Cloud native describes an approach to software development where cloud infrastructure is used to achieve quicker and more scalable deployments. Cloud native applications are designed to take full advantage of modern engineering practices such as automation, managed services, and automatic scaling controls.
The model has implications on your organization’s culture and working practices too. Cloud technologies should become an integral part of how you deliver software. Everyone needs to be aware of the possibilities so they can seek to utilize them in their work. This lets you operate in a more agile manner than rival companies which use the cloud as a bolt-on facility.
In this article, you’ll learn some of the principles you can follow to efficiently build cloud native applications and maximize your return on investment. Cloud native isn’t just about using cloud services: it’s an holistic approach to software delivery that differentiates your organization from others in the industry.
Decouple Your Services
Decoupling your systems into self-contained microservices is one of the first steps to cloud native adoption. Breaking down your architecture into smaller pieces means you can scale them independently of each other. This makes it easier to respond to demand spikes without incurring costs for components that are already performing acceptably.
Services should communicate with each other using well-defined interfaces that encapsulate your application’s data flows. Reducing the coupling between components in this way gives you more flexibility when deciding where they should be hosted. In some situations, you might choose to distribute your application across multiple clouds to consume the most optimal combination of features.
Use Containers as Fundamental Units
The containerization movement underpins most cloud native implementations. Containers are inherently flexible, repeatable, and scalable so they share many of the objectives of cloud native systems.
Containers package your application’s code alongside its dependencies and environmental requirements. They make it possible to run distributed application instances and scale them as your service grows. Adding more capacity is as simple as starting new containers and linking them to your load balancer. This permits rapid ramp-ups when demand grows.
Making containers the fundamental unit in your architecture increases portability and gives you additional deployment options. You can launch services anywhere a container runtime is available, whether in the cloud or on your workstation. Narrowing the gaps between environments is another effective way to improve your operating efficiency.
Automation is essential to most cloud native architectures. Cloud native’s grown up alongside a groundswell of automated management tools and methodologies. Infrastructure as Code, CI/CD pipelines, and alerting solutions deliver a hands-off approach to cloud resources that improves reliability and consistency across systems.
Automating processes has a direct impact on overall efficiency. Engineers can stay focused on building new features instead of having to manually rollout deployments and perform server maintenance tasks.
Unlocking the full power of cloud infrastructure is often dependent on good use of automation. You can automatically scale application components in response to changing resource consumption, ensuring your service remains performant even when demand peaks. Identifying mechanisms you can automate and then implementing tooling around them will streamline your cloud processes and increase throughput.
Be Conscious About State
Cloud native applications are often viewed from a stateless perspective. Stateless apps are easier to deploy and scale because they’ve got no ties to a particular environment. Truly stateless systems are rare in the real world though – most apps will require a database connection or some persistent file storage.
The decoupling process described above can help to identify and compartmentalize stateful components. Consciously planning where state arises enables you to take an intentional approach to its management. Removing state from most components will help you maximize scalability, offering more flexibility in distributing services across clouds.
Although more attention’s now being paid to stateful cloud apps, there are still several potential stumbling points. Protecting stateful data and achieving visibility into which applications can access it is one challenge. It’s also problematic to make persistent data available across multiple cloud environments without opening up security boundaries that could you make vulnerable to attack. Reviewing these problems early in development reduces the risk of roadblocks when you move towards growing your system.
Don’t Forget Security
Cloud platforms aren’t inherently secure. Managed services often come with poor security defaults that could leave you open to attack. Simple misconfigurations can occur too, such as incorrect security settings on object storage buckets that allow sensitive files to leak.
You should take the time to harden your cloud resources as you create them. You can incorporate security adjustments into automated provisioning scripts so you’re sure they’re applied without delay. It’s also important to regularly audit your resources, identify unused ones, and work out who in your organization can interact with each cloud service.
Security impacts efficiency because incidents pull engineers away from new development tasks. For maximum cloud effectiveness you need to be able to utilize resources confidently while possessing a clear picture of the threats they present. This permits you to keep iterating while safeguarding your infrastructure.
Build for Observability
Observability is an essential component of cloud native applications. You need to understand what’s happening in your cloud so you can identify problems and measure the effects of remedial work.
Making a system observable is more involved than simply measuring the fundamental hardware utilization metrics like CPU and memory consumption. An observable application should be able to tell you why individual metrics have reached their reported levels. You need to architect your system to emit logs and traces that can answer these questions for you.
Observability enhances efficiency by providing immediate explanations for problems. You can jump straight to the root cause of an issue without manually interrogating your system. The data emitted by your application should explain how and why errors are occurring, allowing you to focus on implementing mitigations.
Cloud native works best when you adopt agile working practices. Making frequent small changes is more efficient than waiting for a big release. Working iteratively allows value to be delivered to customers sooner and lets you study the effects of individual revisions in isolation. You’ll be able to revert bad deployments more easily when each rollout is dedicated to a single change.
Breaking down tasks into smaller items also helps prevent team members from getting overwhelmed or over-engineering too large a solution. It encourages the continuation of other cloud native principles, such as the decoupling of components into independent sub-systems.
Iterative working creates a cycle of building, observing, and modifying in response to feedback. This provides regular opportunities to understand where you could be making better use of your available cloud resources.
Cloud native applications require conscious work to get right. Maximum efficiency is achieved when you decouple your services, deeply integrate automated tools, and plan for observability and security. These principles permit you to rapidly iterate on new improvements, providing more opportunity to capitalize on the benefits of cloud infrastructure.
An efficient cloud native development model can give you a competitive advantage, allowing you to ship code more quickly with maximum reliability. This means it’s worth taking the time to analyze how you’re currently using cloud resources and where you could increase or optimize your adoption. Migrating from legacy infrastructure takes time but the benefits can quickly offset the one-time cost.
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