A data platform is the core of modern data-driven organizations, enabling the integration, management and analysis of data at scale. Whether cloud-based, on premises or hybrid, it centralizes data from various sources (transactional and operational, internal and external) for storage, processing and access by applications, primarily for machine learning, analysis and reporting. Data platforms power everything from financial transactions to social media feeds.
As application and data requirements evolve, they demand low-latency, scalable performance with no downtime. Kubernetes has become the key enabler of these architectures by efficiently orchestrating containers — lightweight, portable packages of software that include everything needed to run an application, from the code and runtime to dependencies. This approach allows real-time operational, transactional and analytic workloads to be deployed and scaled seamlessly across diverse environments.
In this article, I’ll compare running databases inside Kubernetes versus on legacy infrastructure (bare metal or virtual machines). I’ll also highlight how Kubernetes operators — application-specific controllers built to manage and automate stateful applications — make database management on that system a viable, and even superior, option for many organizations.
Legacy Data Platform Management
Deploying and managing databases has traditionally involved a manual, error-prone process, especially when starting from scratch on on-premises hardware or raw cloud instances. Databases are complex systems, often requiring intricate configuration and ongoing maintenance (the so-called “Day 2 operations”). These tasks include scaling, patching, performance optimization, backups and recovery — all of which require careful attention.
In these traditional setups, database management typically means manually provisioning servers, configuring the database, managing storage and handling scaling and fault tolerance by hand. This legacy approach not only wastes time and is vulnerable to human error, but also leads to significant costs in terms of both operational overhead and downtime. These challenges are magnified when running distributed databases (multiple nodes), especially when running multiple clusters for high availability (HA).
However, with the rise of cloud native technologies like Kubernetes, organizations can now manage many of these issues through automation and orchestration. Manual processes can be automated through Kubernetes using a data platform’s Kubernetes operator.
A New Era of Data Platforms Inside Kubernetes
Running data platforms inside your own Kubernetes deployment offers several compelling advantages, particularly for organizations already standardized on the system.
Simplified Deployment and Unified Management
Kubernetes provides a unified platform for managing both application code and infrastructure. Deploying databases on Kubernetes enables you to centralize the management of your entire stack, from the application layer to the database layer. This allows for easier coordination of resources, better observability and automated provisioning and scaling.
Kubernetes helps mitigate the operational complexity of managing databases by abstracting infrastructure. When using Kubernetes, deploying a database involves defining a few high-level YAML files, which can be version-controlled, reviewed and shared. This declarative approach makes managing databases in Kubernetes a GitOps-friendly process that fits well with modern CI/CD workflows.
High Availability and Self-Healing
Kubernetes’ self-healing capabilities ensure that if a node fails, workloads can be rescheduled to healthy nodes. When combined with the inherent fault tolerance of certain database architectures (replication, clustering), Kubernetes can automatically recover from failures with no downtime by electing new primary nodes or re-syncing replicas.
For instance, a data platform such as Aerospike or MySQL, running in Kubernetes, relies on an operator to monitor the health of pods and nodes to trigger automated healing if issues arise. In the event of node failure, an operator, in conjunction with Kubernetes, would reschedule and redistribute workloads to ensure that the database continues to function without requiring manual intervention, increasing availability and reducing downtime.
Kubernetes Operators Enable Automated Self-Management
Organizations seeking more control may opt for self-managed databases running on Kubernetes, either on premises or in the cloud. Kubernetes operators play a key role in this approach. An operator extends Kubernetes with custom resource definitions (CRDs) to automate deployment and Day 2 operations such as scaling, backups and upgrades. Aerospike’s Kubernetes Operator (AKO) exemplifies this capability at scale, managing over 170 clusters for e-commerce marketplace Flipkart and enabling it to handle 95 million transactions per second during the Big Billion Days event.
Operators enable organizations to build their own self-healing, automated data platforms, offering greater flexibility, cost savings and control over data infrastructure. Key capabilities include:
- Automating deployments: Operators deploy and scale databases, maintaining the correct configuration, including security configurations, a common source of manual error.
- Multicloud and hybrid cloud support: Operators deploy and manage database clusters in hybrid and multicloud environments, enabling disaster recovery and HA.
- Managing backups and restores: Operators integrate backup solutions, schedule backups and handle restores.
- Patching and rolling upgrades: Operators update and patch databases without downtime.
- Monitoring and observability: Integration with tools like Prometheus and Grafana enable detailed monitoring, alerts and dashboards for database health and performance.
This approach allows developer teams to focus on building applications, while Kubernetes and operators automate and manage the underlying database infrastructure.
Cloud Portability Is Crucial
Kubernetes provides a key benefit that legacy deployments typically lack: cloud portability. Running databases in Kubernetes allows you to easily migrate between cloud providers or run multicloud environments.
With Kubernetes, you define your infrastructure declaratively, making it possible to move or replicate the same database setup across different environments. This can be particularly beneficial for organizations looking to avoid vendor lock-in or optimize their workloads across multiple clouds for reasons of cost, redundancy or performance.
Control and Flexibility
Another key advantage of running databases inside Kubernetes is the unparalleled flexibility it offers. By managing databases in-house or in your own cloud on Kubernetes, you gain control over essential features such as automated scaling, backup and monitoring — without being tied to the predefined configurations of database as a service (DBaaS) offerings. Kubernetes enables granular control over resource allocation, allowing you to tailor your database deployments to meet specific performance and operational requirements.
In addition, Kubernetes facilitates seamless workload consolidation, maximizing the usage of existing cloud infrastructure. By orchestrating multiple workloads on the same set of nodes, Kubernetes provides a more dynamic and efficient way to manage resources compared to static bare metal or independently managed virtual instances. This flexibility empowers organizations to adapt their database infrastructure as needs evolve, optimizing for both current demands and future scalability.
Reality Check – Does Your Data Platform Need Kubernetes?
While running databases in Kubernetes has many advantages, it’s not the right fit for every situation. Naturally, if your organization lacks Kubernetes expertise, the additional overhead might outweigh the benefits. If you are all in on a legacy or niche data platform, then you may have to wait for the vendor to develop an operator, and then maybe wait some more for it to become useful as an automation tool. While Kubernetes provides self-healing, some database systems often require more advanced configurations and fine-tuning to ensure maximum reliability and performance, which may be harder to achieve in Kubernetes.
If any of these cases apply to your organization, you’ll have to address them prior to embracing Kubernetes for your data platform.
The Future of Data Platforms Is Today
Organizations are increasingly choosing to run databases inside Kubernetes for its flexibility, scalability and automation in database management. Kubernetes operators automate critical Day 2 operations — like backups, monitoring and self-healing — while Kubernetes provides the infrastructure for easy deployment, scaling and fault tolerance. For teams already using Kubernetes as their container orchestration platform, running databases within it offers a unified stack with greater control and flexibility. However, teams must carefully assess the trade-offs to ensure this approach aligns with their operational needs. One thing is clear: The days of managing databases manually on bare metal or virtual machines are coming to an end, as Kubernetes provides a more automated, cost-effective and future-proof solution.
Learn how the Aerospike Kubernetes Operator simplifies and automates the deployment and management of Aerospike databases, whether in the cloud or on premises.
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Kubernetes operators — app-specific controllers that manage stateful applications — can make database management a viable, and even superior, option.