Why Database Modernization Is the Next Frontier for Cloud-Native Adoption

Over the past several years, the path toward cloud-native adoption has followed a familiar pattern: organizations have containerized their applications, assembled Kubernetes platforms, embraced microservices, and automated deployments. Yet hidden within the cloud-native fabric lies an older form of infrastructure that hasn’t changed very much at all: the database.

Given the context, this comes as little surprise. Moving an application is hard enough. Moving the system that manages decades of business-critical data is on another level, especially when business rules, compliance, and many other services depend on that data.

But that disparity is getting harder to ignore. As cloud-native applications gain traction, modernization has turned its focus to the database, with enterprises considering how they manage, operate, scale, and secure their data.

The last piece of the modernization puzzle

The application modernization race has far outpaced the database modernization race. Application developers are familiar with running containers across several clusters, and CI/CD tooling can release new applications several times a day. Database teams, however, usually still face regular maintenance, scaling, and infrastructure concerns.

Part of the issue is that the standard relational database wasn’t built for the cloud-native operating model. The assumption is that servers are stable, resources are predictable, and infrastructure is rigid. In contrast, the operating model for Kubernetes expects workloads to be fluid, fault-tolerant, and auto-scalable.

Developers can build highly portable applications, but if the database is tied to a particular virtual machine or storage, the cloud-native promise doesn’t hold.

Additionally, application modernization is incremental by its nature. In many cases, a database simply can’t follow that route.

Why moving a database isn’t like moving an application

Migrations are typically the result of complex trade-offs in data consistency, replication, transactional integrity, latency, and compliance, with even brief outages carrying serious business implications.

In many organizations, there’s the added complexity of the database ecosystem. Each enterprise database often supports numerous applications, analytics platforms, reporting tools, and other integrations. Moving one component without affecting all the dependencies requires a lot of careful planning.

Some organizations first turned to a lift-and-shift approach for their data, moving their databases to the cloud without fundamentally modifying the database. While this reduces operational overhead in the cloud, it does not necessarily achieve the elasticity, availability, or manageability expected of the cloud.

Cloud-native databases change the operating model

As opposed to simply moving existing databases to the cloud, organizations are increasingly considering databases that have been designed for the cloud.

Cloud-native databases are often designed to support horizontal scaling, automatic failover, continuous replication, and infrastructure abstraction. Instead of assuming the server is the primary component, they assume that nodes fail, workloads shift, and the capacity requirements are dynamic.

That creates an operational shift that is felt every day. Platform teams need to be involved less and less in the manual management of storage and infrastructure, and instead can rely on automation to manage most operational tasks, like replication, failover, backups, and recovery.

The rise of Kubernetes operators has significantly sped up this transition. An operator embeds the operational experience into an application, allowing databases to be deployed, upgraded, and recovered using the same declarative workflows as the rest of the Kubernetes platform.

Modernization isn’t about replacing SQL

Database modernization isn’t about giving up on relational databases; SQL continues to underpin many enterprise applications, and there are relational database platforms specifically developed to work within cloud-native environments. Instead, the shift is about choosing architectures that better match modern workloads.

Some organizations are using distributed SQL databases that provide familiar relational features but deliver higher availability across multiple regions. Other organizations are using purpose-built databases, such as document, graph, time-series, or key-value databases. As opposed to expecting one database platform to solve every problem, architects are increasingly selecting databases based on workload characteristics, performance requirements, and operational needs.

The economics are changing too

As with application modernization, it’s not just about choosing the newest technology; it’s about choosing the right one. The maintenance of databases requires administrator time for capacity planning, software patching, and managing disaster recovery scenarios. In enterprise organizations, where the database estates are ever expanding, this becomes even harder to scale. Cloud-native database platforms automate much of this work with features like elastic scaling to limit overprovisioning and managed services to handle day-to-day operations.

Additionally, the rise of real-time analytics and AI is increasing the need for the database to support constantly evolving workloads and to serve online transaction processing, streaming, and analytical queries at the same time. While this is achievable using traditional databases, it adds a significant amount of operational complexity.

Security and resilience are now core priorities

As ransomware attacks, supply chain compromises, and regulatory scrutiny have risen, resiliency and recoverability have become the main concerns. Enterprise organizations aren’t simply looking to ensure their databases stay online but are instead looking at how quickly they can recover from failures, be available across the globe, and keep sensitive data secure. 

In addition to being able to enforce policy-based security, cloud-native databases provide capabilities like automated backups, cross-region replication, and snapshots that are immutable.

These don’t remove the need for operational risk management, but they make resilience a built-in part of the database. This also simplifies security management. Instead of managing security as a separate silo, organizations are now increasingly able to manage identity policies, encryption, observability, and compliance in the same way as other cloud-native infrastructure.

In conclusion, database modernization can be more than containerization. While the reality is that most organizations have now proven that applications can be modernized, the big question is whether the technology supporting the application can be modernized at the same speed.