Hey everyone, thank you for joining me today. So today we're going to talk about something exciting. How cloud native analytics is revolutionizing the way we use big data to drive real time product insights. So in this session, we'll explore why traditional analytics fall short, how cloud native approaches make a difference, and how this transformation can drive business success. So let's dive in. So now that we've set the stage, let's take a quick look at what we'll be covering today. So we'll start by discussing the Big Data Challenge. as in why businesses struggle with traditional analytics. Then we'll define cloud native analytics and how it enables real time insights. Next, we'll explore why real time analytics matters, followed by the key technologies that make it possible. Then I'll share a real world example of Uber, showing how they use cloud native analytics to drive business success. Then we'll go through an implementation roadmap for businesses, outlining the steps to get started. We'll also touch on challenges and considerations, because no transformation is without obstacles. Finally, we'll wrap up by looking at future trends and summarizing the key takeaways. So we are in an era where data is being generated at an unimaginable scale. Every day, businesses, devices, and users generate about 328 million terabytes of data. What's the challenge? Traditional analytics and their systems weren't designed for the speed of volume. They often rely on batch processing, meaning decisions are based on historical rather than real time data. So this gap between data generation and actionable insights creates a real business problem. And that's delayed decision making. Missed opportunities and inefficiencies. So cloud native analytics is a game changer. Unlike traditional systems that struggle with scaling, cloud native solutions are designed from the ground up to handle massive, dynamic workloads. They leverage serverless computing, meaning you and only you use resources when needed, making it cost effective. They use containers and microservices, making them modular and scalable. And most importantly, they enable real time streaming analytics, allowing businesses to act on data instantly rather than waiting hours or even days. So why does real time analytics matter so much? Because in today's world, being fast is being competitive. Take Netflix or Spotify. Real time analytics drive their personalized recommendations. And think about fraud detection. Banks need to act on suspicious transactions immediately, and not hours later. Even product teams benefit from this. So companies using real time insights can tweak and improve their offerings based on live user feedback. So to make real time analytics possible, we need the right technology stack firstly. That's number one, event streaming tools like Kafka or AWS Kinesis. are able to process massive amounts of live data. Number two, serverless computing ensures resources scale automatically, which optimizes cost and performance. Number three is data lakes and warehouses, which act as the foundation storing vast amounts of data, which is structured or could be unstructured. And number four is AI and ML. And they really supercharge analytics by turning raw data into predictive insights. So whether it's forecasting demand, identifying risks, or personalizing experiences, AI and ML are where you look at. So now let's take a look at Uber. I think this is a perfect example of real time analytics in action. Uber doesn't just use static pricing. Instead, they analyze demand in real time. And if a major event occurs Ends and demand quickly spikes. Uber's analytics engine instantly adjusts pricing to balance supply and demand. They use Kafka to stream millions of riot requests per second and Flink to process them in real time. Finally, they use Databricks for deeper insights. And what's the result? Smarter driver allocation, reduced wait times, and optimized pricing. This is all thanks to cloud native analytics. So how can businesses actually implement real time analytics in a practical sense? This is not about ripping and replacing old systems. It's about strategically modernizing. Step one is to identify where your data is coming from. Customer interactions, IoT devices, transaction logs are just a few sources. But there could be many more. Step two. Step two is implement real time ingestion tools like Kafka or Kinesis. Step three is to store data in a scalable cloud data lake, so no more rigid databases anymore. Step four is to use serverless computing and AI to process this data efficiently. And step five is to surface insights through real time dashboards and alerts, so teams can act immediately. Now, of course, no transformation is without challenges. So what are the top challenges we can see? Number one is latency issues can arise if data pipelines aren't well optimized. Number two is cost management can be very critical. So real time analytics can be expensive if not, architectured efficiently. And number three is data security and compliance could be a big concern, especially in highly regulated industries like banking, finance, and healthcare. But with careful planning, businesses can navigate these challenges effectively. So looking ahead, we're going to see even more innovation in real time analytics. Edge computing will push real time analytics closer to the source, whether it's through IoT devices or smart sensors. AI driven automation will further reduce human intervention, and this will enable fully autonomous decision making. And multi cloud strategies will become the norm. Allowing businesses to be more resilient and avoid vendor lock in. So to wrap up, cloud native analytics isn't just a buzzword, it's a necessity for businesses looking to stay ahead. Organizations that embrace real time insights will outpace competitors, optimize their operations, and deliver superior customer experiences. Now it's the time to invest in the right technologies and start your real time transformation. That's my time. Thank you.