Hello everyone. In this talk, we are going beyond rockets and rowers to explore the engine that quietly drives space missions which is data engineering. Space missions today aren't just sending satellites into orbit. They're sending back massive volumes of data. We are talking hundreds of gigabytes per day from just one mission. And it's not just about storing it, it's about collecting it efficiently, processing it in real time, and making it usable for scientists across the globe. Think of it this way. If rocket science is a body of the mission, the data engineering is the brain and nervous system that makes it all work together. Let's talk about the scale of the space. Data today space is vast, but so is the data it sensor. The ISS alone generates nearly a terabyte of data every single day. Imagining downloading every season of a streaming show every day. That's the scale we are talking about to handle this, they have built powerful distributed systems that split workloads across missions, almost like a factory assembly line. And what about the compression? It's like vacuum packing your clothes so you can fit a whole wardrobe into a carry on. With compression ratio as high as tennis to one, we make every bite and every bit of bad count. Imagine trying to stream Netflix with only dial up internet connection. That's what we would face without these innovations in space data. These numbers tell a story. Data engineering isn't just supporting space mission. It's powering them. So now let's talk about distributed data systems. Here's where it gets exciting, right? The distributed architectures, instead of one gen computer crunching all the data, we use a network of. Small computers working together, like a team of chefs in a kitchen, each preparing a part of the meal. These systems enhance mission, success, and scientific outcomes. Enable efficient data management across missions are built on a distributor architecture with parallel crossing across mini notes. Picture a relay race where data is passed across runners or processes to input throughput and reduce bottlenecks. These architectures allow space agencies to scale on demand handling up to one 50 simultaneous data requests from multiple missions. Next, we move on to advanced compression techniques. So space data travels a long way, and there's no room for bloated files on this type network. So we compress data smartly, not just to shrink it, but to preserve what matters. It's like organizing a cluttered closet, filter out what unnecessary, keep what's valuable, and arrange it so you can find it fast. From the moment data is captured, it goes through a series of smart steps, the initial cleanup, priority tagging, analysis, and long-term storage, all optimized for speed and scientific value. Now let's go a bit futuristic which is quantum computing. Imagine a normal computer as someone reading a Jane book one word at a time. A quantum computer reads a whole page at once. This leap could change how we plan missions, navigate deep space and even encrypt sensitive data, which is exponential Processing power like quantum computers can solve space algorithms much faster than classical systems. Enhancer encryption using quantum cryptography to secure spacecraft comments and data optimization problems. Solving complex challenges in mission planning and navigation. Think of a classical computing as checking every possible path through a maze one by one. Quantum computing explores all the paths at once. It's still early days, but the potential to revolutionize trajectory planning, resource allocation, and deep space analytics is massive. Now let's talk about edge processing for space applications. Have you ever used your phone's face recognition without internet? That's what the edge processing is. Similarly, today's spacecrafts are smart enough to do some of the data crunching onboard instead of sending every bit back to earth the filter and prioritize it themselves, saving bandwidth and reacting faster. With they have on onboard processing, initial filtering and compression, intelligent filtering prioritizing the most important data, autonomous decisions, making mission adjustments in real time optimized transmission, sending only what matters. It's like having a smart assistance on onboard the spacecraft, one that decides which photos to keep and which to delete before uploading them to the cloud. This says energy and bandwidth and boosts real time decision making. All at under five watts per node. Next let's talk about high performance computing architecture. Think of space missions as having a three type data brain. One is in orbit, small but tough processors that can handle radiation and code on earth. Jane Supercomputers for heavy duty analysis. And in between cloud platforms, connecting them for seamless coordination. It's like having your smartwatch, laptop, and data centers all working together in a sync. So now let's talk about real time data processing capabilities, like with less than 10 milliseconds latency spacecraft. No process and act on data faster than you can blink. Which has data acquisition sensors capture raw input. Processing data is evaluated in milliseconds autonomous action. The spacecraft makes realtime decisions and ground notification. Critical events are related to earth. This is vital for handling solar, flas, animal, or system fault, especially when waiting 20 minutes for a signal from me isn't an option. Let's talk about James Webb's Space Telescope, the golden child of a modern astronomy. It sends back about 40 gigs of data every day, capturing the most detailed images of space we have ever seen. But raw data alone isn't useful. The data goes through on onboard filtering, then to earth, where it's processed by layered pipelines to transform those light signals into breathtaking, imaginary and discoveries. So space data isn't just for scientists, it's a major driver of the global economy from GPS and weather tracking to agriculture and finance. Countless industries rely on space derived data to operate. In fact, satellite communications is a largest slice of a four 23 billion space economy. And what fuels that data engineering quietly running behind the scenes. So what's next? Where This is mainly for AI driven autonomous spacecraft that thinks and reactive smart as systems, a delayed tolerant interplanetary and internet to connect future mass spaces, quantum data centers for solving problems we can't even imagine today. And orbital com cloud computing where spacecraft collaborate by sharing compute and storage resources. We are talking about the cloud in literal space and it's closer than you think. To wrap it all up, as space gets more ambitious, so much of data systems, it's no longer just about launching rockets, it's about managing the data that comes back, making it actionable, accessible, and powerful. So thanks for joining me today. Let's continue to engineer the future of space one data packet at a time. Thank you.