Software development and delivery cycles have quickly ramped up both in speed and complexity. Satisfying users and staying competitive means organizations need to deliver efficiently, at scale, and with a high degree of consistency and security. But as organizations continue to grow and their digital offerings become more popular, they face challenges such as redundant development efforts, scalability issues, inconsistent experiences, increased risk, and difficulty in innovating quickly. Platform engineering has emerged to provide a solution to these challenges. Platform engineering creates a standardized set of tools and components that enable organizations to develop and deliver applications and services efficiently — all from one central platform.
What is platform engineering?
Platform engineering is a discipline that combines designing, building, and maintaining environments and infrastructure to support development and delivery of digital products and services. It offers a set of tools, services, and ready-made components that offer a foundation for building applications and services. Platform engineering involves creating and managing the underlying infrastructure and software components that enable continuous development and delivery with minimal friction. In short, it’s an internal development platform.
What is a platform engineer?
The role of a platform engineer is to create, test, and deploy the platform components, such as databases, application servers, and APIs, so that developers can build fast and effectively. This places platform engineers where the needs of developers and the business meet. They also have to stay current with the latest trends and tools in DevOps at large. Platform engineering also involves ensuring that the platform serving all the resources is reliable, scalable and secure. This includes monitoring platform performance, making adjustments, managing resources, and implementing security measures to keep it all safe.
How does platform engineering work?
Platform engineering is becoming critical to DevOps, as organizations look to continue optimizing how fast, reliably, and scalebly they develop and deliver applications and services. It is essentially a product, the capabilities of which depend on the needs of its end users. These end users can range from developers to data scientists to consumers. The goal is to provide a frictionless, self-service experience that offers the right capabilities so users can get their work done with as little overhead as possible.
What does this look like in practice?
- Platform requirements: The first step is to define the platform requirements. Platform engineers need to understand the needs of the business, developers, and target users. The details should be focused on scalability, reliability, security, and user experience.
- Architecture design: Once the requirements are ironed out, the next step is to design the architecture. It should be modular and scalable, with a set of standard components that can be easily replicated, reused, and expanded as needed. The architecture should also support automation for speed and reliability.
- Platform components: After design comes developing the platform’s components. This includes databases, application servers, APIs, and other tools and services that provide the foundational building blocks. In this process, taking from DevOps practices like continuous integration and development (CI/CD) is key.
- Test and deploy: Testing and deployment are an important step of the process. Automated testing tools and processes can be of help for security and reliability. Additionally, automated deployment processes help with speed and reliability.
- Monitor and manage: Once the platform is deployed, it must be monitored and maintained. Platform engineers should use monitoring tools to track performance metrics like response time and error rates. Observability and alerting tools can also help for fast action.
What are the benefits of platform engineering?
There are a range of benefits for organizations that use platform engineering to develop and deliver products and services. Consider that instead of having everyone operating everything and requiring they understand the entire toolchain, platform engineering binds everything into a consistent self-service experience. The results speak for themselves.
Platform engineering enables organizations to develop and deliver applications and services efficiently with the help of a standardized set of tools and components. It reduces redundant development efforts and eliminates the need for developers to constantly reinvent the wheel every time they build a new app. Platform engineering also improves scalability through its modular architecture which can be easily replicated and expanded. There is also the aspect of consistency. With platform engineering, all applications developed provide a consistent experience in terms of functionality, performance, and user interface — reducing friction and the overall learning curve. All of this comes together to offer better resource management and faster innovation. Overtime, organizations can tap into how resources have been used to optimize and reduce cost. And with little overhead and automated processes, platform engineering let’s developers innovate faster than ever.
What are the challenges of platform engineering?
Like DevOps, platform engineering requires both an organizational and mindset change when it comes to development. Challenges are going to present themselves as more and more organizations adopt this new paradigm in software development. Here are common challenges to expect.
Technical complexity: Platform engineering requires specialized skills and knowledge. Building a cloud-based platform that is dynamically scalable and reliable requires expertise across infrastructure, automation, networking, security, and development.
Integration troubles: Integrating disparate and disjointed systems and components is no easy feat, especially when they are developed and hosted by different teams and vendors. This requires communication and knowledge across various areas and groups.
Cultural shift: Platform engineering as a practice requires a culture shift, particularly in organizations that are used to working in silos. It’s a reiteration of the DevOps culture shift, but one focused on turning tools and resources into products for DevOps teams to use with ease.
Legacy systems: Incompatible and outdated systems are still rife across organizations. This can be an issue when integrating them into new platform engineering practices. Think about the effort required to migrate a legacy CRM system into a modern cloud-based platform for instance.
Cost and resource constraints: Building and maintaining can be costly both in time and money, especially for small to mid-sized organizations. Building a platform that can scale to from hundreds to thousands of users requires significant investment in infrastructure and specialized talent.
What is the future of platform engineering and DevOps?
The future of platform engineering is likely to be characterized by continued innovation and evolution, driven by emerging technologies and changing business needs. Successful platform engineers will need to stay up-to-date with these developments and be able to adapt quickly to new trends and requirements. Here are a few things to keep an eye on.
Edge computing: There is a continued adoption of edge computing, which involves processing data closer to the source instead of centralized data centers. Platform engineering teams will need to design and deploy new architectures and systems that can handle edge computing workloads.
Low-code, no-code platforms: Low-code and no-code platforms that enable non-technical users to build and deploy applications will likely become more popular. This will require platform engineers to integrate these into their architectures and maintain them as much as other more granular toolsets and resources.
More cloud-native architectures: Cloud-native architectures that leverage cloud services and containerization are becoming standard. Platform engineers will need to deepen their understanding of container orchestration tools like Kubernetes and adopt new practices like serverless computing.
Adoption of AI and machine learning: Generative AI is redefining how we think about smart computing. Platform engineers will have to develop new architectures and tools that can support AI and ML workloads — not to mention integrating these technologies into existing platforms for enhanced performance, efficiency, and accuracy.
