Hello everyone. I'm Ti pdi, pa, currently working at one of the leading multinational company as a lead software engineer. I completed my masters at the University of Akron, Ohio, and today I will be presenting at conference 42, incident management, 2025. Now, retail has always been about customer experience. Today, that experience is delivered almost entirely through technology, and that technology has to operate at a scale and speed that is unprecedented. Black Friday alone can generate over 20% of annual revenue in just a few days. That means if your systems fail, the consequences are immediate and long-term revenue losses, brand damage. Customer trust erosion. This is why Cloudnative approaches are no longer optional. They're essential. In this talk, we will explore how retailers are using Cloudnative architecture, not just to survive these high stakes events, but to thrive achieving agility, resilience, and cost efficiency at the same time. By the end, I want you to leave with practical ideas. That you can apply in your own organizations. Here is how I have structured our journey. Today, we will begin with the digital retail evolution. What's driving this transformation and why cloud native is the inevitable answer. Then we will explore the architecture foundations such as. Auto scaling microservices and resilient patterns that are at the core of surviving peak demand. Next, we will look at the real world impact concrete case studies on cost efficiency, personalization, and inventory management. After that, we will dive into incident management because resiliency is only real. If you can detect and recover from failures quickly. Finally, we will cover migration frameworks, especially the Strangler Big pattern, which shows how to modernize without risky Big bang Cutovers. So this agenda moves us from the big picture, why changes needed into the nuts and bolts of how to make it work, and finally into strategies. To future proof retail platforms, let's set the stage. Retail today operates in a high stakes digital arena. Events like Black Friday and Cyber Monday are no longer just busy days. They can account for more than 20% of annual revenue in a single weekend. That means one outage even for minutes, can wipe out millions of dollars. Customers now expect subsecond response times across all channels. Mobile web, in store kiosks. If the site is slow, they don't wait, they leave. Inventory visibility must be in real time. So if someone buys the last pair of shoes online. That's instantly reflected in the store system. Personalization isn't optional anymore. Customers expect tailored recommendations every time. And let's not forget compliance and security. A breach can cause existential damage. Retailers who can't deliver resilient experiences using peak traffic face, both immediate revenue impact and long-term brand erosion. So the message is clear. The retail battlefield is unforgiving and the only way forward is cloudnative. With that urgency in mind, let's explore the first foundation. Auto-scaling infrastructure. Traditionally retailers. Had to prepare for peak events by massively over provisioning infrastructure that is very traditional. To handle Black Friday, they might buy servers that were three to four times more than their normal demand, and those servers would sit idle most of the year. That's wasted money. Cloud native flips the model with elastic auto scaling. Resources expand and contract in real time. Predictive models forecast demand spikes while real time monitoring adjusts capacity Minute by minute, during Black Friday, traffic might spike 10 or 20 times. Auto scaling seamlessly adds servers to meet the load. When traffic drops back to normal, resources scale down automatically. What is the result? So the result is leading retailers see 50% infrastructure cost savings while still maintaining 99.99% uptime. It's efficiency and resilience hand in hand. This is why I call autoscaling the foundation of agility. Without it, retailers either waste millions or risk outages with it. They strike the perfect balance. But scaling infrastructure alone isn't enough. What happens when one service fails? That's where microservices come in. Monolithic systems are fragile. One bug in checkout could take down the entire site. Cloud native retailers avoid this by adopting microservices in this model. Each domain, like catalog cart, checkout payments, is its own independent service. They can scale individually, pale individually, and be updated independently. Retailers use resilience patterns such as circuit breakers to prevent cascading failures, bulkheads to isolate resources, back pressure to avoid overload. A synchronous communication to reduce tight coupling. The impact is powerful. Teams can deploy three to five, three to five times more frequently, and the blast radius of incidents is cut by 75%. This means retailers can innovate faster, recover faster, and maintain stability even when parts of the system fail. And this architecture doesn't just improve reliability. It enables advanced capabilities like real time personalization. Let's talk about personalization. We all experienced it. You browse for a product and the site instantly recommends something relevant. Today, that's not a nice to have. It's a baseline expectation. The challenge is that delivering personalization in real time across billions of sessions without slowing down performance is incredibly complex. You are running machine learning models, crunching data, and serving recommendations in milliseconds. Cloud native retailers solve this with microservices based machine learning platforms. Recommendation engines are isolated into independent services scale separately using GPUs for fast inference and paid with session specific caching. This means that even during peak traffic, the system can deliver personalized recommendations with sub 20 millisecond latency, and here is the impact. Retailers see 25 to 35% higher conversion rates. That's customers, not just browsing, but actually completing purchases, all without sacrificing the site performance. So personalization at scale proves a key point. Cloud native design doesn't just keep systems alive. It actually drives revenue growth, but to make personalization work. You also need something equally critical. The real time inventory. Visibility inventory is a nervous system of retail. If your system says an item is in stock, but the store says, shows that it's already sold out, you break trust instantly. Legacy systems relied on. Batch updates, meaning inventory might lag by 15 to 60 minutes. That is like one hour 15 to one hour. In an omnichannel world, that's unacceptable. Cloud native retailers move to an event driven architecture. The movement is sale have happens. An event is published into Kafka or Kinesis streams. Those updates flow into distributed caches like Redis across regions, and a graph ql. API layer ensures fast, consistent access. What's the result? Inventory visibility in under 200 milliseconds across both digital and physical channels. This reduces talk outs by 40%. Enables true omnichannel fulfillment. Buy online, pick up in store, return anywhere. Inventory is no longer a back office function. It's a realtime digital nervous system powered by cloud native design. But even with personalization and time, inventory failures are inevitable. The real question is how do you prevent them from cascading? Failures happen. What separates resilient systems from fragile ones is how they handle those failures in tightly coupled systems. A single slow or failing service can ripple through the entire platform. That's what leads to checkout crashes on Black Friday. Cloud native retailers design. For failure from day one, they use circuit breakers to stop endless retries on failing services, fallback mechanisms to serve, cashed or simplify data and rate limiting to control traffic surges. Here is a simple example. Imagine the product detail service slows down instead of showing an error page. The platform can serve cashed content or a simplified version of the page. The customer still completes their purchase revenue still flows, and the incident doesn't escalate. This is resilience in action keeping critical purchase parts open even during partial outages. And resilience isn't just about uptime, it's also about keeping your environment secure while moving fast. Now let's talk. Security retailers may deploy hundreds of times per day. Each deployment introduces risk, but in a cloud native world, security must be baked into the pipeline, not bolted on afterwards. Here is how leading retailers. Secure containers by using immutable images. Every image is scanned for vulnerabilities before deployment. Runtime protection. Behavioral analysis monitors live containers for anomalies, secret management. So say like credentials are rotated dynamically. Only exists temporarily the timeout in a certain time, like not producing long lived tokens or anything like that. Basically we need to produce like short-lived tokens and compliance automation, continuous checks for PCI and GDPR with automatic evidence generation. This approach allows retailers to stay compliant while deploying at scale. In other words, cloud native security pipelines let you move fast and safely without slowing innovation. But even with scaling resilience and security in place, incidents will still happen. So how do we detect and resolve them faster? Before cloud native practices, incident management was slow and painful. The average MTTD mean time to detect was over 45 minutes. The average MTTR mean time to recovery was over four hours. That's half a day of lost sales and frustrated customers. With cloud native and site reliability engineer practices, everything changes. Detection drops to under 60 seconds thanks to automated alerts and observability pipelines. Resolution times improve by 75 to 80% with cannery analysis. Automated rollbacks and playbooks for partial failures. The result is that incidents that used to cripple retailers for hours now get detected and resolved in minutes. This doesn't just protect uptime, it protects revenue, brand trust, and customer loyalty, and that brings us naturally to observability the backbone of rapid detection and response. Monitoring tells you when things are broken. Observability tells you why Cloud native observability combines metrics. That gives us like latency, throughput, error rates with anomaly detection. And also sometimes like metrics can include like how many. API API calls count, error count which I already mentioned. And other important thing is logs. Logs are structured with trace IDs so you can connect errors across services. And then finally the traces. So traces will give us full end-to-end visibility of a request through multiple microservices. But here is the game changer. Leading retailers align observability directly with business outcomes. They track metrics like cart abandonment, rate against service latency, or revenue impact. During incidents, they run synthetic shopping journeys to test checkout continuously. This means incident response isn't just about restoring servers, it's about restoring customer experience and revenue flow. So what about legacy systems? How do we bring them along without breaking everything? This is where the strangler fixed pattern comes in. Inspired by the tree that grows around its host and gradually replaces it. This pattern allows us to modernize without a risky big bang cutover. Here is how it works, and API gateway intercepts traffic and routes it to either the legacy system or new microservices. New services are built alongside the old, gradually traffic shifts to the new services as they prove reliable. Over time, the legacy system is strangled and quietly retired. This incremental approach means you deliver value, continuously, reduce risk, and never disrupt customers. Its evolution, not just revolution. And to make this possible, you need the right team structure to support it. Technology patterns only work if the organization supports them. Leading retailers adopt structures that accelerate both innovation and incident response. They use product align squads, cross-functional teams that own a business domain, and to end they rely on platform engineering teams that provide self-service infrastructure to developers. They embed, so they integrate SRE, like site reliability, engineering best practices within squads, guided by a central team, and they integrate security as code into CSCD pipelines. This structure leads to three to five times faster incident resolution because teams own their services and have the tools to act quickly. It also enables constant innovation. Because safety nets are built into the process. Now let's recap what we have learned today. So these are like four key messages to leave you with Cloud. Native is existential, not optional. Retailers see 50% cost reductions and higher resilience. Second one is designed for failure from the start. Circuit breakers, bulkheads, fallbacks, reserve revenue flow. Third one is observability must connect to business outcomes. That is tying metrics to key performance indicators like conversions and revenue. The fourth one and last is the migrate with evolution, not revolution. The strangler thick pattern lets you modernize safely and incrementally. If you remember nothing else from today, remember, just this cloud native retail is about survival and competitiveness. It's about delivering resilient experiences when it matters most. And with that, let's close. Thank you all for your time and attention today. Detail is in the middle of a massive transformation. Cloud native architecture is at the heart of it. I hope this session has given you some practical ideas, whether you're thinking about scaling systems for peak traffic, modernizing legacy platforms, or aligning observability with business impact. Thank you all once again.