Platform Engineering: Where Software-Defined Storage Fits

Platform engineering has evolved significantly from its early focus on manual on-premises systems management into a complex ecosystem of cloud infrastructure, microservices and automated data pipelines. The role has matured into a strategic imperative, particularly within fast-moving, competitive markets like financial services and e-commerce, where rapid innovation and delivery of high-quality applications and services are the bedrock of success. The fundamental imperative is the abstraction of complexity. Developers, the architects of innovation, should not be burdened with managing the underlying data infrastructure. Platform engineering can step into this role by building a self-service ecosystem that empowers developers to provision resources, deploy code and monitor application performance without needing deep expertise into the intricacies of the data infrastructure itself. Today, this means building automated, self-service, dynamically scalable and reliable data platforms. Software-defined infrastructure, generally, and software-defined storage (SDS) specifically, play critical roles in enabling a self-service ecosystem by abstracting away the complexities of underlying infrastructure and presenting compute, storage and networking as consumable resources. A shift toward SDS is not merely for convenience; it is about unlocking agility and accelerating innovation. The Impact on Efficiency The evolution of the platform engineer’s role is a testament to the relentless pursuit of efficiency. In its nascency, the focus was on racking servers, configuring networks and manually deploying applications. It was an early period characterized by long lead times, high operational overhead and significant barriers to rapid experimentation. The advent of virtualization marked a turning point, introducing a layer of infrastructure abstraction allowing for more efficient resource utilization and faster provisioning. However, managing virtual machines (VMs) often remains a manual process. The true inflection point arrived with the rise of cloud models. Cloud services, or Infrastructure as a Service (IaaS), provide on-demand access to data platform resources, liberating organizations from the capital expenditure and operational burden of managing infrastructure. However, simply shifting infrastructure to the cloud did not inherently solve the complexity challenge. Developers had to navigate cloud providers, configure services and manage deployments. This is the period where the role of platform engineering truly transformed. Building Consistency and Agility Beyond efficiency, modern platform engineering goes far beyond simply managing infrastructure resources and promotes organizational consistency and agility across development teams. By aligning workflows and providing a data platform, including standardized solutions for container persistent storage, platform engineers act as a force multiplier, enabling developers to self-serve their infrastructure needs, automate deployment pipelines and gain observability into their applications. This agility is crucial for reducing developers’ cognitive load without compromising innovation speed. Next-Gen Platforms The value of platform engineering in terms of efficiency, accelerated data pipelines and agility is amplified when next-gen technologies such as serverless Kubernetes and SDS are employed. SDS is a cornerstone of a self-service ecosystem, allowing developers to provision persistent storage volumes through intuitive interfaces or Infrastructure as Code (IaC) tools, without needing intricate knowledge of RAID levels, LUN management or specific hardware configurations. Furthermore, SDS seamlessly integrates with common orchestration platforms like Kubernetes, automating storage provisioning as part of the application deployment pipeline. Serverless Kubernetes provides an ideal environment for deploying and scaling applications that run without manual provisioning and scale dynamically. Platform engineers play a vital role in integrating these technologies to empower developers to obtain containerized resources when they need them and scale them according to application demands, all without manual IT intervention. Implementing Platform Engineering for Success Within competitive markets like financial services and e-commerce, the benefits of a mature platform engineering practice are particularly pronounced. In financial services, speed and reliability are paramount. For instance, a financial institution with a well-designed internal developer platform (IDP) can empower its developers to quickly spin up secure and compliant environments for testing new trading algorithms or deploying innovative customer-facing applications, giving them a crucial edge in a fast-paced market. Similarly, in the dynamic world of e-commerce, the ability to handle fluctuating traffic during peak seasons, rapidly deploy new features to enhance the customer experience and quickly iterate on product offerings is essential for staying ahead. Imagine an e-commerce platform seamlessly scaling its infrastructure to handle a sudden surge in Black Friday traffic. This level of responsiveness is a direct result of a well-engineered platform. To successfully introduce platform engineering, organizations must first identify recurring pain points. For example, are developers spending too much time configuring storage for containerized applications? Addressing these challenges is key to building an effective platform. Standardization is paramount. This includes unified workflow practices and standardized resource management approaches. By starting with small, focused initiatives, organizations can gradually expand their platform engineering capabilities and demonstrate value. Final Thought Platform engineering has emerged as a critical discipline for organizations seeking to accelerate their development pipelines. Platform engineers are tasked with creating the underlying infrastructure and tools that enable developers to access, process and analyze their data efficiently. This includes building automated data pipelines, provisioning scalable data storage and orchestration solutions, and ensuring the reliability and availability of data services. Particularly in sectors such as financial services and e-commerce, it has become a strategic function that directly affects an organization’s ability to innovate and deliver value. For a financial services company, platform engineers would be responsible for setting up automated data ingestion pipelines, providing access to powerful computing resources and ensuring the security and governance of sensitive financial data. Similarly, an online retailer that uses data to personalize product recommendations relies on a robust, scalable data platform to analyze consumer behavior in real time. Platform engineering is the invisible force that makes these data-driven capabilities a reality. Emerging trends such as serverless Kubernetes and software-defined IaC are shaping the future of the discipline. Serverless Kubernetes further abstracts away infrastructure concerns by allowing developers to manage distributed applications without managing servers. IaC practices enable automated, version-controlled infrastructure provisioning. Platform engineers are at the forefront of adopting and integrating these technologies to build even more efficient, scalable and reliable data platforms. If you’re a platform engineer and find yourself eager to learn about next-gen software-defined technology to enable accelerated development pipelines, read this comprehensive guide to enterprise software-defined storage technology. The post Platform Engineering: Where Software-Defined Storage Fits appeared first on The New Stack.