DevOps has fundamentally changed how software is built, tested, and deployed. It has also placed new and often underappreciated demands on storage infrastructure. If storage cannot keep pace with continuous delivery pipelines, it becomes a bottleneck that undermines the very agility DevOps is designed to create.
What Is DevOps, and Why Does It Matter for Storage?
DevOps sits at the intersection of software development and systems engineering. Engineers in this discipline write code that orchestrates deployment, build pipelines, configuration management, and infrastructure provisioning. If you imagine a Venn diagram with developers on one side and operations engineers on the other, DevOps occupies the overlapping space in the middle.
The term itself dates to 2009, when Patrick Debois, a Belgian IT consultant, coined it. Since then, DevOps has evolved from a job title into a broader philosophy shaped by lean and agile thinking. It is as much a cultural shift as a technical one: DevOps flattens hierarchies, decentralises decision-making, and accelerates release cycles from monthly cadences to daily or even hourly deployments.
That acceleration is precisely why storage deserves serious attention in any DevOps conversation. Slow, manual, or rigidly governed storage workflows conflict directly with the speed and autonomy that DevOps teams depend on.
Automation: The Core of Modern DevOps Workflows
Automation is central to what DevOps engineers do. Modern delivery pipelines involve many discrete steps—compiling code, running tests, packaging artefacts, provisioning environments, and deploying to production—all of which can be automated to reduce human error and increase repeatability.
Common automation tools in DevOps environments include:
- Chef: Uses Ruby-based scripts called “recipes” to provision and configure cloud and on-premises infrastructure.
- Puppet: Focuses on day-to-day configuration management and maintenance of servers at scale.
- Ansible: An agentless automation tool commonly used for compute provisioning and application deployment.
- Terraform: A widely adopted infrastructure-as-code tool for provisioning resources across multiple cloud providers.
These tools share a common trait: they allow infrastructure—including storage—to be defined in code, version-controlled, and applied consistently across environments. Any storage system that cannot be managed programmatically through APIs or configuration management tooling will struggle to fit into these workflows.
What DevOps Teams Actually Need from Storage
Speed
Continuous integration and continuous delivery (CI/CD) pipelines move large amounts of data frequently. Docker container images, virtual machine snapshots, build artefacts, and test databases can each measure in the gigabytes. Slow storage directly extends pipeline duration, reducing deployment frequency and defeating the purpose of CI/CD.
Flash-based storage—particularly NVMe—addresses this by delivering the low-latency, high-throughput characteristics that fast pipelines require. Vendors such as Pure Storage have positioned NVMe-based arrays specifically for DevOps workloads, citing pre-built integrations with Docker and Kubernetes.
Automated Provisioning
DevOps engineers should not need to raise a ticket or escalate to a storage administrator every time an application needs additional capacity. Storage that supports self-service provisioning—either natively or through a controller that integrates with orchestration platforms—removes this friction.
Container platforms such as Kubernetes formalise this requirement through the Container Storage Interface (CSI), which allows storage systems to provision and attach volumes dynamically in response to workload demands. NetApp’s open-source Trident project is one example of a CSI driver that automates storage provisioning for Kubernetes workloads.
Efficiency and Copy Data Management
DevOps environments frequently spawn multiple copies of datasets—for development, testing, staging, and production. Without a disciplined approach to managing these copies, storage consumption grows rapidly and becomes difficult to govern.
Copy data management (CDM) capabilities—such as thin cloning, deduplication, and snapshot-based data sharing—allow teams to create lightweight, space-efficient copies of datasets without duplicating the underlying data. This is especially valuable when provisioning ephemeral test environments that need realistic data but should not persist for long.
API-First Integration
DevOps engineers are tool builders. They rely on APIs to compose storage operations into automated workflows, whether that means triggering a snapshot before a deployment, mounting a volume as part of a container startup, or cleaning up test data after a pipeline run.
A storage platform without a robust REST API is essentially incompatible with a modern DevOps workflow. API coverage should extend to provisioning, snapshot management, replication, and monitoring at a minimum.
The Cultural Dimension: Decentralising Storage Decisions
The DevOps philosophy places a high value on team autonomy. Engineers working in agile delivery teams are expected to make rapid decisions about the tools and infrastructure they use. Requiring every storage change to pass through a centralised approval chain introduces exactly the kind of organisational inertia that DevOps exists to eliminate.
This does not mean storage governance disappears—it means governance must be implemented through policy and automation rather than manual gatekeeping. Role-based access controls, capacity quotas, and automated compliance checks can enforce guardrails without requiring human approval for routine operations.
For storage professionals, this is an opportunity rather than a threat. By embedding storage expertise into DevOps conversations and helping teams design self-service workflows, storage engineers can become strategic contributors to delivery speed rather than perceived obstacles to it.
Cloud Storage in DevOps Pipelines
Cloud object storage has become a common component in DevOps pipelines, used for storing build artefacts, container image layers, log archives, and backup snapshots. The appeal is straightforward: object storage scales without provisioning, is accessible over HTTP, and is priced on consumption.
Vendors targeting DevOps teams with cloud storage products—such as Wasabi with its Hot Cloud Storage offering—typically emphasise API compatibility (most commonly with the Amazon S3 API), integration with CI/CD tooling, and predictable pricing. S3-compatible APIs are particularly valuable because a large ecosystem of DevOps tools already knows how to interact with them.
Key Takeaways for Storage and Infrastructure Teams
- Treat storage as code. Storage provisioning and configuration should be scriptable, version-controlled, and repeatable. If it cannot be automated, it will become a bottleneck.
- Prioritise API coverage. Evaluate storage platforms on the completeness and usability of their APIs, not just raw performance specifications.
- Support CSI for Kubernetes workloads. If your organisation runs containerised workloads, ensure your storage backend has a reliable CSI driver that supports dynamic provisioning.
- Implement CDM to control sprawl. Thin cloning and snapshot-based workflows reduce storage consumption without sacrificing the data fidelity that development and testing require.
- Embed storage expertise in DevOps teams. Storage professionals who engage with delivery teams directly—rather than operating as a separate approval function—will deliver more value and face less friction.
- Plan for speed. CI/CD pipelines are sensitive to I/O latency. Understand where storage sits in your pipeline’s critical path and size accordingly.
Storage is not an afterthought in DevOps—it is a foundational layer that can either accelerate or constrain every other part of the delivery pipeline. The organisations that recognise this and invest in storage strategies aligned with DevOps principles will have a meaningful advantage in delivery speed, reliability, and team autonomy.
Original source referenced: computerweekly.com (2019).