Navigating Data Management for Kubernetes, Part 1: Why Storage Is Still Catching Up

Kubernetes has come a long way since its early days of stateless workloads. But as enterprises push it into more complex, data-heavy use cases, storage and data management struggle to keep pace. In Part 1 of this series, we explore how Kubernetes evolved, why storage is lagging behind, and what modern organizations expect from their infrastructure.

Kubernetes matures—but storage lags behind

Early on, most Kubernetes^® workloads didn’t need to worry about storage. Applications were stateless, spinning up and tearing down without any persistent data to maintain. That changed in 2019 with the introduction of the Container Storage Interface (CSI). Now you could provision third-party storage within Kubernetes, which opened the door to stateful workloads. Today, almost every stateful application running in Kubernetes relies on some form of CSI integration.

Yet, while CSI changed the way Kubernetes could be used, it also brought new challenges—most obviously in data management. The technology doesn’t address broad, production-grade requirements facing organizations today—like data replication across clusters, multi-cloud data mobility, and disaster recovery. CSI helps Kubernetes understand where data lives, but not how to manage it over time or what to do when things go wrong.

The reality is that today’s enterprise Kubernetes environments are complex. They span multiple clusters, host a wide range of stateful applications, and increasingly operate across hybrid or multicloud architectures. As a result, storage and data management have become pressing challenges—and organizations expect more features that address the entire data lifecycle.

Evolving expectations for Kubernetes storage

As Kubernetes workloads shift from stateless microservices to stateful, data-intensive applications, the expectations around storage have grown dramatically. It’s no longer enough to simply provision storage volumes. Organizations now demand a Kubernetes storage stack that delivers the same capabilities they relied on in traditional environments—but tailored for the complexity and scale of containerized infrastructure.

Many Kubernetes solutions today still fall short when it comes to supporting multiple applications across multiple clusters. Organizations are increasingly designing for five core pillars that define enterprise-ready storage in a Kubernetes context:

Performance and capacity at scale

Modern production environments demand storage infrastructure that can scale from exabytes and beyond, managing billions of files, objects, and volumes. To support diverse workloads—ranging from low-latency transactional apps to high-throughput analytics pipelines—Kubernetes storage must be built from the ground up with scalability in mind. This means supporting a wide range of storage types and intelligently balancing performance and capacity without manual tuning.

Space efficiency and intelligent data management

Data sprawl is a real challenge. Without efficient storage mechanisms, multiple applications may unknowingly duplicate data, wasting capacity and driving up costs. Organizations need storage platforms with built-in intelligence—particularly for snapshotting and deduplication—that can identify and store only what’s necessary. Compression algorithms must also adapt to different data types: text, video, audio, or otherwise. Efficient data handling isn’t just about cost savings—it’s about operational sustainability at scale.

Security built in from the start

Security can’t be bolted on—it must be inherent to the Kubernetes storage layer. Data must be encrypted both in transit and at rest, and access tightly controlled via role-based access control (RBAC). Organizations require fine-grained control over who can access what data and when. These capabilities help ensure regulatory compliance while also protecting sensitive data from accidental exposure or malicious access.

High availability and crash consistency

When applications span clusters, nodes, and storage volumes, maintaining consistency becomes critical. Organizations need assurance that their data will remain consistent across the stack, even in the face of failures. A crash-consistent architecture means that applications can resume exactly where they left off after a failure—without corruption or loss of data. That level of resiliency is non-negotiable in production.

Operational simplicity

No matter how powerful the technology, it has to be easy to use—or it won’t be adopted. Kubernetes-native storage solutions must offer clear, integrated workflows with built-in health checks, monitoring, and automation. Organizations are looking for simplicity without compromise: powerful functionality delivered through intuitive, production-ready interfaces.

As expectations for Kubernetes storage grow, so do the challenges. In Part 2, we’ll dive into the real-world data management issues organizations face—and why traditional approaches aren’t enough.

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