Hi everyone, and thank you for joining this session. I'm work as senior data engineer. Seven years of experience specialize in building high performance scalable data solutions that connect innovation. With the three year world impact, my focus is on our systems that can handle massive data volumes without compromising speed, reliability, or flexibility. I'm passionate about exploring emerging technologies that push the limits of what's possible in modern data infrastructure. That's exactly what brings us here today to explore how rust pair with data mesh principles can completely reshape the way we think about large scale production grade. Datas in this session we'll talk about how Rust can transform modern datas by applying data mesh principles. We'll explore why enterprises need to move away from monolithic systems, how rusts memory, safety, and fearless concurrency enabled high performance, reliable pipelines, and real world implementations. We'll also look at benchmarks, advanced risk patterns, strategies for integrating with cloud infrastructure to build scalable future ready data platforms. Let's get started. We are all aware of how rapidly data is growing. It's exponential. The problem is that monolithic architectures can't keep up anymore and we end up with bottleneck, any inefficiencies. That's why moving to distributor system isn't just an option. It's essential. Distributor systems give us the scalability and flexibility we need to manage these massive data demands. This is where data mesh comes in a decentralized approach. That puts data ownership in the hands of domain experts, improving collaboration, reducing dependencies, and scaling more effectively. So first, memory safety. RUS ownership model means no data races, and let us write the robust concurrent processing code without sacrificing comments. Then there are zero cost fractions, which let us write expressive high level code without any runtime penalty. Finally, fearless concurrency trust makes parallel processing safe and reliable. Which is absolutely critical for high throughput data pipelines. When we talk about performance, rust truly stands out. One of the biggest gains I've seen is in data processing speed. Using rust as synchronous runtime, we can achieve a through 10 times faster class sync compared to many traditional solutions. That's not just a small optimization. That's a difference between a job finishing in hours versus minutes. Another huge advantage is building memory Safe pipelines because of Rust Strong type system. A lot of the bugs that would normally show up at runtime, things like null point or errors or data mismatches simply can't happen. This means we production incidents. And much more reliable systems. And then there is stream processing. If you have worked with JVM based solutions before, you will know they can be powerful, but also heavy in realtime analytics. Rest outperforms them. Delivery low latency and higher throughput. That means we can process streams of events faster, react to changes more quickly and do it all without consuming excessive resources. In short, rest gives us speed, reliability, and efficiency all at one. And those are the three pillars you need for any modern high performance data pipeline. Let's move from theory into practice when we are actually building high performance data systems in rust, there are few tools and techniques that really make a difference. First, a synchronous crossing with Tokyo. Tokyo is a high performance Synchron runtime that lets us handle massive amounts of concurrent work without blocking threats unnecessarily. In a data pipeline, this means we can process incoming requests, transform data and push results downstream, all in parallel without choking the system. Next, zero copy serialization with survey in many systems, serialization and D, serialization of bottlenecks because you are constantly copying data in and out of memory structures. With ser, you can add those extra copies, which not only improves the performance, but also reduces memory treasure. This is especially powerful when you're transforming and transmitting huge data sets. And finally, column of data operations with Apache Arrow. Arrow. It's a store and process data in a column of fiber. Which is far more efficient from analytical. In my experience, combining rust with Arrow means we can run high speed analytics on large data sets that otherwise. More traditional row based tourist systems. When you put all of this together as in processing zero copy serialization and columnal data operations, you get a data pipeline that is extremely fast and resource efficient. This is where rust really starts to shine in real world scenarios. So now let's talk about building. Infrastructure. Infrastructure that supports these high performance pipelines for me starts with the high throughput Kafka consumers. I use the Kafka RS Library, which is a re around the battle tested Kafka SEA Library. It gives us reliable, low latency consumption, xk, which is perfect for ingesting millions of. Even per second without dropping messages. Then I focus on memory efficient transformations, rust, iterators, and zero allocation patterns are game changers. Here, instead of creating temporary objects that collect of memory and put further on the garbage collector, which rust doesn't even need downstream transformations directly keeping memory usage minimal and performance high. Finally, I think about robust HTTP APIs when we need to expose data or services to other systems. I like using frameworks like Axiom along with top middleware. This combination let me build APIs that are fast, secure, and easy to integrate into wider data ecosystem. Plus, since these APIs are written in rest, they inherit the same performance and safety guarantees as the rest of the pipeline. All of these pieces, Kafka, consumers efficient transformation and strong APAs work together to create an infrastructure that isn't just fast, but also resilient. It's scalable for long term growth performance. Me. So whenever I talk about rust in data systems, one of the first questions I get is, how does it actually perform compared to Python, Java, or scaler? So I make it a point to run real world benchmarks, not just synthetic tests. I take actual data workloads, that kind you would see in production and compare implementations across these languages every time. Rust, consistently shows lower latency, higher throughput, and better resource efficiency. The tools I use for these benchmarks are well established and reliable. The measure, not just speed, but also memory usage, startup times, and consistency under load. This isn't about proving that one language is better in all cases, but about having accurate performance insight so I can make informed architectural righteous, and the impact is huge. These benchmarks directly influence system design. For example, if certain part of a pipeline is performance critical, real time event processing our data transformations on massive data sets. Rest often becomes the clear choice by contrast. For less time sensitive components, another language might make sense for speed of development. Ecosystem support. In short, benchmarking keeps us honest. It ensures that we are picking the right tool for the job, and more often than not, it highlights where REST can give us a major advantage in high performance data processing. Now we have covered performance in infrastructure. I want to share some advanced rust patterns that I found especially valuable when building large scale distributed data system. First active model implementations. I like using the active model to isolate domains within a distributor system. Each actor has its own state. It communicates through message passing. Which not only improves modularity, but also makes the system much easier to scale in rust. This model is both safe and efficient. Thanks to the languages concurrency guarantees Second custom derived macros in big projects, I often need to enforce certain rules consistently across the code base, like validating the incoming data structures with custom device macros. I can automate that. It means every developer on the team gets validation for free without having to remember to write it manually. Indeed, it keeps the code base clean and uniform. And finally, web assembly for security. This is where Ru Flexible really shines. I can compile parts of the rest application into web assembly modules and then run them in a sandbox environment. That's perfect, Phil. Crossing data across different domains and even different organization because the isolation gives me an extra layer of security without giving up performance. These advanced patterns, actors, macros, and web assembly, gives us the ability to build system that are not just fast, but also maintainable, secure, and ready to evolve as requirements change for me. A high performance data system isn't complete unless it integrates seamlessly with the cloud environment. It's deployed. Rest makes this easier than you might think. Start with seamless cloud integration. Whether I'm deploying on Azure, a Ws, gcp, REST applications can hook into existing cloud services with minimal overhead. I can connect to managed databases, storage systems, message queues, just as easily as I can work with on. Components. That means they get the flexibility to run in hybrid or multi-cloud setups without rewriting large parts of the system. Next, look at microservices architecture. Rust is good for building small focused services that do one job extremely well. When each service is independent, I can scale them individually based on demand scaling the ingestion layer without touching the analytics layer, for example. This is approach also makes maintenance easier because the teams can work on different services without stepping on each other source. And finally, data contracts. Enforcement. This is one of the. Russ hidden superpowers by defining strict data structures and when forcing them at compiled time. I can guarantee that data exchange between services always in the correct format. This eliminates a whole category of runtime errors and reduces the need for defensive coding when I combine this capabilities cloud integration. Microservices and compiling data contracts. I get a cloud ready architecture that's sufficient, safe, and designed for the long haul. As we wrap up, I want to bring the big picture back into focus for me. Rust Isal, just another programming language. It's a game changer for how we approach data architectures. Combining rest with the data mesh principles, I've seen firsthand how we can build infrastructures that are both highly scalable, rock solid, and reliability. Looking ahead, the rest ecosystem is evolving fast. We are seeing improvements in asynchronized. New libraries for data processing and even more seamless integrations with cloud platforms. All of these developments means the rest is only going to get more capable for handling the toughest data challenges. My takeaway for you is this, if you are working on system that need to handle. Massive data volumes, low latency and high reliability. Rust should be on your short list. Gives you performance with sacrificing safety, without sacrificing safety and scalability without the typical complexity overhead. And I will leave you with a call to action. Explore rest. Try it on a small, high impact part of your pipeline. See how it performs in your environment. You might be surprised at just how much of a difference it makes. This concludes my presentation. Thank you for your time and attention. Have a good day.