 Hi, so thank you very much for having me today. I'm really excited to be back on stage really after two years of the pandemic. So as Gab said, I'm an industry practitioner, but I firmly believe as a technologist that technology is here to solve business problem. And we can talk technology for hours, but we can link that back to a business problem. We can show how technology matters for those organizations, how open source drives innovation. So my job at Databricks is to be able to bridge the gap between business and technology. So without getting into a kind of salesy pitch of Databricks, but just to give you an idea. So Databricks is the cloud data and AI company. We provision the right software, the right infrastructure, the right cloud framework for you to be able to spend less time on the plumbing, on the stitching, on the orchestrating, and more time on delivering some value from your data. All the way from SQL and MI and BI type of workloads to more AI, ML or complex type of workloads. And that drives innovation to multiple aspects across the entire ecosystem in financial services. And retail banking, gaining more insights around customer segmentation, about behavioral analytics, and fraud detection, about building those great models. But coming back to that concept of bringing business and technology together, it's not always about having the best possible AI model in the planet is being able to combine domain expertise and AI driven insights to operate those type of models faster to innovate. Risk management, when lots of risk managing framework were always backwards looking, process looking at what happened in the past. Moving more towards being able to describe, to predict, to prescribe using data and AI. All the way to alternative data, all those trends that we see, extracting insights from IoT devices, social media feeds, news articles, plenty of different insights that could be generated that would need the right framework, the right cloud infrastructure, the right AI strategy, the right data personas being able to collaborate on the same platform. Extracting those trading strategies, backtesting all your different models, and iterating faster. So that's great. But what prevents organization from innovating? Why is technology a problem here? So this is a typical kind of environment every large scale organization would be facing. You have a database on the side, you have a data mats, you have a data lake, you have some AI there, you have some real times, some trimming, some ETL sources, and underpinning all of those concepts are all those plethora of libraries and framework and vendor looking and proprietary formats. That creates silos, that hinders collaboration. The data that is accessible by a line of business is completely disparate from the data available for a data scientist. They speak different language and there are restraint and to specific technologies only used for a specific use case. So our data bricks, the way we look at this is can we start simplifying the data ecosystem, abstract the technical complexity of cloud, of AI, of all those concepts here, to be able to unify those different data personas. Even though you may not speak the same language, you still as a domain expert needs to sit down next to a data scientist, next to a data analyst, next to a quote, next to a data engineers and being able to collaborate on driving value from data. So we do that through a technology called Lakehouse that takes the best of a data lake, flexibility, the AI readiness with the data warehouse, that is more the governance, the structure, the discipline, the transparency, the lineage. In a simple open and collaborative environment. So across different clouds, abstracting that complexity to make a unified platform for all your data or your use case to enable that collaboration and everything at Databricks fundamentally is open source. Spark, we invented Spark, we made that open source. Delta Lake, what pours those Lakehouse is part of the Linux Foundation. MLflow, Koalas, all those technologies are open source. And I use here to drive that collaboration, to drive that opportunity of getting the best of what the community has to offer and managing that. So when we moved, when we joined Phinos, I looked at all the different projects and I found Legend. And for me, Legend, I looked at this, I read a few things and I was sure that it solved the exact same three business value. So technical problem, making it simple for users to be able to interact with their data. Easier for users to be able to map their business processes to data. It's open source, it's available. I started to look at the code and I started to understand where I could potentially contribute back. And therefore, collaborative. So the same three values make me think that clearly there is something here, some synergy. The idea is abstract in the complexity so that regardless of where your data is, you will be able to query the same, providing that same experience to users with the right engineering best practices and governance framework. So naturally, the first thing is let's just connect Databricks as a backend to a Legend front end. Databricks is a powerful engine, let's just connect that. But as you've seen before, it's not just a technology problem that we are trying to solve. When the data sits in various different places in the organization, it doesn't really matter if we just query a database somewhere. We have to make sure that the data that goes through all those transformation is following the exact same steps maintained by domain experts through the Legend framework. So you can define all your rules, your transformation, but actually we didn't want necessarily to connect Legend to Databricks right away. I wanted to make sure that we can learn all those domain models, those rules, those transformations, those semantic layer, and be able to not just simply ingest data and ask for an engineer to recall all those rules but programmatically infer those rules and make sure that the data going through this pipeline will be of correct schema, validated the right way. A currency code will be a valid currency code according to those rules. An interest rate will be in the same boundaries as defined by the domain analyst. The data will be quarantined for you to review and how we can automate that pipeline provisioning on the backend so that when we connect, now we've closed that loop. And this is really critical to drive innovation because it enables for continuous improvements. It enables for interactivity with your data. The data keep flowing in. You start going your data directly from the Legend interface. You start refining your models. You start building those rules and you see those rules continuously improved and implemented. That enables a full collaboration and that enables a joint point before all the governance framework, making sure that we don't just define rules. We manage to get those rules seamlessly implemented, followed, explained. So to summarize what we've done is we looked at the Legend stack to see whether or not Databricks has a powerful engine to create that data and offer that powerful engine to the hands of the domain experts, of the analyst. But we made sure that we could take the best practices of Legend in terms of governance coupled with the best practices of Databricks in terms of operation pipeline to improve that governance and reduce the amount of manual effort that you're gonna have to rewrite all those different ETL pipelines. Remove the number of technology required to operate that stack, but most importantly, connect business and technology. Even though we may not necessarily always be the same language, we can start working collaboratively on the same platform, on the same data, on the same rules. That's in my view as a technologist is what drives innovation. Continuous improvement, fast interactivity, and collaboration. So we are more than happy to answer any question that you may have. We have my colleagues out there at the table answering questions around different type of use cases, the work that we are doing, actively contributing with the fantastic team of Legend and the help of Finos and any other question about how to unlock this innovation in financial services. Thank you very much.