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The Rise of Kubernetes: Reshaping the Future of Application Development

Kubernetes has become essential for modern app development. Learn how it's evolving to support AI/ML workloads and changing the developer landscape.

Kubernetes has emerged as the de facto standard for container orchestration, revolutionizing how developers build, deploy, and manage applications. A recent report by Pure Storage's Portworx division reveals that 80% of respondents plan to develop most of their new applications on cloud-native platforms in the next five years. This shift is not just a trend; it's a fundamental change in how we approach software development and infrastructure management.

The Kubernetes Advantage for Developers

Kalyan Ramanathan, VP of Marketing at Portworx, emphasizes that Kubernetes is built with developers in mind. It offers three key advantages:

  1. Faster time to market: Kubernetes streamlines the development and deployment process, allowing teams to iterate and release applications more quickly.

  2. Flexibility in deployment: Applications can run on-premises, in public clouds like AWS or GCP, or in hybrid environments.

  3. Self-service capabilities: Developers can declare their infrastructure needs, and Kubernetes automatically provisions and manages the required resources.

These benefits are driving the rapid adoption of Kubernetes across industries. Ramanathan says, "If you're a CIO today and build an application on anything other than Kubernetes, you will be shot."

The Transition From VMs to Kubernetes

With 58% of organizations planning to migrate some VM workloads to Kubernetes, developers and architects face new challenges. Ramanathan offers several insights on managing this transition:

  1. Mind the skill gap: The personas managing VMs and Kubernetes are different. VM administrators focus on infrastructure, while Kubernetes requires more application-centric skills.

  2. Technology maturity: While VM technologies like VMware are mature, Kubernetes-based solutions for running VMs (like KubeVirt) are still evolving.

  3. Start small: Begin with tier-two and tier-three applications rather than mission-critical workloads. This approach allows teams to gain experience and refine their processes.

  4. Experience matters: Organizations with more Kubernetes experience are better positioned to handle the migration from VMs.

Supporting Data-Intensive Applications

As Kubernetes' adoption grows, so does its use for data-intensive workloads like AI and machine learning. The survey indicates that 54% of respondents already run AI/ML workloads on Kubernetes. However, Kubernetes was initially designed for stateless applications, presenting challenges for data management.

Ramanathan explains how Portworx addresses this issue: "We provide that persistent layer backing a Kubernetes platform to whatever the storage systems on the back are. We ensure your data is always available to your containers and pods wherever they are running."

The industry is also evolving to support data-intensive applications better. The Container Storage Interface (CSI) is an open-source standard that allows storage vendors to integrate with Kubernetes. As CSI matures, we expect more robust data management capabilities for Kubernetes-based applications.

The Rise of Platform Engineering

The adoption of Kubernetes is giving rise to a new role: the platform engineer. These professionals bridge the gap between traditional infrastructure teams and application developers. Ramanathan shared an example of a customer where just three platform engineers support 400 developers and data scientists.

This trend will likely continue, with platform engineering teams becoming crucial for Kubernetes adoption. These teams focus on providing self-service capabilities to developers, allowing them to focus on writing code rather than managing infrastructure.

Unifying VM and Container Management

As organizations run both VMs and containers, there's a growing desire for unified platforms to manage both environments. This convergence benefits developers in several ways:

  1. Simplified troubleshooting: Developers can use a single system to diagnose and fix issues across VM and container-based applications.

  2. Reduced cognitive load: With fewer systems to learn and manage, developers can focus more on building applications.

  3. Increased efficiency: A unified platform supports the overall efficiency of the development process.

Deploying Across Diverse Environments

With 86% of respondents running cloud-native applications across public and private cloud environments, portability is critical. Ramanathan's advice for developers working on applications that need to be deployable across diverse environments is clear: "Build on Kubernetes. There is no other choice."

Kubernetes provides the abstraction layer needed to run applications consistently across different environments. However, data portability remains a challenge, which is where solutions like Portworx come in to ensure data follows compute resources.

The Self-Service Revolution

One of the most significant changes Kubernetes brings is the shift towards self-service for developers. Ramanathan uses the example of database provisioning to illustrate this point:

"In the past, if I had to get a database, I would go to my DBA and give them a ticket, and God forbid if they're on vacation, I get it when they return. Now, developers can do that themselves. That's the beauty of Kubernetes."

This self-service capability extends to storage, backups, and other infrastructure needs, dramatically reducing wait times and increasing developer productivity.

AI and Kubernetes: A Perfect Match

Looking to the future, Ramanathan sees artificial intelligence as the next major paradigm shift in cloud-native development. Importantly, he notes that "AI, containers, and Kubernetes go together."

This synergy is evident in several ways:

  1. AI models as containers: Many AI frameworks and models are distributed as containers, making Kubernetes a natural fit for deployment.

  2. Resource optimization: Kubernetes' ability to efficiently manage compute resources is crucial for resource-intensive AI workloads.

  3. Scalability: The elastic nature of Kubernetes clusters aligns well with the variable resource demands of AI applications.

Ramanathan emphasizes this: "If you want to build AI applications, the only packaging I have today are containers."

Conclusion: The Kubernetes Imperative

As we look to the future of application development, one thing is clear: Kubernetes is no longer optional. It has become the foundation for modern, cloud-native applications, supporting everything from traditional web services to cutting-edge AI workloads.

For developers, engineers, and architects, this means:

  1. Investing in Kubernetes skills is crucial for career growth.

  2. Embracing a more declarative, infrastructure-as-code approach to application deployment.

  3. Leveraging self-service capabilities to increase productivity and reduce dependency on operations teams.

  4. Thinking in terms of microservices and containerized applications, even for legacy workloads.

  5. We are preparing for a future where AI and machine learning are integral to many applications built on Kubernetes foundations.

Ramanathan succinctly says, "If you're not container-ready, you cannot do AI." In today's rapidly evolving tech landscape, being container-ready means being Kubernetes-ready. The journey may be challenging, but the benefits of developer productivity, application portability, and future readiness are undeniable.


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