The modern influx of data requires agile technologies. Cloud-native applications take advantage of the faster internet and the ability to scale in or out instantly. Developers can build and deploy applications in small components, allowing teams to focus on specific functionality without worrying about infrastructure. This improves productivity significantly.
Adaptability is an essential quality that drives innovation in organizations. To innovate, teams must quickly learn from mistakes and move past roadblocks. Adaptability allows teams to respond to customer needs, deliver features faster, and stay ahead of the competition. Unlike monolithic applications, cloud native architecture breaks down application functions into microservices, making them easier to port or rearchitect to the cloud. This provides scalability, flexibility, and resilience to applications. Cloud-native solutions offer a pay-per-use model and open-source tools that reduce costs significantly as CapEx becomes OpEx. It also enables developers to scale resources and maintain their apps at optimal performance based on usage without overprovisioning infrastructure, thereby saving time and money.
Additionally, with automated CI/CD pipelines and DevOps processes, teams can rapidly deploy, test, and gather feedback seamlessly, accelerating the Software Development Life Cycle (SDLC). This enables organizations to deliver more features faster to customers for business growth and competitive advantage. They can even introduce improvements in real time to optimize the app further. This is possible only with a flexible and scalable platform.
Scalability is the ability for systems to operate at the optimum size for current or expected contexts without requiring sudden refactoring or rearchitecting activity that can introduce new risks and complexity. It’s a key factor for business operations and IT systems architects to consider when developing technology platforms for their organizations.
Cloud-native architecture provides many scalability benefits to businesses. Its modularity allows developers to build and deploy software components in parallel, significantly reducing development and testing times. It’s based on microservices and containerization that enable continuous delivery and DevOps practices, resulting in faster time to market for customer-centric applications. It’s also stateless, allowing developers to scale quickly, cache more efficiently for performance gains, and avoid vendor lock-in by not being attached to a single server. In addition, it leverages distributed data services that allow for more horizontal scaling patterns. These scalability advantages result in a more resilient application that can withstand infrastructure outages or other disruptions and remain online even when a component fails. This is known as a “blast radius.” A single failure in one microservice can have a minimal impact on the rest of the system.
Businesses seek to digitalize their processes and provide more affluent customer, partner, and employee experiences. This requires a complex network of applications that work together to provide data and service. This network needs to be resilient and scalable, and it must be able to cope with infrastructure outages or changes in demand. A cloud-native architecture can scale with elasticity by flexing consumption based on the application’s demands.
This approach helps reduce infrastructure expenses by avoiding overprovisioning and reducing application performance degradation. Developers can build, deploy, and manage modern applications quickly and securely using microservices, containerization, and DevOps. This includes modernizing existing apps with a low-code platform that allows them to work directly with containers, microservices, and APIs. Traditional monolithic applications are challenging to change and require lengthy testing cycles. They may run slowly and have unanticipated consequences from minor changes. With cloud-native, teams can change individual components that are isolated from other parts of the application. This allows them to test and release updates with minimal downtime quickly.
Cloud-native applications use a modular approach to design and architecture, which reduces the time needed for development and improves the overall quality of the application. This is achieved through microservices and declarative APIs that support DevOps and continuous delivery. Additionally, cloud-native apps can access ready-to-use infrastructure through a service broker layer that offers different services like data virtualization, workflow engines, and caching services. This helps businesses save on cost, as they do not have to spend money building these infrastructure components from scratch. In addition, cloud-native architecture provides flexibility when handling observability data. For instance, each container in a cloud-native environment emits massive volumes of telemetry data, as much as ten times more than a traditional VM. Cloud-native technologies are designed to handle this data volume using a distributed tracing system that manages the observability data and helps teams find errors faster. They also help companies avoid losing valuable information by identifying the lineage of each piece of data. This way, developers can focus on improving customer experiences without sifting through mountains of data to find the source of an error.
The time it takes for a feature to be implemented, deployed, and rolled out to all users is drastically shorter in the cloud-native world. This makes it easier to take a more data-driven approach to dev. For example, suppose Tara sees that 50% of customers drop out of her e-commerce checkout. In that case, she can easily roll out a new bill-splitting feature to a small percentage of her user base, get irrefutable feedback, and then scale the feature to everyone. This is much quicker than she would have been able to do in the old world.
Similarly, if one of the microservices fails, it is easy to track down which one and repair it without impacting any other services in the system. This starkly contrasts with non-cloud native architectures, where troubleshooting often involves a lot of manual work to identify the source of problems. As a result, cloud-native systems are more resilient, reducing the blast radius of failure to a smaller set of components rather than a monolithic application. This increases both performance and efficiency.