The article discusses the challenges and benefits of running data on Kubernetes. Here are the main points:
Challenges:
- Complexity: Running data on Kubernetes can be complex due to its distributed nature and the need for specialized tools and configurations.
- Operator extensibility: While operators provide a way to extend Kubernetes functionality, their lack of standards and different implementation approaches can make it difficult to choose and customize them.
- StatefulSets limitations: The StatefulSets feature in Kubernetes has limitations, such as not being able to resize PVCs (Persistent Volumes), which can affect the performance of stateful workloads.
Benefits:
- Revenue impact: 54% of organizations surveyed attributed more than 10% of their revenue to running data on Kubernetes.
- Productivity and cost savings: 33% of organizations saw a transformative impact on productivity, while 51% experienced a significant positive impact in terms of cost savings.
- Data sovereignty: With increasing regulations around data privacy and consumer rights, companies must host user data in the countries they operate in, making Kubernetes a necessary tool.
Best practices:
- Choose an operator that matches your needs: Select an operator that meets your requirements, considering factors such as scalability, reliability, and customization options.
- Understand the operator’s implementation: Familiarize yourself with the operator’s design, trade-offs, and potential limitations to ensure it aligns with your project’s goals.
- Contribute to open-source projects: If you can’t find an operator that meets your needs, consider contributing to existing open-source projects or extending their capabilities.
Overall, while running data on Kubernetes presents challenges, the benefits and flexibility it offers make it a worthwhile investment for organizations looking to optimize their infrastructure performance and comply with data sovereignty regulations.
