Welcome everyone. Today, I wanted to take you on a journey into the art of building long management stacking systems that combine modularity, scalability, and simplicity. I have learned that the most resilient systems often operate invisibly, allowing businesses to thrive without constant firefighting. Let's explore how to achieve this harmony. Back end architectures have come a long way. We started with simple, monolithic systems that were easy to understand but hard to scale. Today, we work with cloud native, distributed systems that serve global users in real time. However, this complexity often leads to systems requiring constant maintenance. How do we build systems that scale without becoming a burden? Modularity is the key. Microservices allow teams to build, deploy, and scale independently. However, the goal is not microservices for their own sake. Design around your business dominance. If your application doesn't need microservices, don't force it. Complexity can grow fast if we are not careful. Cloud providers offer managed services that handle infrastructure concerns like scaling, patching, and backups. Serverless computing takes this further by abstracting servers altogether. This allows developers to focus solely on business logic. Yet a serverless isn't a silver bullet. Hold starts and time limits can be constraints. Often a hybrid approach works best. In modern software design, event driven architectures have become a critical approach to building scalable, responsive, and resilient systems. Unlike traditional request response architectures, EDA enables services to communicate asynchronously, ensuring flexibility and efficiency in handling unpredictable workloads. One of the most significant advantages of event driven architectures is asynchronous communication. Instead of directly invoking other services and waiting for responses, the system generates and publishes events that interested consumers can process independently. This decoupling allows services to operate at their own pace without being blocked by slower components, leading to improved responsiveness and reliability. To facilitate this asynchronous event flow, systems use event blockers like Apache Kafka. AWS EventBridge and RapidMQ. These tools act as message intermediaries, ensuring that producers and consumers remain loosely coupled, which means that the services do not need to be aware of each other's existence, and the consumers can process events at their own speed. Without overwhelming upstream services, systems can evolve independently, making them more maintainable. For example, in an e commerce platform, an order placement event can trigger multiple downstream processes, like payment processing, inventory update, shipping initiation, customer notification, etc. And each of these events listens for the event and acts independently. Improving scalability and fault isolation. One of the biggest challenges in the modern architectures is high load scenarios. Event driven architectures naturally support backpressure, which is a mechanism that prevents the system from being overwhelmed by incoming requests. This is crucial for maintaining system stability under spike loads. Kafka and Sibila systems provide mechanisms like consumer lag monitoring and rate limiting, ensuring services do not process events faster than they can handle. Since consumers process messages independently, they can scale services horizontally by adding more consumers. to distribute the load dynamically. If a sudden surge in the traffic occurs, such as a flash sale on an e commerce website, additional consumers can be spun up automatically to handle the increased event flow. In today's digital landscape, the scalability is not just a feature, it's a necessity. The application must handle unpredictable traffic, growing user basis and fluctuating workloads by maintaining performance and reliability. The scalability ensures that a system can efficiently grow or shrink in response to demand without compromising speed or availability. A key principle of scalable architecture is designing stateless applications. So in a stateless system, each request is processed independently without relying on stored session data. Any instance of the application can handle any request and scaling horizontally, which means adding more instances, is straightforward as there is no dependency on local state storage. For example, in a microservices based e commerce platform, the stateless services like authentication, order processing, and inventory management can be deployed across multiple instances. Since, they do not store session specific data, they can be dynamically scaled up or down based on traffic volume. Manage user sessions. In a stateless architecture, the developers use external storage solutions such as, or for session cing, database backend storage for persistent user, and jwt, which means the J to for. or stateless authentication, where the session data is embedded in the token itself. By ensuring that the applications remain stateless, we eliminate bottlenecks and make scaling seamless. As traffic increases, distributing incoming requests efficiently across multiple instances is crucial. Load balancers act as traffic directors, ensuring that no single server is overwhelmed. There are different types of load balancing strategies, which include round robin in which each request is distributed sequentially to the available servers. The least connections in which the requests are routed to server with fewer active connections and weighted load balancing in which the servers are assigned different weights based on their capacity directing more traffic to high performing instances. The popular load balancers include AWS Elastic Load Balancer for the cloud environments, Nginx for premises deployments and Cloudflare for global traffic distribution. The load balancing enhances the fault tolerance as failed instances can be automatically removed from the pool while new instances take over seamlessly. Even with load balancing, a static infrastructure is insufficient for handling spikes in demand. The auto scaling allows systems to dynamically adjust computing resources based on real time traffic patterns. The auto scaling groups monitor key performance metrics such as CPU utilization, memory usage, network traffic, and request per second. When the threshold are exceeded, additional instances are spun up automatically. On the other hand, during low traffic periods, excess resources are deallocated to optimize cost. For example, an online streaming service like Netflix experiences peak loads in the evening. Using AWS Auto Scaling, they can deploy more instances in real time and scale down overnight when the demand decreases. Autoscaling is most commonly used with cloud native services like AWS Autoscaling Groups and Google Cloud Instance Group. By integrating autoscaling, businesses reduce operational costs while ensuring high availability and optimal performance. Observability ensures system reliability by providing deep insights into application performance and behavior through logs, matrices, and traces. The tools like ELK Stack, AWS Cloud Watch help us to capture and analyze logs and system health in real time and thereby reducing the troubleshooting time. The distributed tracing with open telemetry allows developers to track requests across microservices. pinpointing bottlenecks efficiently. The smart alerting systems like PagerDuty can notify teams about anomalies before they escalate into critical failures. By integrating observability into your architecture, you can proactively detect, diagnose, and resolve issues, minimizing downtime and enhancing user experience. Avoiding common pitfalls in software design ensures maintainability and long term success. Overengineering leads to unnecessary complexity, making systems harder to debug, scale, and maintain. Therefore, simplicity should be always prioritized. ignoring legacy systems can create integration roadblocks. Instead, adopt a gradual migration strategy or, wrap them with APIs to extend their usability. lack of documentation causes confusion and slows down development. So clear up to date documentation ensures smooth onboarding and knowledge transfer. By focusing on practicality. seamless integration and thorough documentation. Teams can build efficient and sustainable systems. Scalability starts with choosing the right database for your workload. NoSQL databases like MongoDB and Cassandra excel at handling large scale distributed data with high write output. The relational databases such as PostgreSQL, now enhanced with JSON support, Provide a structured query while allowing semi structured flexibility, making them a hybrid solution for evolving applications. Blindly adopting a database trend can lead to inefficiencies, so it's crucial to evaluate data consistency, query patterns, and scaling needs before making a choice. A well planned database strategy ensures optimal performance, maintainability, and cost effectiveness as your system grows. Building adaptable systems ensures long term stability and smooth evolution as requirements change. Feature flags enable controlled rollouts and instant rollbacks, allowing teams to test new functionality without disrupting users. API versioning and continuous refactoring prevent breaking changes and keep the codebase maintainable, reducing technical depth and ensuring seamless integration with future enhancements. Invisible infrastructure means a system that runs smoothly without drawing attention, allowing both users and developers to focus on functionality rather than maintenance. Modularity ensures that components are loosely coupled and easily replaceable, making scaling and updates seamless. Automation eliminates manual interventions, enabling self healing systems, efficient deployments, and reduced operational overhead. By prioritizing simplicity, teams can build resilient, scalable architectures that enhance agility and innovation while minimizing complexity. At its core, technology exists to drive business value, not just to implement the latest trends. The true measure of success lies in building resilient, scalable systems that support long term growth while minimizing maintenance overhead. By focusing on stability, adaptability, and efficiency, we ensure that architecture remains an enabler for innovation rather than a source of complexity. A well designed system doesn't just keep up with the change, it also anticipates it, allowing the businesses to evolve with confidence. Thank you all for your time and attention today. I hope you found valuable insights that you can apply to your own backend architectures. And if you'd like to continue the conversation, feel free to connect with me on LinkedIn. Let's keep learning and building great systems together.