicating that we can use something like CouchBase to replace the SQL database for highly transactional things like banking? I thought you need ACID in such a situation. Yes, and CouchBase supports ACID. So because CouchBase supports ACID, CouchBase supports SQL, you can use CouchBase as a source of truth and in a highly transactional application. Would you make your ProcessingFasterRDB queries store code and optimization? How do you really compare and do you have benchmarks? We do actually have some benchmarks on our website. So if you go to our website or search for Alterose CouchBase, we have benchmarks against, I believe it is MongoDB, which is another NoSQL database. DataStacks Cassandra, which is another type of NoSQL database as a column family. And I think MySQL, just so that you can get a taste. Obviously, the license for the two most popular enterprise NoSQL databases prohibit you from releasing public benchmarks, I believe. So you can find those to kind of get an idea. But again, this is like, if I'm doing a single join with a short number of rows and what have you, and I don't have a lot of scale and I don't have a lot of data, well, that probably will perform better on a single server, on a single computer, on a single CPU, on a single disk. Once I have scale and performance requirements, then that won't perform as well on a single server, on a single CPU, on a single disk. So they're gonna be like, okay, well, I just did this simple query. Well, yeah, that's gonna perform better on a flat file with grep, honestly. But when you get a more complex, when you get a query that's going across lots more data, or you've got a query that's, or you've got a lot of queries, and you require more scale and what have you, then our requirements are different. So in short, it'll perform better at scale on a NoSQL database. It'll perform worse at low-end kind of scale, with the exception of E-value lookups are usually in memory on Couchbase. And do you recommend migration to NoSQL for all types of business needs based on your use cases? So there's a lot of different reasons people are migrating to NoSQL databases that go beyond the traditional. So I'm gonna make a qualified yes, meaning, okay, do you need to migrate your Microsoft Access database that has 10 rows in it and you're the only user? Well, probably not. But if you're looking at your license fees going out, going crazy, you're looking at putting out more and more changes to your applications, you're looking for higher reliability. These might be reasons, the size just performance and scale that you might consider migrating to a NoSQL database because you can have the same sort, you can have, you don't need Golden Gate and Data Guard and all of these sort of complex solutions that add on and add on the license fee as a multiplier. You can have that architecturally out of the box. Moreover, if you're doing mobile applications and you need resilience, sometimes that's because you've got lots of different locations and they need to be able to sync up to something or you may have lots of users with mobile phones and they need to be able to work and collaborate on something and then sync up. You need a platform that supports that. So I'm gonna make a qualified yes. There's always a, gee, if you're doing grandma there's a dress book and there's one, two, three users to it and performance doesn't matter, reliability doesn't matter, scale doesn't matter and it's never changing and you've got money to spend on Oracle or whatever then leave it in place. Once some of those things aren't true then it's probably time to consider a NoSQL database. And we're getting a lot of specific questions on install requirements of scale limits. I assume you have links to that that we can include in the follow-up email and make sure how... Yeah, absolutely. So I think the biggest sort of install requirement that I do wanna cover is generally speaking for something that's not just developer on your laptop, you're gonna want three nodes minimum and there's various reasons for that from a clustering and reliability standpoint but that would be sort of your minimum installed with the three nodes. You can obviously just install it on your laptop right now if you go to couchbase.com and download it. But really that's sort of the minimum, very minimum setup. But yeah, I'll send you guys links and other resources in the follow-up. I love it. Yeah, suit those my way and I'll make sure I get that out and to that everybody. And because one has to specify a bucket as well as a type in order to select from a quote unquote table does that mean that different buckets could host different data and that one can miss data if one selects the wrong bucket? So that's probably not a super common problem. The real setup here for the bucket you should think of as basically a database instance. So could you miss a table if you selected the wrong database instance? So if you did select from orders with the app sign on the old Oracle sort of notation from orders at some database instance and it was the wrong database instance. Yes, you could miss that too. So it's the same sort of problem. So yes, but that's probably not a problem that most people have. And you mentioned Irwin is a modeling tool that integrates with Cachebase. Are there any other tools as well? Yeah, we have other things that you can use and I'll send that in the follow-up. Irwin is sort of the one I've used since gosh since going, oh wow, I'm feeling old over 20 years and it's probably the most common but I can send you guys some follow-up but Irwin is sort of the most sort of famous modeling tool so it came to mind. And can we get a NoSQL catalog into a metadata management tool and then could we draw a lineage tree? I will have to ask someone else that. I'm afraid that that doesn't map to my knowledge. No worries, what's the largest database that Cachebase can support? So we have, I can't go into super customer specifics but we have customers that have in the thousands of nodes and what have you. If you look on our website, you'll find some use cases from major airlines to companies like AmEx and what have you but there's not a theoretical limit that you're likely to hit in that some of the largest companies in the world with some of the largest data volumes in the world are using Cachebase. In fact, whether you know it or not, you're already a Cachebase customer because it's virtually impossible that you wouldn't be using Cachebase and some of the applications and companies that you do business with today. I love it. And can you speak a little more about how Cachebase enables shared nothing architecture? Sure. So Cachebase, each node, well, first off, it's the clients get an updated map whenever they communicate with Cachebase. So every time they make a call, okay, the first call fails because that node went down it fails over the next node. That node then gives us an updated map of who it can talk to, so smart clients. You can also do this with a load balancer and hit the restful services if you need to. So first off, the smart clients basically keep the initial topology map or get an updated initial topology map in order to hit an active node. When a node goes down, then Cachebase automatically rebalances the sharding basically. So when Cachebase supports auto sharding, so whenever you add data, it automatically assigns it to at least two nodes. This is all configurable, of course, but at least two nodes. So there's at least two copies of the data at all time. If a node goes down, then that gets rebalanced. When the client talks to a node, that node automatically knows where to communicate in order to get the client the data that it needs if it talks to the wrong, quote unquote, node. There is no basically master election as there is with other databases. At all times, you have this sort of updated map. It hits memory, so it's a memory-first architecture. So things are written to disk as sort of a right behind Cache. And that's basically the crux of how it works. There's a lot more sort of innate detail in the devils and the details and what add you. Oh, it's also basically asynchronous by default. I love it. There's so many great questions going on here. And if you have a question for Andrew, please feel free to submit it in the bottom right-hand corner of your screen in the Q&A section. And do all major cloud platforms support CouchBase? Yeah, so you can deploy CouchBase on any of the major cloud platforms and people have and are using CouchBase today on all the major cloud platforms. A lot of companies choose CouchBase for their multi-cloud architecture because a lot of companies want to avoid having a single cloud provider or they have reliability, data sovereignty and global requirements that are not met by a single provider. So absolutely. Additionally, if you want, you can use, you can, although it just was announced and released in beta and the general availability will be later in the year, you can sign up for the beta for CouchBase cloud and that'll let you have a fully managed cloud-based NoSQL database. One of the things that's sort of interesting about CouchBase cloud is if you use a different cloud or as a service database, they upcharge you for your underlying cloud provider.  So they upcharge you for Amazon. So you, they buy Amazon and resell it to you at an upcharge. For CouchBase, it's bring your own cloud. Sort of like bring your own deer, right? But bring your own cloud. So you can provide your own cloud infrastructure, negotiate your own deal with Amazon. CouchBase just charges you for the use of the database rather than upcharging for that. And that's big if you're working for a big company because they generally have a cost center a lot of our clients are significantly bigger than all of the NoSQL vendors combined, right? So if your market cap or sort of your IT spend is bigger than even a public NoSQL vendors market cap then you can guess that you're getting a better deal from Amazon on your underlying infrastructure as a service charge. So you wanna negotiate your own deal with Amazon for the IaaS and then you want to just pay for the NoSQL database. And that's what CouchBase Cloud offers you. And there's a lot of different variations of this question, Andrew. With your customers, are you seeing a blended model with relational and NoSQL or are your customers majority of your customers moving exclusively to NoSQL? It's a mix of both. I wouldn't say that most companies are like, okay, I'm gonna rip out my relational database and immediately move it all to NoSQL. There are customers that are doing that. They generally do it piecemeal. They do it as the applications being updated as opposed to an application that's just sitting there with no new users and not a significant uptake in users or requirements or what have you. But there are companies that as a strategy including a large financial company are consolidating on CouchBase. I would say the vast majority have both and probably the vast majority have of large, since our customers tend to be larger vendors or larger companies tend to have everything. But they're running their mission critical sort of applications on CouchBase and yes, they also have relational databases. So combination of blended, some are actually migrating to CouchBase as a strategic sort of initiative. And what do you do for end user reporting? Is there an ETL tool or is there a replicator for transaction by transaction roll off? So there are a lot of ETL tools that will work with CouchBase and there's a large ecosystem that'll work with CouchBase. For that, I'm gonna kind of roll you to our website on the list of partners and what have you and you can look up through a pretty large set of partners and tools and whatever that work with CouchBase. Because we support SQL, I mean, you can use Tableau if you want to, you can use Excel if you want to as far as querying and doing reports and analysis and that sort of thing. So you've got quite a bit of that or just sort of monitoring and what have you. CouchBase has a pretty nice built-in administration module that you can look at the pretty screens and ops monitoring whatever on our web tool. And Andrew, what's the number one reason you find customers moving from relational to non-relational? So number one reason is that they need a performance, they've got a performance and scale requirement that they're not meeting. I would say the number two reason is cost. I'd say probably tied for number two or possibly rising to number two is reliability and the rate of change. It's easier to ring to change a schema, to move a new version of application on CouchBase than it is to relational database without having to restart things without having to rebuild a schema or rebuild various things that require you to take down the cluster. So we're seeing sort of the one, two, three would be, hey, I can't mean my performance and scale requirements, two, I don't want to pay this kind of licensing fees when you start having lots of nodes and needing reliability, what have you. And then sort of the third is that I can't meet my application change requirements and meet my reliability requirements with my existing database technology. And are there any scenarios, Andrew, where CouchBase is not recommended? Oh yeah, there's plenty. While CouchBase does, the first that comes to mind is, while CouchBase does have an embedded NPP, this is for like real-time analytics. It's not to go source against petabytes of data or do something where you'd need a materialized indimensional cube, right? So this is, there are fewer scenarios where people are doing that. Sometimes they're doing less good analytics that are faster, and that's where CouchBase may be appropriate where it might not have been in the past, but any place where you've got to do this indimensional cube and do this large projection and what have you will by means, Teradata and Ateza, whatever, those are your friends, but maybe they're not your friends, but they're the devil you need, right? But in the case of, but there are a lot more scenarios where CouchBase is appropriate, particularly in the operational space. I'd say if you've got, again, a small, not changing that often application with a small number of users and it works and you don't need high reliability, high scale, high performance or whatever, then why would you migrate that to anything, right? Leave it on MySQL or on Microsoft Access or what have you, but it's really when you start having more requirements and more reliability, more performance, all that kind of stuff that you're really going to start looking at making a change. It's a clarification from earlier. A benefit of normalization is reduced space usage and space usage is not an issue in NoSQL because of NoSQL's architecture. I'd say it's not an issue because disk got cheap. So yeah, we're gonna waste way more space. If what you're trying to do is store your database and as few megabytes as possible, then the NoSQL architecture is not going to be as efficient for that. I just say that's not a goal the way it once was. So you're trading something off, right? You could have your database in a tiny amount of space. Instead, you've decided that you want your database to perform and scale to a larger number of servers and be more reliable. So yeah, we're gonna waste a little more space there. There are updates coming in our future roadmap that may reduce that a little bit, but that can't be your top goal. If that's your top goal, that this may not be the technology for you.  But yeah, I would say that if saving space is your most important goal, this probably won't be your favorite architecture. And is a denormalized database more difficult to enhance with new entities and attributes? No, quite the opposite, because you can just add a new document immediately, you don't have to do a create table statements and any of that kind of stuff. You can add a new field, you can add a new embedded document, an array, whatever to your document without doing any kind of schema updates, whatever you just go. So I would say exactly the opposite, that's part of why people have been choosing these sorts of databases. Andrew, I love it. And there's a lot of great questions here. A lot of these questions, I'll get links for, there's some really super, again on the size and the install and we'll get those links out to everybody. Andrew, thank you. Yeah, I'm sorry, I actually can't see the questions. The console crashed my console with the questions and it crashed shortly after the presentation began. Oh gosh, no worries, no worries at all. I will get you the additional questions that and to you and the team, I really just, the remaining questions require links and information that you guys have available on your website and the technical details for the installs and such and such. But Andrew, thank you so much for this fantastic presentation and thanks to our attendees for being so engaged in everything we do and all the great questions, we really appreciate it. Again, just a reminder, I will send a follow-up email by end of day Thursday with links to the slides and the recording along with the additional information that you all are asking for and we'll get that out to you. And I hope everybody has a great day and stay safe out there. Andrew, thank you so much. Yeah. Thank you.


Hi, everybody. Nice to see you guys once again. My name is Andrea Terowski. If you have not been watching me on YouTube or seen any of my social media content, it's nice to meet you officially online. So let me talk about the VIP package and the express package because I do understand you probably have questions about them after going through all of the board exam prep kind of introduction information. So the express package, that is your condensed package of board exam prep. So you have full access for two months, but only two months and it's condensed. So it's more for people who have already been studying. You've been preparing for months, but you just want to make sure you have all the right information because you're about to take the board exam. There are topic materials. There are some case studies and there are mock exams, but in a condensed version. So you would log into your express board exam prep academy and you will see it all right there for you. You will also see teaching videos where I teach you that topic as well. Now it's typically updated once a year, but it's not updated every few months like the VIP board exam prep academy. So the VIP board exam prep academy is just that. It's the VIP package. So you get full access to everything right away. It is updated. You will get new materials per module per week and you will get a teaching video. You will get PDFs on that topic, new mock exams and new case studies every single week. It's meant to be studied over 12 weeks, but don't worry. I have lots of students who sign up and they say, well, Andrea, I have five months to prepare. How is that going to work? Or I have two months to prepare. That's okay. Everything is uploaded right away inside the course. So you could do two weeks at a time if you wanted to. You could do two modules at a time, three modules, four modules, or if you even want to jump ahead and do six modules, you can do that. So I make it very, very easy. In the VIP board exam prep academy, you just get so much more. There are more mock exams, more case studies and it's updated on a regular basis. Plus you also get interactive quizzes. So you can go online and take your mock exams and you pick your question and it's going to tell you, sorry, you pick your answer and it's going to tell you right away if you get the answer wrong or not. And then you can keep taking that and it's going to take your score at the end. So it's a great way to really see if you know your material or not. And also in the VIP package, you get full access to our members only Facebook groups. So you can network with other students and I have more mock exams and more case studies and more weekly videos inside the Facebook group as well. So you're always preparing.  So I do still maintain a 99.7% success rate helping students pass the board exam both in dental assisting and dental hygiene. So I know I can help you guys pass. I've been teaching tutoring since 2005. I've been doing this for a while and I love what I do. This is my full-time job. I teach students. I'm a mobile dental hygienist as well on the side. So I do still see patients. So I remain active in the field but also very active teaching and tutoring. So please let me know if you guys have any questions. You can send me an email anytime and you can also chat with me through the website. If I'm not at my desk, you can leave me a message through that chat feature on the website when I'm away and I will get back to you as soon as I come back. So thank you guys. I hope you learned a lot and I hope to see you inside the course.


Okay can you all see that now? Yes we can. Perfect, brilliant thank you. Okay so hi thanks for the intro. My name is Katie, I'm a postdoc working at the Marine Biological Association in the UK doing work on marine heatwaves and I'm just going to talk to you a little bit today about some of the work we're doing. Okay I'm going to give a little bit of background on marine heatwaves because I'm not sure what background everyone has and how much they know so essentially as we all know the oceans are getting warmer and in line with that we're also seeing heatwaves in the ocean just like we see on land. So there are essentially discrete periods of anomalously warm water that are occurring in addition to background climate change. So if you see this this global map on the side this just gives you an example of notable marine heatwaves that have occurred over the last 25 years and you can see they can occur anyway in any sea any ocean and we get these big blobs of warmer than average water where sometimes the water is increasing to temperatures as much as five degrees above the background temperatures for the time of year in location. So the most commonly used definition of a marine heatwave is a period of five or more days where water temperatures are warmer than the 90th percentile based on the 30-year historical climatology for the time of year and the location. So if you have a look at the figure on the left hand side at the bottom you can see a blue line which is your average temperature for a time of location over the last 30 years. The green line above it gives your 90th percentile threshold over that time and then the black line is your today's temperature and you can see if the back if the black line goes above the green line and then we start to get heat spikes and if it goes above that 90th percentile threshold for five days or more then this becomes what we classify as a marine heatwave at this point and just like many other things in line with global warming marine heatwaves are becoming increasingly common and increasingly increasing intensity as we go through time. So within any event the category of the heatwave can vary from moderate to extreme as the difference in the threshold temperature and the recorded temperature varies. So you have your average temperature and then above that you have your threshold and once you pass that threshold you have a moderate marine heatwave. Now if you take that difference between the average and the threshold and you go to two times that height you get a strong heatwave and then they become incrementally more intense so you get then severe and then extreme and just to give you an idea of how that could vary on the right hand side here you can see a marine heatwave that happened off the coast of Western Australia in 2011. Now you can see with both spatially and with time the heatwave varies from moderate to extreme if you look at the different colours and different areas of the coast were impacted over time with different levels of severity of heatwave. And just to kind of drive home some of the impacts that heatwaves can have and why we're looking at them. If you look at the two pictures above so A and B just on the right hand side there. A gives you an example of a lush kelp forest that was found off this coast of Australia and has been since time began not then but you know since as long as people can remember and this one particular heatwave event caused a 100 kilometre range contraction of the seaweed of this kelp and B the picture on the side is what's left now. So we have these turf algeas very low level turf algeas which don't have the same three dimensional structures and more than a decade later none of the kelp has recovered and we still just have these turf algeas there. Now today I'm going to talk to you about what we've been doing focusing on marine heatwaves of a strength and strong and above and the reason we look at these is because this is where we start to see the more significant impacts of marine heatwaves. We also focus on summer months rather than winter because organisms are living close to their thermal maxima so again we see larger impacts of marine heatwaves in the summer months so this is generally where our research focuses at this point. So what do we know about marine heatwaves so far? We know the impacts can be significant so we can get mass mortality and we can have huge shifts in habitats in relation to them and if you have a look here all these white boxes on this very simple figure here just give you an idea of the global expanse of impacts we've seen that affects foundation species all over the world. Now the work we're doing currently is looking at foundation species but I'll come back into that in a minute but yeah this is just to show you that this is where we've seen broad impacts of marine heatwaves all over the world and this isn't we don't just see impacts sorry again we also know that the impacts we also see huge impact in relation to ecosystem services so this figure is very busy and I don't expect you to take too much away from it but all it's telling you is that globally marine heatwaves have impacted provisioning services regulating services habitat services and cultural services everywhere and in that way the majority of large marine heatwave events start to impact us as humans and this is generally why we care at the end of the day. So just to put some of that into monetary values just to give you an idea of the kind of extent of impacts these heatwaves can have around 2016-2017 there was a large marine heatwave off the coast of southern Chile which caused harmful algal blooms. Now these harmful algal blooms led to huge mortalities in the farmed fish down there which led to a loss of 800 million US dollars in export just for that one season alone and similarly in 2010 the marine heatwaves in Southeast Asia caused huge bleaching mass bleaching of coral reefs and the loss to tourism because people were less interested in visiting bleached reefs was somewhere in the value of 50 to 75,000 for local operators around Southeast Asia so we're seeing huge, huge impacts of these marine heatwaves. So we wanted to start building an understanding of how marine heatwaves of different magnitudes impact different species to see if we could start to see the relationship between some of the more major marine heatwave metrics and foundation species. So we were looking at things like how long do marine heatwaves last, how intense are they, what's the highest temperature we reach and we're looking at how they then impact our foundation species. So we're focusing on foundation species because the impacts of these species are disproportionately high compared to many other species in the environment. In any marine environment the foundation species are essentially the identity of the environment and if they disappear the entire community changes. So this is where we're focusing right now and the work we're doing is looking at kelp forests, seagrass beds, non coral habitat forming invertebrates like these Gorgonians and coral reefs. So to start to investigate how marine heatwaves are impacting these areas, the first thing we did was identify time series data sets for these four different groups of foundation species where we could find long time, long series of data where we had either changes in densities of kelp or seagrass or bleaching events or mass mortality events occurring all over the place. We then explored for every single location that we had data for, we explored marine heatwaves that had occurred in those areas and as I said before we're looking for strong summer marine heatwaves and then once we've done this we cross referenced all of our data for every location we had and we pulled up every overlap where we had time series data and a marine heatwave events occurring and had a look at what we could see there. Okay, so just to give you an idea, kind of a better idea of what we did. This is an example from off the coast of California and it just gives you an idea of how we kind of worked through our data. So at the top you can see 40 years worth of climatology. There's a package called Heatwave R if anyone is using our and wants to look at marine heatwaves which will pull up for any location your climatology. So we pulled up for the coast of California, this particular area, the climatology over the past 40 years and you can see the red arrows above just indicate where there was a strong summer marine heatwave and then underneath you have your change in kelp density. So this is macrocystis, one of the main kelp species over there and you get your density on the side and you got about 20 years worth of data. So here you can see we've got two different periods where there were summer marine heatwaves and we have kelp data from before to after so we can start to look to see how the kelp has changed during a marine heatwave event. So kind of in closer context we for each event we then took a couple of years before if we could and looked at the natural variability and then we looked at a year or so after and we looked at the difference between the averages of density of kelp in this example from before to after and kind of took that as a response to the marine heatwave. So we did the same for seagrass and for bleaching and mass mortality events. We just looked at the occurrence of bleaching or occurrence of mass mortality events that they'll start after or just after marine heatwaves. Okay, so what will we find? Well, first of all we managed to find data from 1,365 sites globally where there were heatwaves occurring strong summer heatwaves that occurred at the same point as we had data for and these across 72 ecoregions. So it's a good global kind of expanse that we've got here. If you have a look on the right hand side, these were the different data sets that I used that were used. They are all freely available, big intense data sets, but if any of you want to know any more about them, my email address at the bottom of all the slides. So feel free to contact me and I can either give you a list of them or any other information you are interested in in these. Okay, once we found all this data, the first thing we did was kind of a blanket analysis to determine how foundation species were impacted within each of these. So this again is an example for kelp here and it just gives you an idea of how kelp responded in different ecoregions to marine heatwaves. We averaged the data from sites within each ecoregion and then looked at changes in kelp from before to after, as I said. So you can see here for every location on average we're seeing about 50 to 60 percent of the time we're seeing a loss of kelp in relation to marine heatwaves, which is no great shocker. We all know they're damaging to the environment. However, what was a bit of a surprise is around 20 percent of the time we're instead seeing gains in relation to strong summer marine heatwaves. So based on this, we then had a look at how species responded across their point in range, across their entire distributional range. So we separated species by trailing edge mid and leading range edges and looked at how marine heatwaves affected species in each of these distribution points. Okay, so first of all for our animals, so for the corals and the invertebrates, we found at the warmer range edge, we were seeing larger impacts than at the cooler range edge. So more coral was being bleached, more invertebrate mass mortality events happened. And similarly for our plants, we again see the same kind of thing where at the warmer range edge, we're seeing a real decline, but at the warmer cooler range edge, we're actually seeing an increase on average in response to these strong summer heatwaves. So it just goes to show anything you're doing, the importance of taking into account where the species is in its point in range and how that affects its thermal capacity. Okay, so after this blanket analysis, we wanted to start looking within eco regions as to see if we could determine in any eco regions, if there were relationships between the marine heatwave characteristics and the responses we're seeing. We narrowed this down by eco regions just to try and take out some of that thermal effect from species ranges. So this, doing this, we kind of also limited our data to anything that had 10 or more data points in, which left us with 29 eco regions globally, and we ran general linear models to compare our responses to the marine heatwave characteristics that we were looking at. So duration and maximum intensity, the average intensity, the cumulative intensity, which takes into account the length of time and how warm the water is over that length of time and the maximum absolute temperature. So what do we find? We found pretty much for every foundation species in eco region, there were significant relationships between marine heatwave characteristics and some of the impacts we're seeing. Sorry, I thought my daughter was coming in then. So just to show you some of the impacts that we saw, this is just for the Caribbean. So this gives you an idea for some of the coral reefs we were looking at in the Caribbean. And you can see that the two most common parameters for, and we found this across the board for coral reefs that kind of predict how a reef is going to respond during a marine heatwave is the maximum intensity of the heatwave and the maximum absolute temperature. So this just gives you a couple of examples. So these are many reefs off the coast of Florida, where we see a significant relationship. And although coral bleaching remained quite low level across the heatwaves, we see this significant increase in bleaching with increasing maximum absolute temperature. And then in the greater Antilles on the right hand side, it's a much more clear cut pattern. And once the temperature gets above 30 degrees, we're seeing huge levels of bleaching. And this was kind of the more common pattern we saw across the board. Most most reef areas we found above 30 degrees, we're seeing this huge increase in bleaching. So just to show you our overall findings, again, this is a very busy figure, but this gives you our 29 eco regions where we have compared the different marine heatwave characteristics to different foundation species responses. You can see in general we've got these significant relationships linking the foundation species with these different marine heatwave metrics. And if we can start to scale this up, we can start to understand how marine heatwave impacts of foundation species in different parts of the world and begin to predict how these marine heatwaves will or how foundation species will respond to marine heatwaves in the future, which hopefully will start to to guide management. So as we all know, nothing's ever quite as clear cut as we'd like. So we're working towards developing the understanding of these relationships between marine heatwave characteristics and foundation species. And as marine heatwave forecasts improve, we'll be able to hopefully guide better management decisions and start to predict better what's going to happen in the future. But doing this also provides us with some hypotheses to start testing in the lab. So this is one thing we're doing back at home is running lab experiments to support what we're seeing in the field, just to try and understand if we can see the same same kind of responses. Yeah, and that's that's the kind of direction we're going in right now. And just to finish, this is the lovely team that I had to spend lots of my time working with. So a thanks to them. A thanks for all of you guys for listening. And with that, I'll take any questions.


So, as we're saying about the LIFE-AIR Initiative for Deputation and Resilience, one of the components that LIFE-AIR is looking at is a learning component, which we thought would be good to talk about in this CBA because CBA is a community of practice about learning that has been going on for a long time. And as the LDC Chair mentioned that the opening in the last CBA, the telenoid that happened informed the compact that has been signed by the LDCs and the donors. And some of the things that the LDC wants to promote is regional and national learning and capturing good practice, peer support within the LDC countries and learning from good practice happening there and those informing good practice. But then learning has been going on for adaptation. Most of the times we don't learn from the good things that are happening. And sometimes we don't even learn from failure. So we continue failing and failing at not learning from it. And the LIFE-AIR Initiative focuses on working in different ways, business and usual ways of doing things. And that also relates to learning. And the learning agenda will also follow the principles that have been signed by the LDCs and the international community, making sure that there is equal partnership and a shared platform, trust, transparency, accountability, focus on the whole of society approach. The reason why the LDC governments are coming in here where we have civil society organizations, private sector, academia and others. And then also working at the pace of individual LDCs, individual groups, men, women, youth and others to make sure that we are focusing on those that are most vulnerable to the impacts of climate change and they are not left behind. So the learning agenda will also focus on most of those principles that are being promoted within LIFE-AIR. So this session is both sharing from the LDCs. We have two of the front-runner countries, the government of Uganda and the Gambia will be sharing on how learning has been happening in their countries, what has worked and what has not worked and what they would want to see happening in LIFE-AIR to make sure that the business as usual approaches are being transformed into more effective learning. We also have, we shall also have experience sharing from the List of Opt Countries initiative, not List of Opt Countries University's Concertium on Climate Change, which is also a long-term initiative of the LDC group. And the purpose is to strengthen capabilities of LDCs to address adaptation and resilience within their countries. They'll also share their learning experiences and that will happen in the breakout sessions, which have been organized in smaller groups to be able to give us time to really discuss and share in those groups. And before we do that, we wanted to get everybody's views on some questions, which we are going to do through Mentimeter, because sometimes the time is short and we may not be able to share. So I request Michael to take us into that and look at the questions and then get general responses. Then we'll go into the breakout groups and have more deeper discussions with learning that comes out from the three that are going to share with us. So one of the questions that we wanted to look at is why learning is important for climate change adaptation and resilience programs. It may sound obvious. We always talk about mail and we have lots of frameworks, but the air is always silent in this. So we wanted to hear from you why you think learning is important for climate change adaptation and resilience programs. Michael, so we request you to go to either your phones or computers and type in www.menti.com and the code is 757097. Then we do the voting and see what you think about that question. So there's a link as well in the chat box and the codes. So responses are coming in, understanding what works, what does not, and adjusting best one experience on the ground. So no programs are perfect. We need to learn as we go, introducing updated technologies, no magic bullet looking at complex challenges, dialogue with communities of practice, understanding success and failure factors, empowering innovation in addressing climate change issues. So we don't invest and not learn what is already failing, improving to listen to communities which is really important, listening to communities in a structured way and responding to with adaptation programs, listening to people, replicating lessons, improving knowledge. Power, it's a foundation for great programming, scaling up and replicating. So we could look at the effective change. So we are closing in a few seconds, then we look at the second question. Okay, so let's look at the second one. The second question we wanted to look at is, Michael, if you could put up the second one, the most effective approaches and methods that have worked for you or your institutions. We've been learning for many years and we know what works and what doesn't, whether we do it or not. So what has worked for you or your institution, what you think, either methods or approaches that have worked for you or your institutions. Participatory approach, co-generated and peer learning. Participatory rather than formal rate learning. Social learning, participatory approaches. Listening, don't go somewhere with preconceived ideas, that's right. Listening to communities, stories of change and narratives. Investing in personal and communication relationships over time. Indeed, that's why life here is long term because adaptation doesn't happen in a short time, participatory, identifying and working with poorest and marginalized communities. I'm not seeing anymore. Are we still voting or are we finished? Michael, I'm seeing the top of the slide. Can it go down? The third one, so if we are done with that, Michael, we could go to the third. The third question, we wanted to look at what are the business as usual learning approaches that life air should avoid. The business as usual that we should avoid and use more effective approaches or things that have been tried before and have not worked. We've done lots of evaluations and these should actually inform what really works and what doesn't. So if you could share some of those, the business as usual approaches that we really need to avoid in this program, in terms of approaches, methods. Yeah, Mentimeter hasn't moved on yet, so Michael. Michael, apologies, I will quickly read it out. That's the second one, right? Yeah, so that's the third. The business as usual approaches that life air needs to avoid. As practitioners that have been there for years, definitely. We know things we've done and have not worked, so it would be great to hear. Log frames, one off training, project based mindset, short termism, I like the word. Top down approaches. Focusing on short outputs, not long term capabilities and competences. One way provision of information from scientific and powerful institutions, providing services and goods, centralized systems of developing learning materials, lack of ownership. Assumptions that donors know exactly what the vulnerable communities needs. Need to empower communities, voice, not respecting local knowledge, top down approaches. So, yes, so thank you for those responses. So now what is going to happen? One, this to be as informal as possible to enable us have conversation, honest conversation at that. So we're going to go into five groups. And these groups will have experience sharing from the life air from China countries and the lack, half of it. And then also hearing from you, discussing what they presented and any other additions you might have from your learning. Those are three groups. One will be led by Uganda, the Gambia and one the lack. Then we will have two extra groups that will give us external perspectives. These are not government. They're not learning institutions, but these are civil society and others. Communities of practice that have been at it for a long time, to give us honest feedback on what we really need to do. And one of them will be led by Fiona Palsy and another one by Grace Inezza. And we are discussing three questions which will be shown in the breakouts about learning in the countries, the approaches and then real recommendations on what life air should take forward. So Michael, if you could take us to the groups and please don't forget to click on the join that your group where you'll be taken into. We are taking 30 minutes, 15 minutes for those presenting and 15 minutes for discussion. And in the two groups, it's really like discussing the three questions and giving us that feedback that life air is looking for. So if you have any any issues or comments or whatever, don't forget to use the chat box as we go through this session. That would be valuable for us as well. So Michael, can you take us to the groups, please? One second, yes. I'll just have to apparently my pre-assigned didn't work. So I'll just have to do it here. So I'll open them slowly one by one. One thing I would like to ask of people in the breakout groups is that whoever is the main session host in there presses record as soon as you enter just so that we can make sure that we capture all the insights from each session. So if you have any questions about to do that, please flag with me and I'll enter the breakout room and help you out. But yes, I will be opening the rooms shortly. So welcome back from the breakout discussions. More people coming back. I hope they were useful and I hope you had good discussions. And we are so many, to be honest, we expected to have fewer people, but we are so many and we had 65 on the waiting list, which is difficult to manage. So I'm going to request that if there's something inspiring or something that you forgot or something you learned in your group to type it in the chat box because we don't have time to come back and share what we've discussed. I think that will be one way of knowing what happened in one group that you were not able to listen to. So we are coming back to a plenary session where we are going to hear from the decentralized climate finance alliance. This is an alliance that has been working so hard on making sure that adaptation funds reach the local level. And IID has been supporting that work with the governments and civil society in Kenya, Tanzania, Senegal, Mali, and they have lots of experience. And out of that learning, some governments like the Kenya government is planning to scale up this innovation in the whole country with lots of support from different donors and local governments and communities. So we have a team that has been working on this and have experience to share on what learning they've generated through working with government, through working with civil society and communities. They're going to talk to us on what decentralized climate finance alliance has learned about participatory learning, which kept coming up in the sentiment and the discussions and the challenges and opportunities for mainstreaming learning into national processes and institutions. Since we are looking at Lefair, which is really like LDC led for LDC countries. So we'll start with the Esperanza Karaho, who works with the adaptation consortium in Kenya. She's a program officer on devolving, devolving, scaling out devolved climate finance and they are working on scaling on the whole country to share her experiences with us on the two questions that we discussed. So Esperanza, you have the floor. OK. Hi. Thank you, Tracy. I'll just I'll try to be quick. So on what the DCF Alliance has learned about participatory planning. First, I'd like to mention that in Kenya, we've had quite a number of approaches. We've used participatory vulnerability capacity assessments. We've used also participatory resource mapping. We've used focus group discussions. So what we have learned about the participatory planning is that number one, we are able to complement baseline information for measuring impacts. Like, for example, the investments that were made, the water ponds. We've done investments on water ponds, wells and sand dunes and rears, which have benefited the community. And some of the films we've had for telling the transformative impacts we can be able to tell from where we started or the way to as a baseline from where we started to the transformative impacts we've had. We also learned that participatory learning empowers communities. This is because the community, usually the members of the community, communities usually come together to work towards a common goal, which in this case is to prioritize an investment that they think is going to that they believe is going to build some resilience towards climate change and also to help them adapt to climate change. So that means that they are able to come and agree towards and set on what they want to work to to build resilience. We've also learned that participatory learning brings partners and volunteers and government, county and national, into closer contact with communities. Basically, meaning that there's working with people rather than working for them. Now, a good example is that it enhances public participation and county planning. You know, during the training of the planning committees, to transfer our skills and they're able to to be able to actually go sit down again with the community for them to come up with proposals and then all the way to approvals at the county level. So basically the partners all work together. And then we also learned that participatory learning strengthens participation of beneficiaries in decision making. In the in the policy and legislation of the climate finance devolved climate finance in Kenya, we've ensured that the structures are inclusive, where we have the marginalized groups sitting in them. The women, the people living with disabilities and representatives of the youth and also community based organizations and NGOs and faith based and another thing on strengthening participation is that they also get to know what budget they have so that they are able to plan with it. And meaning basically that we use the bottom up approach and the members of the community are able to agree, like, build a consensus on the goal they want to achieve. Then we've also learned that participatory learning also takes an integrated approach, especially in terms of vulnerability, we are able to tap into the community's knowledge of the local needs and risks so that that is through the PBCS in order to build resilience into the project that the community identifies. And then we've also learned there's value for money and sustainability. Value for money because it optimizes the relevance and appropriateness of the action because as I mentioned, this is a project that has been chosen by the community, meaning it's a bottom up approach and also sustainability because once the project is completed, it's usually handed back to the community for them to run the investment and they are also able now to protect the investment because they are the ones who chose it and they know the importance and they are also the ones who are running it. Then we've also learned that there's needs when we talk about the participatory resource mapping. There's need for accuracy in mapping and at least somewhere like Garisa and DiCiolo, they were able to digitize the perception maps, which makes it easier for policies, the policymakers and planners to make decisions and also for appropriate skill mapping to capture things like settlement and ecosystem scale for planning. In terms of challenges, the challenges and opportunities for mainstreaming learning into national process and institution, the main challenge we had especially on mal, that is monitoring, evolution and learning, was that the Kenya, the Kenya climate adaptation, monitoring, evolution and learning framework is weak, but it is something we were working on, I think last month, we went out to the fields to actually capacity build the counties for them to be able, we actually identified, what are they called, a core indicators, so that they can they can be able to monitor, evaluate and learn. So the main challenge we had there was that we had scattered and unconsolidated climate change indicators and monitoring with varying agencies, some sitting in national agencies, some sitting in other departments, and also the indicators were not well structured, you'd find that one indicator would measure more variables. So in terms of the opportunities, last month, we set out to strengthen the climate change monitoring, evaluation and learning system by integrating the county, integrated monitoring, evaluation and learning system together to the national one, so that the indicators that are usually sector and sector, sector role, there's a level for reporting that is consistent from the county, it feeds into the national, then of course use of harmonized tools like baseline tools and data collection. Yeah, then also another opportunity for learning is institutionalizing the devolved climate finance at the Kenyan National Government, and we also had last month, we also held an informal learning events, basically to promote a coherent approach to the management of devolved climate finance, and the quality standards and the best practices based on our pilot from the five pilot counties where devolved climate finance was piloted in Kenya. So it's drawing on that expertise in the pilot counties that we are scaling out now to the other 42 counties in Kenya to cover the entire country. Another opportunity in learning is that probably we have a communication plan tailored to the needs of different stakeholders at different stages, especially when we are scaling out so that we can develop and deliver ensuring that we have coherence and application of common standards. So yeah, that is what I put together. Thank you very much, Esperanza. And to note that the DCF Alliance cuts across the countries that I'm implementing DCF and brings in a rich experience and context from different countries. And Le Faire is interested in this because some of the countries want to use the same mechanism. So this learning is really very useful for us. So next we'll hear from Mamadou, who works with ID AFRIK in Senegal, and he's a technical advisor on climate change and decentralization. So Mamadou, please, you have the floor. Thank you a lot. Thank you. And good morning, everybody. I'm very happy to be with you today, talking about the DCF experience in Senegal, in terms of learning lessons, challenges and opportunities. I start to talk about the DCF. For us, it is a research action and advocacy project to promote an experiment approach to decentralize funding for adaptation. We think that DCF is more than a financing mechanism. It is a model of climate change governance. And for the first question about what has the DCF already learned about participatory learning, we have four main lessons. And the first is that the community-based planning. The DCF project served us that planning should come from the community. If we want to address the right problem related to adaptation on climate change, we have to give voice to the base community that are best able to identify the most relevant adaptation option in order to avoid maladaptation. The second lesson is we have an inclusive approach. An inclusive approach with gender, economic sectors, territories and partnerships. The third lesson is we have to make the focus on public goods for financing priorities. The first lesson now is that we have to improve flexibility, iteration for a successful model. That includes these dimensions, economical, social, environmental and political. So for the first lesson, for the first question, this is the answer we have talked about. And for the second question, what are the challenges and opportunities for mainstreaming learning into national process and institutions? Here are our responses. First, the first challenge for us is how to operationalize the DCF mechanism. And for this, we have to improve adaptation local committee. We have to help the communities to control locally the source. We have to plan and we do to make tools for climate information. And the first is to make a tracking adaptation and measuring development attempt for the evaluation. In the past, on this project, we elaborate the DCF guide. We elaborate also two other guides on local planning, including climate change and also the climate finance access guide. So it is our first challenge. The second challenge is how to scale up the DCF initiative at national and international level. It's still be a very recent challenge for us. And how to make adaptation committees autonomous and sustainable. Also, how to convert central government to integrate climate change into planning budgets and also make budgets of local communities sensitive to climate change. We have also to make the focus on public goods and funding resiliency. In terms of opportunities, it is to work with diverse stakeholders. Diverse stakeholders at the local level, like adaptation local committee, like regional development agencies, like local authorities and others. And for the national level, we have to work with the national platform supported by the ministry of local authorities. We have to make advocacy, energy and fund-raising. And for international level, we are on networks like DCF Alliance, like LifeEye, we are on UNFCCC COP, and we are working in other networks on climate change. And it is to share, sharing lessons, not capitalization, global partnerships. And for opportunities, we think that climate change is increasingly taken into account in local planning in Senegal. And we also see that the financial capacities of the local authorities are improved. And also we have the support to the National Territorial Planning Guide, integrating cross-cutting dimensions, like climate change, like gender, like migration, like national. So we work also to disseminate the metrological information in partnership with the agencies of metro. And also setting up the national support planning for decentralized funding for resilience. And in the term of learned lessons, we learned that today's local authorities is a relevant case for climate. The training of local actors is necessary for climate change integration. I think we talk about we talk about this, the training of local authorities. And the participation, the commitment and empowerment of local actors are fundamental to build the resilience. And the path of descent realization allows to channel funds towards climate vulnerable communities. And at the end, public institutions ensures appropriation, sustainability, and scaling up innovation. It is what I have to share with you. And I'm waiting for your question, if you have. Thank you. Thank you, Tracy. Tracy, you are mute. Sorry. Thanks, Mamadou, for that sharing, bringing in communities or stakeholders, governments, and life here really is looking at the whole of society approach and whole of governments. How do different government sectors come into supporting adaptation, which is important? And the last speaker is Samson Siliu, who works with the local government training Institute in Tanzania. And they're also part of the DCF Alliance. And these in-country institutions have been useful in supporting local government capabilities. And national governments as well, which is also in line with the discussions we had from the list of countries, universities, consortium on climate change, having internal capacities to support countries than depending on consultants and external facilitators. Samson, you have the floor. Yeah, thank you very much, Tracy. Good morning and good afternoon, everyone. Yeah, my name is Samson Siliu, as we have been introduced. From the first questions, which says what has the decentralized climate finance alliance learned about Spatio Lenny? From the Tanzania side, the DCF mechanism, which has been piloted here, has shown very good advantages to the local communities. It has been a good approach, especially in streaming the climate change adaptation planning, and also finance systems to the local communities that we've been working in also to the government. As Tanzania side, we've been able to learn through the DCF Alliance to learn the different new mechanisms from our colleagues, even Kenya, Mali and Senegal, and from other members. And this has been a very good platform to sharing this kind of knowledge and networking and also building capabilities and also sharing the technical expertise from different members. We have seen the different advantages to the local communities that we've been working here in Tanzania, such as understanding the local community initiatives that has been indigenous knowledge. Since we've been able to include them from the local leaders from the national leaders who are the policy makers and also planners so that they have been able to integrate the climate change and gender issues on climate change or on the government planning issues. We have seen the different advantages we've been learning here, such as sustainability of these kind of projects, projects which have been initiated by the local communities, because of the participatory way of participatory way of mechanism which has been used here in Tanzania. And also the value for money, we've been seeing it. And which shows us these projects are very sensitive. And in the near future, we'll leave this to Sanford Kwai, who is working with the ministry. From the ministry, he will add some few things here. And also from the second questions, what has been the challenges and opportunities in streaming into international processes and institutions, where this has been a good opportunity. Let me start from the opportunities. We have a number of institutions that have been working in the DCIF and the DCIF Alliance as well. Such as this community, non-government organizations, non-government organizations, the government institutions such as the such as learning institutions, academic institutions. We have been working together. It has been a good opportunity since that we have been able to plan from the local communities, into the high level, which is a national level to the police makers and also planners. These are very good opportunities that is opening and is opening for us to be able to widen our projects. And also big metric. And also a good approach, such as TAMDI and MEA. The challenges that have been seen. So we have very few challenges here, such as financial, financial lack of finance in expanding. We want to expand it to almost all the regions here in Tanzania. But the funding issues has been a little bit challenging. And my colleague here, Sanford Kwai from the ministry, will be able to add some few points here. And also lacking in building to politicians and the technocrats has not been, we have not been able enough to train almost all of them because of the financial lack of funding on this financial side. My colleague Sanford Kwai is online. He will add some things. Okay, thank you, Samson. Sanford, do you have something to add to Samson's point? Sanquai? Okay, so thank you, Samson, for those insights. If Sanquai has something to add, he might add or not. I don't think he's online. He's not online. Okay, thank you so much for your insightful lessons. There are some questions that came up in the chat box that I was able to pick. There's a question for Esperanza. Is the participatory approach also used in decision making and adapting the program to learning and results? If so, how has this worked? And do you have any tips? Then another one to Mama do, how do you follow participatory planning, but also provide ideas for innovation or expert advice to avoid multi-adaptation? So any responses to those in the few minutes? Esperanza, you're on mute? Or Mama do if you're ready? If you have any response to the questions raised? I think that Esperanza is here. Okay, for the patient of maladaptation and how to avoid it, we think that if we give the floor to the local communities, they are able to show how they adapt their activities on climate change. It is not something new. They live with this challenge, challenges with on many years, and they developed, they already developed some kinds of adaptation. So what we have to do here is to support them to improve this local adaptation. Because if we come on the field with technical technical solutions, it can be not adapted, adapted to the community or socially accepted. So I think that if we give the floor, if we plan on the bottom up approach, with real participation, we can have good adaptation, and it can help us to avoid maladaptation. Thanks. Thank you, Mama do. Esperanza, do you want to add anything? I think Esperanza said in the chat that her internet's a bit slow at the moment. So she's going to answer questions in the chat. That's okay, Tracy. Okay, great. Thank you. So we have a few minutes to close. And I want to thank you all, but I want to make a comment on some of the issues that came up about learning. Learning is not between experts and local communities. I think that comment was from Fiona Pasi, which is very good. There's learning between experts and communities. There's social learning that happens between communities within themselves and the Indigenous knowledge and practices that they have, giving examples of COVID response and other responses to risks that happen. There's peer learning. And Lefair is also really focusing on that learning between countries and other institutions that are focusing on the same mechanisms, and then co-designing initiatives between practitioners and taking co-people with the local communities, trying to understand what their needs are, that they may not be able to articulate in the taking away that we want to see them. And those areas that also the lack wants to focus on how do we learn with communities, take up their issues and document them into what government wants to see to influence policy and practice, which is all very important. So from my part, I want to thank all the speakers, Muhammad Semambu from Uganda from Toronto Country, Alfa Jalo from the Gambia, another from Toronto Country, Dr. Yafa from the LAC who has represented the LAC initiative, and Fiona Pasi for facilitating our outside experts advice, and Gracie Neza as well, plus all the facilitators and our volunteers, Maiko and Alex. Thank you so much. So I want to hand over to Claire Shakia, Director, Clemachan Group, IID, to give us a few closing remarks. And I want to thank everybody for creating this time to share with us, and also add that the LDC Initiative for Adaptation and Resilience is focusing on creating a community of practice to share government learning. Government has been doing a lot of things, a lot of learning, which doesn't get out. But this is the time to share between countries, regions and bring the practice to the global level. So this is very useful as a conversation starter for that community of practice. Claire, over to you, please. Thank you very much, Tracy. I'm standing in for Gebru Jemba, who's the LDC technical lead for this initiative. And I mean, I'm looking forward to hearing from the different groups. I hope we can share that on the WAVA platform, perhaps. And I see that some, some organizations are offering some of their approaches, which perhaps also we can share with each other on WAVA, because this is the start for the LDC groups under this initiative for effective adaptation resilience, trying to get the learning working at all the different levels that it needs to, whether that's between communities and local governments and social movements at the local level, or whether it's between local and national, or whether it's between the different countries in the front runners, or between the front runners, and the rest of the LDC group, and indeed, other countries that are interested to learn what are effective approaches to supporting action on the ground that communities really can drive. So I've just learned so much today, and I'm looking forward to talking all through with Gebru. I'm sure he's very sorry that not to be part of this. But if you have other thoughts, please do feed them in, because we are in the process of trying to rapidly understand what has worked in different contexts, what really enables that sort of buy into the learning as well, not just the learning happening, but that it's shared in ways that are effective and influential, and really start to drive different ways of working to get to this business unusual that we've been talking about. So just to add my thanks to Tracy, to all of those that have contributed, I've really enjoyed the discussion myself, and look forward to further interaction and getting hearing more of what you think does work, but also let us know what doesn't work, because we definitely don't want to do that. Thank you. Thank you. Thank you, everyone.